six-minute walk test postoperative complications abdominal surgery

cd /tmp/workspace && python3 -c " import pdfplumber pdf_path = '/tmp/workspace/attachments/e9c26152-246e-4e04-b624-3855bc26ee9d/ISRC Annex A Form 1A Clinical Research Proposal_Ong (April 2026 Final) with stamps.pdf' with pdfplumber.open(pdf_path) as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() if text: print(f'\\n=== PAGE {i+1} ===') print(text[:4000]) "

six-minute walk test preoperative functional capacity prediction surgery outcomes

six-minute walk test postoperative pulmonary complications prediction

preoperative functional capacity major abdominal surgery morbidity mortality

PMID: 31111872

PMID: 31402737

PMID: 35253333

PMID: 38526466

PMID: 34629224

PMID: 29049742

PMID: 35579479

PMID: 28738386

mkdir -p /tmp/workspace/6mwt-proposal && ls /tmp/workspace/

cat << 'PYEOF' > /tmp/workspace/6mwt-proposal/build_proposal.py from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.oxml.ns import qn from docx.oxml import OxmlElement import copy doc = Document() # ─── Page margins (match reference paper: normal margins) ─────────────────── for section in doc.sections: section.top_margin = Inches(1.0) section.bottom_margin = Inches(1.0) section.left_margin = Inches(1.25) section.right_margin = Inches(1.0) # ─── Default style ────────────────────────────────────────────────────────── style = doc.styles['Normal'] style.font.name = 'Times New Roman' style.font.size = Pt(12) style.paragraph_format.space_after = Pt(0) style.paragraph_format.space_before = Pt(0) def set_font(run, bold=False, italic=False, size=12, name='Times New Roman'): run.font.name = name run.font.size = Pt(size) run.bold = bold run.italic = italic def add_para(text='', bold=False, italic=False, size=12, align=WD_ALIGN_PARAGRAPH.JUSTIFY, space_before=0, space_after=6, indent_left=0, first_line=0): p = doc.add_paragraph() p.alignment = align p.paragraph_format.space_before = Pt(space_before) p.paragraph_format.space_after = Pt(space_after) p.paragraph_format.left_indent = Inches(indent_left) p.paragraph_format.first_line_indent = Inches(first_line) if text: run = p.add_run(text) set_font(run, bold=bold, italic=italic, size=size) return p def add_section_heading(number, title): """Bold numbered section heading like '2. Brief Description / Summary'""" p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.LEFT p.paragraph_format.space_before = Pt(12) p.paragraph_format.space_after = Pt(6) run = p.add_run(f"{number}. {title}") run.font.name = 'Times New Roman' run.font.size = Pt(12) run.bold = True return p def add_subsection_heading(number, title): """Bold subsection like '3.1. Significance of the Project'""" p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.LEFT p.paragraph_format.space_before = Pt(8) p.paragraph_format.space_after = Pt(4) run = p.add_run(f"{number} {title}") run.font.name = 'Times New Roman' run.font.size = Pt(12) run.bold = True return p # ════════════════════════════════════════════════════════════════════════════ # HEADER / TITLE BLOCK # ════════════════════════════════════════════════════════════════════════════ add_para('DATABANK INFO NEEDED:', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=2) add_para('No Yes', size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=2) add_para('1. R&B Form No. 1A', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=2) add_para('', space_after=4) # Institution header inst_p = doc.add_paragraph() inst_p.alignment = WD_ALIGN_PARAGRAPH.CENTER inst_p.paragraph_format.space_after = Pt(2) r = inst_p.add_run('St. Luke\'s Medical Center') r.font.name = 'Times New Roman'; r.font.size = Pt(12); r.bold = True add_para('RESEARCH AND BIOTECHNOLOGY', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.CENTER, space_after=6) add_para('1.1. CLINICAL RESEARCH PROPOSAL', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.CENTER, space_after=12) # Research Title p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.LEFT p.paragraph_format.space_after = Pt(4) r = p.add_run('Research Title:') r.font.name = 'Times New Roman'; r.font.size = Pt(12); r.bold = True title_p = doc.add_paragraph() title_p.alignment = WD_ALIGN_PARAGRAPH.LEFT title_p.paragraph_format.space_after = Pt(10) r = title_p.add_run( 'Prognostic Value of the Six-Minute Walk Test in Predicting Postoperative Pulmonary ' 'and Major Complications Among Adults Undergoing Major Abdominal Surgery: ' 'A Prospective Cohort Study' ) r.font.name = 'Times New Roman'; r.font.size = Pt(12); r.bold = False # Investigators table add_para('Investigators:', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=2) table = doc.add_table(rows=4, cols=2) table.style = 'Table Grid' table.columns[0].width = Inches(3.5) table.columns[1].width = Inches(2.5) headers = ['Name and Signature', 'Unit/Position'] for i, h in enumerate(headers): cell = table.cell(0, i) cell.text = h for run in cell.paragraphs[0].runs: run.bold = True run.font.size = Pt(11) rows_data = [ ('Project Leader/s:\n\n_______________________________', 'Consultant\n(Consultant/Manager/Faculty)'), ('Co-Project Leader/s:\n\n_______________________________', 'Pulmonary/Surgery Fellow\n(Resident/Fellow/Student)'), ('Research Fellow:\n\n_______________________________', ''), ] for i, (name_col, unit_col) in enumerate(rows_data): table.cell(i+1, 0).text = name_col table.cell(i+1, 1).text = unit_col for col in [0, 1]: for run in table.cell(i+1, col).paragraphs[0].runs: run.font.size = Pt(11) doc.add_paragraph() # spacing add_para('Inst./Dept./Center/Group: Department of Surgery / Anesthesiology', size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=12) # ════════════════════════════════════════════════════════════════════════════ # SECTION 2 – BRIEF DESCRIPTION / SUMMARY # ════════════════════════════════════════════════════════════════════════════ add_section_heading('2', 'Brief Description / Summary') brief_desc = ( 'Major abdominal surgery carries a significant risk of postoperative pulmonary ' 'complications (PPCs) and other major adverse outcomes, which contribute substantially ' 'to perioperative morbidity and mortality. Identifying patients at high risk before surgery ' 'enables targeted preventive strategies, yet current preoperative risk tools are either ' 'resource-intensive or rely on subjective estimates of functional capacity.' ) add_para(brief_desc, size=12, space_after=6) brief_desc2 = ( 'This study investigates the prognostic value of the Six-Minute Walk Test (6MWT), ' 'a simple, low-cost, and widely available field exercise test that objectively quantifies ' 'functional exercise capacity through the distance walked in six minutes (6MWD). The 6MWT ' 'is hypothesized to serve as a composite marker reflecting cardiorespiratory reserve, ' 'physical conditioning, and overall physiologic resilience — factors directly relevant to ' 'the ability to withstand surgical stress.' ) add_para(brief_desc2, size=12, space_after=6) brief_desc3 = ( 'This will be a prospective cohort study at a tertiary hospital involving adult patients ' 'scheduled for elective or semi-elective major abdominal surgery. The 6MWT will be ' 'performed preoperatively, and participants will be followed through the postoperative ' 'period to ascertain the occurrence of PPCs and other major complications. The 6MWT could ' 'offer clinicians a practical, accessible, and inexpensive preoperative risk stratification ' 'tool, enabling timely optimization and potentially improving patient outcomes in both ' 'resource-rich and resource-limited settings.' ) add_para(brief_desc3, size=12, space_after=12) # ════════════════════════════════════════════════════════════════════════════ # SECTION 3 – INTRODUCTION # ════════════════════════════════════════════════════════════════════════════ add_section_heading('3', 'Introduction') # ── 3.1 Significance ──────────────────────────────────────────────────────── add_subsection_heading('3.1.', 'Significance of the Project') sig1 = ( 'Major abdominal surgery encompasses a broad range of high-risk procedures including ' 'colorectal resection, hepatobiliary surgery, gastrectomy, and pancreaticoduodenectomy. ' 'These procedures are associated with postoperative pulmonary complication rates ranging ' 'from 9% to 40%, depending on patient demographics, comorbidities, and surgical complexity ' '[1]. PPCs — encompassing pneumonia, respiratory failure, atelectasis requiring intervention, ' 'pleural effusion, bronchospasm, and aspiration — are among the most common causes of ' 'perioperative morbidity, prolonged hospital stay, and mortality following abdominal surgery [2].' ) add_para(sig1, size=12, space_after=6) sig2 = ( 'Despite their clinical importance, a reliable and practical bedside tool for preoperative ' 'risk prediction of PPCs remains elusive. Current tools such as the ARISCAT (Assess Respiratory ' 'Risk in Surgical Patients in Catalonia) score and ASA Physical Status classification provide ' 'general risk estimates but do not directly capture an individual\'s functional reserve. ' 'Cardiopulmonary exercise testing (CPET) is widely regarded as the gold standard for ' 'objective preoperative functional assessment, but it requires specialized equipment, ' 'trained personnel, and considerable time and cost, limiting its routine use especially ' 'in resource-limited settings [3].' ) add_para(sig2, size=12, space_after=6) sig3 = ( 'The Six-Minute Walk Test (6MWT) offers a compelling alternative: it requires no specialized ' 'equipment beyond a measured corridor, takes less than ten minutes to administer, and yields ' 'an objective, reproducible measure of submaximal exercise tolerance — the six-minute walk ' 'distance (6MWD). The 6MWT is already validated and widely used in cardiopulmonary ' 'rehabilitation and chronic disease management. Its perioperative application, particularly ' 'in abdominal surgery, is an emerging area of study with important clinical and public health ' 'implications.' ) add_para(sig3, size=12, space_after=6) sig4 = ( 'The study aims to:' ) add_para(sig4, size=12, space_after=4) aims = [ ('1. ', 'Determine the prognostic value of preoperative 6MWD in predicting PPCs and major ' 'postoperative complications in adults undergoing major abdominal surgery at a tertiary hospital.'), ('2. ', 'Identify an optimal 6MWD cut-off value that discriminates patients at high versus low ' 'risk for PPCs, enabling bedside clinical risk stratification.'), ('3. ', 'Contribute locally relevant evidence on preoperative functional capacity assessment in ' 'a setting where CPET is not routinely available, supporting cost-effective perioperative ' 'care pathways.'), ] for num, text in aims: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.3) r1 = p.add_run(num) r1.font.name = 'Times New Roman'; r1.font.size = Pt(12); r1.bold = True r2 = p.add_run(text) r2.font.name = 'Times New Roman'; r2.font.size = Pt(12) doc.add_paragraph() # ── 3.2 Rationale ─────────────────────────────────────────────────────────── add_subsection_heading('3.2.', 'Rationale for Doing the Study') rat1 = ( 'Timely and accurate preoperative risk stratification remains a persistent challenge in the ' 'perioperative management of patients undergoing major abdominal surgery. Existing severity ' 'scoring systems and risk indices are often underutilized due to complexity, limited ' 'applicability across different populations, or the absence of objective measures of a ' 'patient\'s functional reserve.' ) add_para(rat1, size=12, space_after=6) rat2 = ( 'Functional capacity — defined as the ability of an individual to perform physical activities ' 'that require aerobic metabolism — is a well-established, independent determinant of ' 'perioperative risk. Rose et al. (2022) reviewed the physiological basis linking ' 'cardiorespiratory fitness (CRF) to postoperative outcomes, demonstrating that impaired CRF ' 'is an independent risk factor for mortality and morbidity. Surgery triggers a period of ' 'substantially increased oxygen demand; patients unable to meet this demand face greater risk ' 'of organ failure and death. The authors emphasized that CRF is the greatest modifiable ' 'perioperative risk factor, and its accurate preoperative detection is essential for risk ' 'classification and patient management [3].' ) add_para(rat2, size=12, space_after=6) rat3 = ( 'The 6MWT provides an objective, standardized measure of submaximal exercise capacity that ' 'is closely correlated with peak oxygen consumption (VO2 peak) and reflects the integrated ' 'response of the cardiorespiratory, neuromuscular, and metabolic systems. Unlike CPET, the ' '6MWT is simple, inexpensive, and reproducible, making it practical for routine preoperative ' 'assessment even in settings with limited resources. Crucially, the 6MWT captures not only ' 'cardiorespiratory fitness but also the patient\'s nutritional status, muscle strength, and ' 'motivational state — all factors that independently influence surgical outcomes.' ) add_para(rat3, size=12, space_after=6) rat4 = ( 'Several studies have examined the relationship between preoperative 6MWD and postoperative ' 'outcomes. Soares and Nucci (2021) conducted a prospective cohort study of 50 patients ' 'undergoing elective abdominal surgery, finding that 25 (50%) developed postoperative ' 'pulmonary complications within the first seven postoperative days. The mean preoperative ' '6MWD was significantly shorter among those who developed PPCs (444.8 m vs. 498.3 m; ' 'p = 0.013). Multivariable logistic regression confirmed that a lower preoperative 6MWD was ' 'significantly and independently associated with PPCs (OR = 0.978; p = 0.010) in patients ' 'undergoing intestinal, gastric, or biliary tract resection [4].' ) add_para(rat4, size=12, space_after=6) rat5 = ( 'Extending this evidence to other abdominal organ surgeries, Magalhaes et al. (2017) ' 'prospectively studied 100 patients undergoing liver transplantation, finding that 44 ' 'developed at least one postoperative respiratory complication. In logistic regression ' 'analysis, each additional 50 meters walked during the preoperative 6MWT was associated ' 'with a 41% reduction in the odds of developing PPCs (OR = 0.589; 95% CI: 0.357–0.971; ' 'p = 0.03), establishing the 6MWT as an independent predictor of postoperative pulmonary ' 'complications in this population [5].' ) add_para(rat5, size=12, space_after=6) rat6 = ( 'In the oncologic setting, Inoue et al. (2020) retrospectively reviewed 111 patients ' 'undergoing thoracic surgery for esophageal cancer and found that a preoperative 6MWD ' 'of ≤454 m was a significant threshold for predicting grade II or higher Clavien-Dindo ' 'complications, with 71.0% sensitivity and 54.8% specificity. In multiple regression ' 'analysis, lower 6MWD was an independent preoperative risk factor for major complications [6]. ' 'Similarly, Hattori et al. (2018) demonstrated in a retrospective analysis of 321 patients ' 'undergoing lung resection for malignancy that a preoperative 6MWD ≤450 m predicted ' 'postoperative pneumonia with 69.2% sensitivity and 71.1% specificity (p = 0.002) [7].' ) add_para(rat6, size=12, space_after=6) rat7 = ( 'At the level of systematic evidence, Makker et al. (2022) performed a systematic review ' 'and meta-analysis of five studies (379 patients) evaluating preoperative 6MWT or five-times ' 'sit-to-stand performance and postoperative outcomes in gastrointestinal and abdominal ' 'cancer surgery. Higher preoperative 6MWT performance (≥400 m) was significantly associated ' 'with lower-grade postoperative complications (OR = 0.38; 95% CI: 0.15–0.95), though the ' 'association with length of stay was not significant. The authors noted the need for ' 'high-quality prospective studies with standardized definitions and broader patient ' 'populations [8].' ) add_para(rat7, size=12, space_after=6) rat8 = ( 'Argillander et al. (2022) conducted a systematic review of preoperative physical ' 'performance tests and their predictive value for postoperative outcomes specifically in ' 'patients aged ≥65 years undergoing major abdominal cancer surgery. Among non-CPET field ' 'tests, the 6MWT and the Incremental Shuttle Walk Test (ISWT) predicted adverse outcomes ' 'in two studies each. The authors concluded that the 6MWT is a feasible alternative to CPET ' 'for estimating aerobic capacity in older surgical patients, but emphasized the need for ' 'prospective studies comparing different physical tests in a standardized manner [9].' ) add_para(rat8, size=12, space_after=6) rat9 = ( 'In a recent study by Garg et al. (2025) evaluating predictive models for PPCs in upper ' 'abdominal surgery, 20.3% of 133 patients developed PPCs. While the study\'s multivariable ' 'analysis highlighted abnormal chest radiograph, blood urea nitrogen, and duration of surgery ' 'as independent predictors, the investigators specifically tested the 6MWT against established ' 'risk scores. Although 6MWD lacked independent predictive power in that particular cohort, ' 'the authors acknowledged that the study was limited by its single-centre retrospective ' 'design and variable timing of 6MWT administration, and called for prospective designs with ' 'standardized protocols to better characterize the 6MWT\'s predictive role [2].' ) add_para(rat9, size=12, space_after=6) rat10 = ( 'The 6MWT uniquely serves as a composite indicator: reduced 6MWD reflects not only limited ' 'cardiorespiratory reserve but also deconditioning, sarcopenia, and poor nutritional ' 'status — all factors shown to independently worsen surgical outcomes. Since the 6MWT ' 'is inexpensive, non-invasive, and requires minimal equipment or personnel training, it is ' 'ideally suited for routine preoperative application in most hospital settings, including ' 'those with limited access to CPET or advanced physiologic testing.' ) add_para(rat10, size=12, space_after=6) rat11 = ( 'Locally, there is a significant evidence gap regarding the use of objective functional ' 'capacity measures in preoperative risk assessment for abdominal surgery. Existing ' 'perioperative practice largely relies on subjective estimates of metabolic equivalents ' '(METs) via patient history rather than objective testing. Given the high burden of ' 'abdominal surgical disease in the Philippines and the challenges of access to advanced ' 'perioperative testing, there is a compelling rationale to evaluate whether the 6MWT — a ' 'simple, low-cost tool — can reliably predict PPCs and major complications in a local ' 'surgical population. Demonstrating its predictive value could establish the 6MWT as a ' 'practical and immediately implementable preoperative risk stratification tool for Filipino ' 'surgical patients and similar resource-constrained settings.' ) add_para(rat11, size=12, space_after=12) # ════════════════════════════════════════════════════════════════════════════ # SECTION 7 – REFERENCES (Vancouver, continuous numbering) # ════════════════════════════════════════════════════════════════════════════ add_section_heading('7', 'References') references = [ ('[1]', 'Smetana GW, Lawrence VA, Cornell JE; American College of Physicians. ' 'Preoperative pulmonary risk stratification for noncardiothoracic surgery: ' 'systematic review for the American College of Physicians. ' 'Ann Intern Med. 2006 Apr 18;144(8):581–95. doi: 10.7326/0003-4819-144-8-200604180-00009.'), ('[2]', 'Garg S, Govindaraj V, Dwivedi DP, Raja K, Theerthar EP. ' 'Postoperative pulmonary complications in patients undergoing upper abdominal surgery: ' 'risk factors and predictive models. ' 'Monaldi Arch Chest Dis. 2025 Mar 31. doi: 10.4081/monaldi.2024.2915. PMID: 38526466.'), ('[3]', 'Rose GA, Davies RG, Appadurai IR, Williams IM, Bashir M, Berg RMG. ' '\'Fit for surgery\': the relationship between cardiorespiratory fitness and postoperative outcomes. ' 'Exp Physiol. 2022 Aug;107(8):780–95. doi: 10.1113/EP090156. PMID: 35579479.'), ('[4]', 'Soares SMTP, Nucci LB. ' 'Association between early pulmonary complications after abdominal surgery and preoperative physical capacity. ' 'Physiother Theory Pract. 2021 Jul;37(7):852–9. doi: 10.1080/09593985.2019.1650404. PMID: 31402737.'), ('[5]', 'Magalhaes CBA, Nogueira IC, Marinho LS, Daher EF, Garcia JHP, Viana CFG. ' 'Exercise capacity impairment can predict postoperative pulmonary complications after liver transplantation. ' 'Respiration. 2017;94(6):538–44. doi: 10.1159/000479008. PMID: 28738386.'), ('[6]', 'Inoue T, Ito S, Kanda M, Niwa Y, Nagaya M, Nishida Y. ' 'Preoperative six-minute walk distance as a predictor of postoperative complication in patients with esophageal cancer. ' 'Dis Esophagus. 2020 Mar 5;33(3):doz050. doi: 10.1093/dote/doz050. PMID: 31111872.'), ('[7]', 'Hattori K, Matsuda T, Takagi Y, Nagaya M, Inoue T, Nishida Y. ' 'Preoperative six-minute walk distance is associated with pneumonia after lung resection. ' 'Interact Cardiovasc Thorac Surg. 2018 Feb 1;26(2):208–13. doi: 10.1093/icvts/ivx310. PMID: 29049742.'), ('[8]', 'Makker PGS, Koh CE, Solomon MJ, Steffens D. ' 'Preoperative functional capacity and postoperative outcomes following abdominal and pelvic cancer surgery: ' 'a systematic review and meta-analysis. ' 'ANZ J Surg. 2022 Jul;92(7-8):1732–40. doi: 10.1111/ans.17577. PMID: 35253333.'), ('[9]', 'Argillander TE, Heil TC, Melis RJF, van Duijvendijk P, Klaase JM, van Munster BC. ' 'Preoperative physical performance as predictor of postoperative outcomes in patients aged 65 and older ' 'scheduled for major abdominal cancer surgery: a systematic review. ' 'Eur J Surg Oncol. 2022 Mar;48(3):575–84. doi: 10.1016/j.ejso.2021.09.019. PMID: 34629224.'), ] for num, text in references: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.4) p.paragraph_format.first_line_indent = Inches(-0.4) r1 = p.add_run(num + ' ') r1.font.name = 'Times New Roman'; r1.font.size = Pt(12); r1.bold = False r2 = p.add_run(text) r2.font.name = 'Times New Roman'; r2.font.size = Pt(12) # ─── Footer note ──────────────────────────────────────────────────────────── doc.add_paragraph() footer_p = doc.add_paragraph() footer_p.alignment = WD_ALIGN_PARAGRAPH.RIGHT footer_p.paragraph_format.space_before = Pt(24) r = footer_p.add_run('Protocol Version 01 dated ________________') r.font.name = 'Times New Roman'; r.font.size = Pt(10) # ─── Save ──────────────────────────────────────────────────────────────────── out_path = '/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Sections.docx' doc.save(out_path) print(f'Saved: {out_path}') PYEOF python3 /tmp/workspace/6mwt-proposal/build_proposal.py

python3 -c " from docx import Document doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Sections.docx') for p in doc.paragraphs: if p.text.strip(): print(f'[{p.style.name}] {p.text[:120]}') "

python3 -c " from docx import Document doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Sections.docx') for i, p in enumerate(doc.paragraphs): if p.text.strip(): print(f'{i}: [{p.style.name}] {p.text[:150]}') "

six-minute walk test perioperative risk assessment abdominal surgery prospective

postoperative pulmonary complications definition incidence abdominal surgery

PMID: 35750401

PMID: 38472053

six-minute walk test standardization ATS guidelines reliability validity

six-minute walk distance reference values adults normative exercise capacity

Clavien-Dindo classification postoperative complications grading surgery outcomes

PMID: 41027267

PMID: 37560171

PMID: 34939921

cat << 'PYEOF' > /tmp/workspace/6mwt-proposal/append_sections.py from docx import Document from docx.shared import Pt, Inches from docx.enum.text import WD_ALIGN_PARAGRAPH # ── Load existing document ──────────────────────────────────────────────────── doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Sections.docx') # ── Remove the old "7. References" section and footer from existing doc ────── # We will re-add References at the very end with updated numbering paragraphs_to_keep = [] removing = False for p in doc.paragraphs: if p.text.strip().startswith('7. References'): removing = True if not removing: paragraphs_to_keep.append(p) # Delete all paragraphs after (and including) "7. References" # python-docx: remove from the XML body body = doc.element.body all_paras = body.findall('.//{http://schemas.openxmlformats.org/wordprocessingml/2006/main}p') found_refs = False for elem in list(body): tag = elem.tag.split('}')[-1] if '}' in elem.tag else elem.tag if tag == 'p': txt = ''.join( n.text or '' for n in elem.iter('{http://schemas.openxmlformats.org/wordprocessingml/2006/main}t') ) if txt.strip().startswith('7. References'): found_refs = True if found_refs: body.remove(elem) # ── Helper functions ────────────────────────────────────────────────────────── def set_font(run, bold=False, italic=False, size=12, name='Times New Roman'): run.font.name = name run.font.size = Pt(size) run.bold = bold run.italic = italic def add_para(text='', bold=False, italic=False, size=12, align=WD_ALIGN_PARAGRAPH.JUSTIFY, space_before=0, space_after=6, indent_left=0, first_line=0): p = doc.add_paragraph() p.alignment = align p.paragraph_format.space_before = Pt(space_before) p.paragraph_format.space_after = Pt(space_after) p.paragraph_format.left_indent = Inches(indent_left) p.paragraph_format.first_line_indent = Inches(first_line) if text: run = p.add_run(text) set_font(run, bold=bold, italic=italic, size=size) return p def add_section_heading(number, title): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.LEFT p.paragraph_format.space_before = Pt(12) p.paragraph_format.space_after = Pt(6) run = p.add_run(f"{number}. {title}") run.font.name = 'Times New Roman'; run.font.size = Pt(12); run.bold = True return p def add_subsection_heading(number, title): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.LEFT p.paragraph_format.space_before = Pt(8) p.paragraph_format.space_after = Pt(4) run = p.add_run(f"{number} {title}") run.font.name = 'Times New Roman'; run.font.size = Pt(12); run.bold = True return p def add_bullet(label, text, indent=0.3): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(indent) r1 = p.add_run(label) r1.font.name = 'Times New Roman'; r1.font.size = Pt(12); r1.bold = True r2 = p.add_run(text) r2.font.name = 'Times New Roman'; r2.font.size = Pt(12) return p def add_sub_bullet(text, indent=0.55): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(3) p.paragraph_format.left_indent = Inches(indent) run = p.add_run(u'\u2022 ' + text) run.font.name = 'Times New Roman'; run.font.size = Pt(12) return p # ════════════════════════════════════════════════════════════════════════════ # 3.3 BACKGROUND INFORMATION AND BRIEF LITERATURE REVIEW # ════════════════════════════════════════════════════════════════════════════ add_subsection_heading('3.3.', 'Background Information and Brief Literature Review') # --- Global & local burden of major abdominal surgery ---------------------- bg1 = ( 'Major abdominal surgery — defined as intraperitoneal procedures lasting more than one ' 'hour under general or regional anesthesia — represents one of the highest-risk categories ' 'of elective surgical care worldwide. Procedures in this group include open and laparoscopic ' 'colorectal resection, gastrectomy, hepatectomy, pancreatectomy, esophagectomy, and ' 'small-bowel resection. Globally, more than 300 million major surgical operations are ' 'performed annually, and the complication burden after abdominal surgery remains a major ' 'driver of perioperative mortality, intensive care utilization, and healthcare costs [1].' ) add_para(bg1, size=12, space_after=6) # --- Postoperative pulmonary complications ---------------------------------- bg2 = ( 'Postoperative pulmonary complications (PPCs) are among the most frequent and clinically ' 'consequential complications following major abdominal surgery. Based on the consensus ' 'Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative ' 'and Anaesthetic Care (StEP-COMPAC) definition, PPCs encompass a spectrum of disorders ' 'including pneumonia, respiratory failure requiring ventilatory support, pleural effusion ' 'requiring drainage, bronchospasm, and atelectasis requiring intervention. In a large ' 'international cohort study of 11,591 patients undergoing major abdominal surgery, the ' 'overall PPC rate was 7.8% using the StEP-COMPAC definition; however, rates vary widely ' '(9%–40%) depending on the operative site, patient population, and PPC definition used [10]. ' 'Among patients undergoing upper abdominal surgery specifically, Garg et al. (2025) reported ' 'a PPC incidence of 20.3%, with pleural effusion (11.3%), respiratory failure (7.5%), and ' 'pneumonia (4.5%) as the most common events [2].' ) add_para(bg2, size=12, space_after=6) bg3 = ( 'PPCs carry substantial prognostic weight. They are independently associated with prolonged ' 'hospital stay, escalation of care to the intensive care unit, increased 30-day and 90-day ' 'mortality, and significantly higher resource utilization. Boden et al. (2024) demonstrated ' 'in an individual patient-level meta-analysis of 800 patients across two randomized controlled ' 'trials that a single preoperative physiotherapy session reduced the odds of PPCs by 47% ' '(adjusted OR 0.53; 95% CI: 0.34–0.85), underscoring both the preventability of PPCs and ' 'the importance of identifying at-risk patients preoperatively [11].' ) add_para(bg3, size=12, space_after=6) # --- Current preoperative risk stratification tools ------------------------ bg4 = ( 'Current tools for preoperative PPC risk stratification are either complex, resource-intensive, ' 'or insufficiently validated. Existing risk prediction models — including the ARISCAT score, ' 'ASA Physical Status classification, Gupta Respiratory Failure Index, and spirometry-based ' 'risk estimates — show only moderate discriminative ability. In the STARSurg/TASMAN ' 'international validation study, none of the six externally validated prognostic models ' 'showed good discrimination (defined as AUROC ≥0.70) for PPCs; the ARISCAT score performed ' 'best with an AUROC of 0.700 (95% CI: 0.683–0.717) [10]. Similarly, a systematic review by ' 'Dankert et al. (2022) found that pulmonary function tests including spirometry provided ' 'inconclusive evidence for PPC prediction in non-thoracic surgery, with only a possible benefit ' 'identified in upper abdominal surgery subgroup analyses [12]. These findings highlight a ' 'critical gap: an objective, broadly applicable, and bedside-feasible tool for preoperative ' 'PPC risk stratification is currently lacking.' ) add_para(bg4, size=12, space_after=6) # --- Functional capacity and the physiologic basis for periop risk --------- bg5 = ( 'A patient\'s functional capacity — their ability to sustain aerobic metabolism during ' 'physical activity — is a fundamental determinant of perioperative risk. The physiologic ' 'basis is straightforward: surgery imposes an acute increase in whole-body oxygen demand ' 'through the stress response, inflammatory cascade, and the metabolic demands of tissue ' 'repair. Patients with limited preoperative cardiorespiratory reserve are unable to meet ' 'this demand, resulting in relative oxygen debt, organ dysfunction, and adverse outcomes. ' 'Rose et al. (2022) characterized this relationship in detail, demonstrating that impaired ' 'cardiorespiratory fitness (CRF) is an independent predictor of postoperative morbidity ' 'and mortality, and that CRF is the single greatest modifiable perioperative risk factor [3]. ' 'While cardiopulmonary exercise testing (CPET) provides the most objective metric of CRF ' 'via peak oxygen uptake (VO2 peak) and ventilatory anaerobic threshold, CPET requires ' 'specialized equipment, trained physiologists, and approximately 30–45 minutes per patient, ' 'limiting its routine perioperative use to well-resourced centers [9].' ) add_para(bg5, size=12, space_after=6) # --- The 6MWT: description, standardization, normative data ---------------- bg6 = ( 'The Six-Minute Walk Test (6MWT) is a standardized, submaximal exercise test in which the ' 'patient walks as far as possible along a flat, 30-meter corridor for six minutes, with ' 'the primary outcome being the six-minute walk distance (6MWD) in meters. The test was ' 'formally standardized by the American Thoracic Society (ATS) in 2002 and has been widely ' 'adopted across cardiopulmonary, oncology, musculoskeletal, and rehabilitation medicine. ' 'The 6MWT reflects the integrated performance of the pulmonary, cardiovascular, ' 'neuromuscular, and metabolic systems, and is strongly correlated with VO2 peak on formal ' 'CPET. Normative reference values for the 6MWT in adults have been well characterized: ' 'a systematic review and meta-analysis by Otadi and Malmir (2026) pooled data from 28 ' 'studies and reported mean 6MWDs of 473 m in older men and 428 m in older women, with ' 'distance declining by approximately 10.25 m per year of age [13]. For the Asian adult ' 'population — most relevant to a Filipino cohort — Yeung et al. (2022) reported an overall ' 'mean 6MWD of 578 m (±75 m), with age-stratified values ranging from 601 m in adults ' 'aged 21–39 to 519 m in those aged 60–80 [14]. These normative data provide a framework ' 'for identifying clinically relevant thresholds in the preoperative setting.' ) add_para(bg6, size=12, space_after=6) # --- 6MWT evidence in surgical populations --------------------------------- bg7 = ( 'Several prospective and retrospective studies have evaluated the 6MWT as a preoperative ' 'risk tool in surgical populations, with a growing body of evidence specifically addressing ' 'abdominal surgery. Soares and Nucci (2021) conducted a cross-sectional cohort study of ' '50 patients undergoing elective abdominal surgery and found that half developed early PPCs ' 'within the first seven postoperative days. The preoperative 6MWD was significantly shorter ' 'in patients who developed PPCs (444.8 m vs. 498.3 m; p = 0.013), and multivariable ' 'logistic regression confirmed 6MWD as an independent predictor of PPCs ' '(OR = 0.978; p = 0.010) for intestinal, gastric, and biliary tract resections [4]. ' 'In a prospective cohort of 100 liver transplant recipients, Magalhaes et al. (2017) ' 'demonstrated that every additional 50 m walked preoperatively was associated with a 41% ' 'reduction in the odds of postoperative respiratory complications ' '(OR = 0.589; 95% CI: 0.357–0.971; p = 0.03), establishing 6MWD as an independent ' 'predictor even in this complex surgical population [5].' ) add_para(bg7, size=12, space_after=6) bg8 = ( 'In oncologic surgery involving thoracoabdominal access, Inoue et al. (2020) found that ' 'a preoperative 6MWD of ≤454 m independently predicted grade II or higher Clavien-Dindo ' 'complications in 111 esophageal cancer patients undergoing thoracic surgery ' '(sensitivity 71.0%, specificity 54.8%) [6]. Similarly, Hattori et al. (2018) demonstrated ' 'in 321 patients undergoing lung resection for malignancy that a 6MWD of ≤450 m was ' 'significantly associated with postoperative pneumonia (p = 0.002), with 69.2% sensitivity ' 'and 71.1% specificity on ROC analysis [7]. These cut-off values, derived in thoracic ' 'surgical cohorts, provide a reasonable starting reference for comparable investigation ' 'in purely abdominal surgery populations, where the evidence remains sparse.' ) add_para(bg8, size=12, space_after=6) # --- Systematic review evidence ------------------------------------------- bg9 = ( 'At the level of systematic evidence, Makker et al. (2022) performed a meta-analysis of ' 'five studies encompassing 379 patients with gastrointestinal and abdominal cancer, ' 'finding that a preoperative 6MWT performance of ≥400 m was significantly associated with ' 'lower-grade postoperative complications (OR = 0.38; 95% CI: 0.15–0.95). The association ' 'with hospital length of stay, however, was not statistically significant ' '(MD = 3.29; 95% CI: -1.07 to 7.66) [8]. Argillander et al. (2022) reviewed objective ' 'preoperative physical tests in 23 studies of patients aged ≥65 undergoing major ' 'abdominal cancer surgery, concluding that the 6MWT and incremental shuttle walk test ' 'are feasible alternatives to CPET for aerobic capacity estimation, but that prospective ' 'studies with standardized protocols and consistent outcome definitions are still needed [9]. ' 'These reviews consistently identify a critical gap: the prospective prognostic value of ' 'a standardized preoperative 6MWT specifically in a non-oncologic, general major abdominal ' 'surgery population has not been rigorously established.' ) add_para(bg9, size=12, space_after=6) # --- Gap in local/Philippine context -------------------------------------- bg10 = ( 'In the Philippine clinical context, the routine preoperative assessment of functional ' 'capacity relies almost exclusively on clinical history and the subjective estimation of ' 'metabolic equivalents (METs). Formal exercise testing such as CPET is not routinely ' 'available outside academic tertiary centers. Published studies evaluating objective ' 'functional capacity measures as preoperative predictors of surgical complications in ' 'Filipino patients are essentially absent. Given the high incidence of major abdominal ' 'surgical disease, limited intensive care resources, and the need for practical and ' 'low-cost perioperative risk stratification tools in this setting, evaluating the 6MWT ' 'as a preoperative predictor fills a significant evidence gap. The present prospective ' 'cohort study is designed to address this gap by prospectively measuring preoperative ' '6MWD in adults undergoing major abdominal surgery and systematically ascertaining the ' 'occurrence of PPCs and major postoperative complications during the first 30 postoperative ' 'days.' ) add_para(bg10, size=12, space_after=12) # ════════════════════════════════════════════════════════════════════════════ # 4. OBJECTIVES # ════════════════════════════════════════════════════════════════════════════ add_section_heading('4', 'Objectives') # 4.1 General Objective add_subsection_heading('4.1.', 'General Objective') general_obj = ( 'To determine the prognostic value of the preoperative Six-Minute Walk Distance (6MWD) ' 'in predicting postoperative pulmonary complications and major postoperative complications ' 'among adults undergoing major abdominal surgery at a tertiary hospital.' ) add_para(general_obj, size=12, space_after=10) # 4.2 Specific Objectives add_subsection_heading('4.2.', 'Specific Objectives') specific_objs = [ ('1. ', 'To describe the baseline clinical and functional characteristics (including ' 'preoperative 6MWD, comorbidities, BMI, spirometry results, and ASA classification) ' 'of adult patients scheduled for major abdominal surgery.'), ('2. ', 'To determine the association between preoperative 6MWD and the occurrence of ' 'postoperative pulmonary complications (PPCs) within 30 days of surgery.'), ('3. ', 'To determine the association between preoperative 6MWD and the occurrence of ' 'major postoperative complications (Clavien-Dindo grade II or higher) within 30 days ' 'of surgery.'), ('4. ', 'To evaluate the relationship between preoperative 6MWD and secondary outcomes ' 'including: length of hospital stay, unplanned intensive care unit (ICU) admission, ' 'need for invasive or non-invasive mechanical ventilation, and in-hospital mortality.'), ('5. ', 'To identify an optimal preoperative 6MWD cut-off value, using ROC curve analysis, ' 'for predicting PPCs and major postoperative complications.'), ('6. ', 'To assess the discriminative accuracy of the preoperative 6MWT compared to ' 'established risk tools (ASA classification and ARISCAT score) in predicting PPCs.'), ] for num, text in specific_objs: add_bullet(num, text, indent=0.3) doc.add_paragraph() # ════════════════════════════════════════════════════════════════════════════ # 5. METHODS # ════════════════════════════════════════════════════════════════════════════ add_section_heading('5', 'Methods') # 5.1 Study Design, Time Period, Target Population add_subsection_heading('5.1.', 'Type of Study, Time Period and Target Population') study_design = ( 'This is a prospective analytic observational cohort study involving adult patients ' 'scheduled for elective or semi-elective major abdominal surgery at a tertiary hospital. ' 'Participants will undergo a standardized preoperative 6MWT and will be prospectively ' 'followed from the date of surgery until hospital discharge or 30 days postoperatively, ' 'whichever is shorter, for ascertainment of outcomes.' ) add_para(study_design, size=12, space_after=6) add_para('Time Period', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) tp1 = ( u'\u2022 Enrollment Period: Consecutive eligible patients will be enrolled over an ' 'estimated 12-month recruitment period.' ) tp2 = ( u'\u2022 Patient Follow-up: Each participant will be followed from the date of surgery ' 'through the 30th postoperative day (or until discharge if discharge occurs after ' 'day 30) for all outcome ascertainment.' ) for t in [tp1, tp2]: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.35) run = p.add_run(t) run.font.name = 'Times New Roman'; run.font.size = Pt(12) doc.add_paragraph() # 5.2 Criteria for Subject Selection add_subsection_heading('5.2.', 'Criteria for Subject Selection') add_para('5.2.1. Inclusion Criteria', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) inc = [ ('1. ', 'Age: ≥18 years old at the time of surgery.'), ('2. ', 'Surgery Type: Scheduled for elective or semi-elective major abdominal surgery ' '(intraperitoneal procedure with anticipated operative duration ≥60 minutes under ' 'general or regional anesthesia), including but not limited to colorectal resection, ' 'gastrectomy, hepatobiliary surgery, pancreatectomy, and small-bowel resection.'), ('3. ', 'Functional Assessment: Able to perform the preoperative 6MWT (must be ambulatory ' 'without a mobility aid that precludes standardized testing).'), ('4. ', 'Clinical Course: Inpatient stay resulting in either documented hospital discharge ' 'or documented in-hospital mortality.'), ('5. ', 'Consent: Provision of written informed consent prior to enrollment.'), ] for num, text in inc: add_bullet(num, text, indent=0.35) doc.add_paragraph() add_para('5.2.2. Exclusion Criteria', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) exc = [ ('1. ', 'Emergency Surgery: Patients undergoing emergency (non-elective) abdominal surgery ' 'in whom preoperative 6MWT cannot be safely performed.'), ('2. ', 'Inability to Walk: Patients who are non-ambulatory preoperatively or have ' 'neurological, orthopedic, or vascular conditions that independently preclude safe ' 'completion of a 6MWT (e.g., severe lower-limb ischemia, active musculoskeletal injury, ' 'hemiplegia).'), ('3. ', 'Hemodynamic Instability: Patients with resting hemodynamic instability or ' 'oxygen saturation <88% on room air at rest, in whom exercise testing is contraindicated.'), ('4. ', 'Severe Cardiorespiratory Disease: Patients with known unstable angina, acute ' 'decompensated heart failure, or acute exacerbation of COPD within four weeks of surgery.'), ('5. ', 'Prior Major Abdominal Surgery in the Same Admission: Patients undergoing ' 're-operation during the same index admission (to avoid confounding of outcomes).'), ('6. ', 'Data Incompleteness: Patients for whom key baseline or outcome data cannot ' 'be obtained.'), ] for num, text in exc: add_bullet(num, text, indent=0.35) doc.add_paragraph() # 5.3 Operational Definitions add_subsection_heading('5.3.', 'Operational Definitions, if applicable') # Build definitions table table = doc.add_table(rows=1, cols=2) table.style = 'Table Grid' table.columns[0].width = Inches(2.0) table.columns[1].width = Inches(4.0) hdr = table.rows[0].cells hdr[0].text = 'Variable' hdr[1].text = 'Definition' for cell in hdr: for run in cell.paragraphs[0].runs: run.bold = True run.font.size = Pt(11) run.font.name = 'Times New Roman' defs = [ ('Major Abdominal Surgery', 'Any intraperitoneal operative procedure (open or laparoscopic) anticipated to last ' '≥60 minutes under general or regional anesthesia, including colorectal resection, ' 'gastrectomy, hepatobiliary surgery, pancreatectomy, and small-bowel resection.'), ('Six-Minute Walk Test (6MWT)', 'A standardized submaximal exercise test in which the patient walks as far as possible ' 'on a flat, 30-meter corridor for six minutes. Administered per ATS 2002 guidelines. ' 'The primary metric is the six-minute walk distance (6MWD) in meters.'), ('Six-Minute Walk Distance (6MWD)', 'Total distance walked (in meters) during the 6MWT. A 6MWD below the study-defined ' 'cut-off (to be determined by ROC analysis) will classify the patient as having ' '"reduced functional capacity."'), ('Postoperative Pulmonary Complication (PPC)', 'Any of the following occurring within 30 days of surgery: (a) pneumonia — new ' 'pulmonary infiltrate with fever, leukocytosis, and purulent secretions; ' '(b) respiratory failure — SpO2 <90% on room air or requirement for mechanical ' 'ventilation beyond 24 hours postoperatively; (c) atelectasis — radiologically confirmed ' 'requiring physiotherapy or bronchoscopy; (d) pleural effusion — requiring drainage; ' '(e) bronchospasm — requiring bronchodilator treatment. Defined per StEP-COMPAC consensus.'), ('Major Postoperative Complication', 'Any postoperative complication graded Clavien-Dindo grade II or higher occurring ' 'within 30 days of surgery.'), ('Clavien-Dindo Grade II or Higher', 'Grade II: Complication requiring pharmacological treatment (e.g., antibiotics, ' 'anti-arrhythmics). Grade III: Requiring surgical, endoscopic, or radiological ' 'intervention. Grade IV: Life-threatening complication requiring ICU management. ' 'Grade V: Death.'), ('Unplanned ICU Admission', 'Transfer to the intensive care unit at any point following the initial return ' 'from the operating theater, not as part of the planned postoperative pathway.'), ('Mechanical Ventilation', 'Initiation of invasive mechanical ventilation via endotracheal tube or tracheostomy ' 'beyond the immediate post-anesthetic recovery period (>24 hours postoperatively).'), ('Non-Invasive Ventilation', 'Initiation of non-invasive positive pressure ventilation (CPAP, BiPAP) or ' 'high-flow nasal cannula (HFNC) beyond the immediate post-anesthetic recovery period.'), ('Prolonged Hospitalization', 'Total hospital length of stay exceeding the 75th percentile for the ' 'specific procedure type, or a stay of ≥14 days (whichever is defined a priori).'), ('In-Hospital Mortality', 'Death from any cause occurring during the index hospital admission.'), ('Reduced Functional Capacity', 'Preoperative 6MWD below the study-defined ROC-derived cut-off value, ' 'or a 6MWD of <400 m based on existing literature thresholds.'), ] for var, defn in defs: row = table.add_row() row.cells[0].text = var row.cells[1].text = defn for ci in [0, 1]: for run in row.cells[ci].paragraphs[0].runs: run.font.size = Pt(11) run.font.name = 'Times New Roman' add_para('Table 1: Operational Definitions', italic=True, size=11, align=WD_ALIGN_PARAGRAPH.CENTER, space_before=4, space_after=8) # 5.4 Study Procedure add_subsection_heading('5.4.', 'Description of Study Procedure') add_para('5.4.1. For observational (prospective cohort) studies:', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=4) add_para('5.4.1.1. Method of Subject Selection', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'Subjects will be identified prospectively from the surgical schedule of the institution. ' 'All adult patients listed for elective or semi-elective major abdominal surgery will be ' 'screened by the study team during the preoperative assessment visit, typically occurring ' '2–7 days prior to the scheduled operation. Eligible patients meeting inclusion criteria ' 'and providing written informed consent will be enrolled consecutively until the target ' 'sample size is achieved.', size=12, space_after=6 ) add_para('5.4.1.2. Data to Be Gathered', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'The following data points will be systematically collected using a standardized case ' 'report form:', size=12, space_after=4 ) data_items = [ ('Demographics: ', 'Age, sex, height, weight, body mass index (BMI).'), ('Clinical Characteristics: ', 'Comorbidities (e.g., COPD, diabetes mellitus, ' 'hypertension, ischemic heart disease, heart failure, chronic kidney disease, ' 'cerebrovascular disease), smoking status, ASA Physical Status classification, ' 'ARISCAT score.'), ('Exposure Variable: ', '6MWD (meters), resting and post-test heart rate, oxygen ' 'saturation (SpO2), Borg dyspnea scale, and reason for test termination (if applicable).'), ('Surgical Data: ', 'Type of procedure, operative approach (open vs. laparoscopic/robotic), ' 'estimated blood loss, operative duration, type of anesthesia.'), ('Postoperative Outcome Data (primary): ', 'Occurrence of any PPC within 30 days ' '(pneumonia, respiratory failure, atelectasis, pleural effusion, bronchospasm) — ' 'Yes/No with date.'), ('Postoperative Outcome Data (secondary): ', 'Clavien-Dindo grade of all complications, ' 'unplanned ICU admission, need for mechanical or non-invasive ventilation, length of ' 'hospital stay (days), in-hospital mortality.'), ] for label, text in data_items: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.4) r1 = p.add_run(u'\u2022 ' + label) r1.font.name = 'Times New Roman'; r1.font.size = Pt(12); r1.bold = True r2 = p.add_run(text) r2.font.name = 'Times New Roman'; r2.font.size = Pt(12) doc.add_paragraph() add_para('5.4.1.3. Description of Procedures to Be Done to Subjects', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'Each enrolled participant will undergo one standardized preoperative 6MWT performed ' 'by a trained research physiotherapist or nurse, within 2–7 days prior to surgery, ' 'following ATS (2002) guidelines. The test will be conducted on a flat, indoor, ' '30-meter corridor, clearly marked at each end. Standardized verbal encouragement ' 'will be provided at one-minute intervals. Participants will be instructed to walk as ' 'fast as safely possible for six minutes, and are permitted to slow down or stop if ' 'needed. Resting SpO2, heart rate, and Borg dyspnea scale will be recorded immediately ' 'before and after the test. The test will be terminated early if any safety criterion ' 'is met (SpO2 <85%, chest pain, acute dyspnea, or patient request). No other study ' 'procedures will be performed; all postoperative data will be obtained through ' 'prospective chart review and clinical follow-up.', size=12, space_after=6 ) add_para('5.4.1.4. Instruments Used for Measuring Exposure and/or Outcome', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) instruments = [ ('Exposure (6MWT): ', 'A measured 30-meter corridor, pulse oximeter, stopwatch, and ' 'Borg dyspnea scale. Distance will be recorded in meters to the nearest meter.'), ('Outcome Assessment (PPCs): ', 'Clinical, radiologic, and laboratory findings ' 'documented in the official patient chart by the attending surgical and medical team, ' 'adjudicated against the StEP-COMPAC consensus PPC definition.'), ('Outcome Assessment (Clavien-Dindo Grade): ', 'Classified by the principal investigator ' 'based on chart review of all postoperative events, using the standard ' 'Clavien-Dindo classification.'), ('Data Abstraction: ', 'A standardized Case Report Form (CRF) will serve as the primary ' 'instrument for uniform data collection.'), ('Risk Scores: ', 'ASA classification assigned by the attending anesthesiologist; ' 'ARISCAT score computed from seven preoperative variables at time of enrollment.'), ] for label, text in instruments: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.4) r1 = p.add_run(u'\u2022 ' + label) r1.font.name = 'Times New Roman'; r1.font.size = Pt(12); r1.bold = True r2 = p.add_run(text) r2.font.name = 'Times New Roman'; r2.font.size = Pt(12) doc.add_paragraph() add_para('5.4.1.5. Method of Validating Measuring Instruments', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'The 6MWT will be administered according to the standardized ATS 2002 protocol, ' 'ensuring reproducibility and comparability with published normative and prognostic ' 'data. Research personnel performing the test will undergo a standardized training ' 'session prior to study commencement to ensure protocol fidelity. Pulse oximeters ' 'will be calibrated and validated per hospital biomedical engineering standards. ' 'Postoperative outcome adjudication will be performed by two independent investigators ' 'blinded to the 6MWD results, with discrepancies resolved by consensus.', size=12, space_after=6 ) add_para('5.4.1.6. Laboratory Procedures to Be Performed, if any', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'No study-specific laboratory procedures will be performed beyond routine preoperative ' 'workup. Preoperative spirometry results (FEV1, FVC, FEV1/FVC) will be recorded if ' 'available as part of standard preoperative care. Routine admission laboratory values ' '(complete blood count, serum albumin, serum creatinine) will be extracted from the ' 'medical record.', size=12, space_after=6 ) add_para('5.4.1.7. Follow-Up Procedures', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'All enrolled patients will be followed prospectively from the date of surgery. ' 'Research personnel will perform daily postoperative chart reviews while the patient ' 'is hospitalized, and a structured clinical review at postoperative day 30 ' '(or at hospital discharge, whichever is later) to ascertain all outcome events. ' 'Patients discharged before day 30 will be contacted by telephone and/or reviewed ' 'at their scheduled outpatient follow-up appointment for 30-day outcome data.', size=12, space_after=8 ) # 5.5 Outcome Measures add_subsection_heading('5.5.', 'Description of Outcome Measures') add_para( 'The primary outcome measure is the occurrence of any postoperative pulmonary ' 'complication (PPC) within 30 days of surgery, defined per the StEP-COMPAC consensus ' 'framework. The rationale for selecting PPCs as the primary outcome is their high ' 'clinical relevance, direct link to impaired preoperative cardiorespiratory reserve, ' 'and consistent use as the primary outcome across the existing 6MWT perioperative ' 'literature [4,5,7,8].', size=12, space_after=6 ) add_para( 'A co-primary outcome is the occurrence of any major postoperative complication ' '(Clavien-Dindo grade II or higher) within 30 days of surgery. This broader composite ' 'outcome captures major systemic complications beyond the pulmonary domain and is ' 'consistent with grading systems used in comparable surgical outcome studies [6].', size=12, space_after=6 ) add_para( 'Secondary outcome measures include: length of hospital stay (continuous, in days), ' 'unplanned ICU admission (dichotomous — Yes/No), need for invasive mechanical ' 'ventilation beyond 24 hours postoperatively (dichotomous), need for non-invasive ' 'ventilation or high-flow nasal cannula beyond the immediate recovery period ' '(dichotomous), and in-hospital mortality (dichotomous). These secondary outcomes ' 'were selected based on their clinical relevance, their established association with ' 'reduced preoperative functional capacity, and their consistent use in comparable ' 'cohort studies and systematic reviews [3,4,5,8].', size=12, space_after=8 ) # 5.6 Sample Size add_subsection_heading('5.6.', 'Sample Size Estimation') add_para( 'Sample size was estimated based on the hypothesis that a lower preoperative 6MWD is ' 'independently associated with a higher rate of PPCs. Using data from Soares and Nucci ' '(2021) as the primary reference — which reported a PPC incidence of 30% in patients ' 'with 6MWD ≥400 m and 65% in those with 6MWD <400 m — with a two-sided alpha of 0.05, ' 'power of 80%, and an anticipated 1:1 ratio of exposed (low 6MWD) to unexposed (normal ' '6MWD) participants, the initial sample size is calculated at approximately 38 patients ' 'per group (76 total). To account for multivariable adjustment of up to 6 covariates ' '(at 10 events per variable) and an estimated 15% loss to follow-up or incomplete data, ' 'the final target sample size is 120 participants. Sample size was computed using ' 'standard logistic regression sample size formulae.', size=12, space_after=8 ) # 5.7 Data Analysis add_subsection_heading('5.7.', 'Data Analysis') add_para( 'Statistical analyses will be performed using SPSS version 29.0 or R (version 4.3 or ' 'higher). Descriptive statistics will summarize baseline clinical and functional ' 'characteristics. Categorical variables will be reported as frequencies and percentages; ' 'continuous variables will be reported as mean ± standard deviation or median ' '(interquartile range) depending on distribution. Univariable comparisons between ' 'patients who develop PPCs and those who do not will use the Chi-squared test or ' 'Fisher\'s exact test for categorical variables and the independent-samples t-test or ' 'Mann-Whitney U test for continuous variables, as appropriate.', size=12, space_after=6 ) add_para( 'The primary hypothesis will be tested using multivariable binary logistic regression, ' 'with PPC occurrence as the dependent variable and preoperative 6MWD as the primary ' 'independent variable, adjusting for clinically relevant covariates identified a priori ' '(age, sex, BMI, ASA class, operative duration, and procedure type). Results will be ' 'expressed as odds ratios (OR) with 95% confidence intervals (CI).', size=12, space_after=6 ) add_para( 'Receiver Operating Characteristic (ROC) curve analysis will be used to determine the ' 'optimal 6MWD cut-off value for predicting PPCs and major postoperative complications, ' 'with the Youden index used to identify the threshold that maximizes sensitivity and ' 'specificity. The area under the ROC curve (AUROC) will be calculated for the 6MWT, ' 'ASA classification, and ARISCAT score, with pairwise AUROC comparisons performed to ' 'assess the discriminative performance of the 6MWT relative to established risk tools. ' 'The level of significance is set at α = 0.05 (two-sided).', size=12, space_after=8 ) # 5.8 Ethical Considerations add_subsection_heading('5.8.', 'Ethical Consideration') add_para( 'This prospective study may enroll participants spanning a range of age groups, including ' 'elderly patients, and may include individuals with significant comorbidities. Given that ' 'the 6MWT is a validated, widely used clinical assessment with an established safety ' 'profile, and that no additional invasive procedures will be performed beyond routine ' 'preoperative care, the risk to participants is considered minimal. Strict confidentiality ' 'of all participant data will be maintained. All study records will be coded and ' 'de-identified, and investigators are responsible for the accuracy, completeness, ' 'and integrity of all collected data.', size=12, space_after=6 ) add_para('5.8.1. Method/s of Dealing with Adverse Events', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'While the 6MWT is associated with very low risk in supervised settings, the test will ' 'be immediately terminated if the participant develops chest pain, severe dyspnea, ' 'dizziness, leg cramps, pallor, or SpO2 <85%. A qualified clinician will be available ' 'during all test sessions and emergency protocols will be in place. Any adverse event ' 'occurring during the 6MWT will be documented, reported to the Principal Investigator, ' 'and managed per institutional protocols. Serious adverse events will be reported to ' 'the IERC within the required timeframe.', size=12, space_after=6 ) add_para('5.8.2. Anticipated Risks and Discomforts to Subjects', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'The anticipated physical risk to participants is minimal, limited primarily to the ' 'physiologic exertion of walking at a self-selected pace for six minutes, which is ' 'analogous to activities of daily living. Potential discomforts include transient ' 'breathlessness and fatigue, which are expected and self-limiting. Pre-test safety ' 'screening will exclude participants for whom exercise testing is contraindicated.', size=12, space_after=6 ) add_para('5.8.3. Expected Benefits to the Subject and to Others', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'The results of this study are expected to establish whether the preoperative 6MWT ' 'independently predicts PPCs and major complications following major abdominal surgery. ' 'If validated, the 6MWT could provide clinicians with a simple, immediately implementable ' 'preoperative risk stratification tool that enables targeted preventive interventions ' '(prehabilitation, physiotherapy, anesthesia optimization) in high-risk patients. ' 'The study will generate locally relevant evidence applicable to Filipino patients ' 'and similar resource-limited surgical settings.', size=12, space_after=6 ) add_para('5.8.4. Protection of Confidentiality', bold=True, size=12, align=WD_ALIGN_PARAGRAPH.LEFT, space_after=3) add_para( 'The following procedures will be strictly adhered to:', size=12, space_after=4 ) conf_items = [ 'A. The study shall abide by the Principles of the Declaration of Helsinki (2013) and ' 'will be conducted in accordance with the Guidelines of the International Conference ' 'on Harmonization – Good Clinical Practice (ICH-GCP).', 'B. The Clinical Protocol and all relevant documents shall be reviewed and approved ' 'by the SLMC Institutional Ethics Review Committee (IERC) prior to enrollment of any participant.', 'C. Patient confidentiality shall be maintained by assigning each participant a unique ' 'study code. No identifying information (name, birth date, hospital number) will be ' 'included in any study database or report.', 'D. Data Storage and Security: All study data will be stored in a password-protected, ' 'encrypted file accessible only to the Principal Investigator and Co-Investigators. ' 'All study documents will be retained by the Principal Investigator for a minimum of ' '5 years after study completion, after which they will be securely destroyed.', 'E. Inspection of Records: Access to study records is restricted to the Principal ' 'Investigator, Co-Investigators, and duly authorized representatives of the IERC ' 'or institutional audit bodies.', 'F. Results will be disseminated only in aggregate, de-identified form through ' 'peer-reviewed publication or scientific conference presentation.', ] for item in conf_items: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.35) run = p.add_run(item) run.font.name = 'Times New Roman'; run.font.size = Pt(12) doc.add_paragraph() # ════════════════════════════════════════════════════════════════════════════ # 7. REFERENCES (with new additions [10]–[14]) # ════════════════════════════════════════════════════════════════════════════ p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.LEFT p.paragraph_format.space_before = Pt(12) p.paragraph_format.space_after = Pt(6) run = p.add_run('7. References') run.font.name = 'Times New Roman'; run.font.size = Pt(12); run.bold = True all_refs = [ ('[1]', 'Smetana GW, Lawrence VA, Cornell JE; American College of Physicians. ' 'Preoperative pulmonary risk stratification for noncardiothoracic surgery: systematic ' 'review for the American College of Physicians. Ann Intern Med. 2006 Apr 18;144(8):581–95. ' 'doi: 10.7326/0003-4819-144-8-200604180-00009.'), ('[2]', 'Garg S, Govindaraj V, Dwivedi DP, Raja K, Theerthar EP. Postoperative pulmonary ' 'complications in patients undergoing upper abdominal surgery: risk factors and predictive ' 'models. Monaldi Arch Chest Dis. 2025 Mar 31. doi: 10.4081/monaldi.2024.2915. PMID: 38526466.'), ('[3]', 'Rose GA, Davies RG, Appadurai IR, Williams IM, Bashir M, Berg RMG. \'Fit for ' 'surgery\': the relationship between cardiorespiratory fitness and postoperative outcomes. ' 'Exp Physiol. 2022 Aug;107(8):780–95. doi: 10.1113/EP090156. PMID: 35579479.'), ('[4]', 'Soares SMTP, Nucci LB. Association between early pulmonary complications after ' 'abdominal surgery and preoperative physical capacity. Physiother Theory Pract. 2021 ' 'Jul;37(7):852–9. doi: 10.1080/09593985.2019.1650404. PMID: 31402737.'), ('[5]', 'Magalhaes CBA, Nogueira IC, Marinho LS, Daher EF, Garcia JHP, Viana CFG. Exercise ' 'capacity impairment can predict postoperative pulmonary complications after liver ' 'transplantation. Respiration. 2017;94(6):538–44. doi: 10.1159/000479008. PMID: 28738386.'), ('[6]', 'Inoue T, Ito S, Kanda M, Niwa Y, Nagaya M, Nishida Y. Preoperative six-minute ' 'walk distance as a predictor of postoperative complication in patients with esophageal ' 'cancer. Dis Esophagus. 2020 Mar 5;33(3):doz050. doi: 10.1093/dote/doz050. PMID: 31111872.'), ('[7]', 'Hattori K, Matsuda T, Takagi Y, Nagaya M, Inoue T, Nishida Y. Preoperative ' 'six-minute walk distance is associated with pneumonia after lung resection. Interact ' 'Cardiovasc Thorac Surg. 2018 Feb 1;26(2):208–13. doi: 10.1093/icvts/ivx310. PMID: 29049742.'), ('[8]', 'Makker PGS, Koh CE, Solomon MJ, Steffens D. Preoperative functional capacity and ' 'postoperative outcomes following abdominal and pelvic cancer surgery: a systematic review ' 'and meta-analysis. ANZ J Surg. 2022 Jul;92(7-8):1732–40. doi: 10.1111/ans.17577. ' 'PMID: 35253333.'), ('[9]', 'Argillander TE, Heil TC, Melis RJF, van Duijvendijk P, Klaase JM, van Munster BC. ' 'Preoperative physical performance as predictor of postoperative outcomes in patients aged ' '65 and older scheduled for major abdominal cancer surgery: a systematic review. Eur J Surg ' 'Oncol. 2022 Mar;48(3):575–84. doi: 10.1016/j.ejso.2021.09.019. PMID: 34629224.'), ('[10]', 'STARSurg Collaborative and TASMAN Collaborative. Evaluation of prognostic risk ' 'models for postoperative pulmonary complications in adult patients undergoing major ' 'abdominal surgery: a systematic review and international external validation cohort study. ' 'Lancet Digit Health. 2022 Jul;4(7):e498–e507. doi: 10.1016/S2589-7500(22)00069-3. ' 'PMID: 35750401.'), ('[11]', 'Boden I, Reeve J, Jernas A, Denehy L, Fagevik Olsen M. Preoperative physiotherapy ' 'prevents postoperative pulmonary complications after major abdominal surgery: a ' 'meta-analysis of individual patient data. J Physiother. 2024 Jul;70(3):195–202. ' 'doi: 10.1016/j.jphys.2024.02.012. PMID: 38472053.'), ('[12]', 'Dankert A, Dohrmann T, Loser B, Zapf A, Zollner C, Petzoldt M. Pulmonary function ' 'tests for the prediction of postoperative pulmonary complications. Dtsch Arztebl Int. ' '2022 Feb 18;119(7):99–106. doi: 10.3238/arztebl.m2022.0074. PMID: 34939921.'), ('[13]', 'Otadi K, Malmir K. Normative reference values for the six-minute walk test in ' 'older adults: a systematic review and meta-analysis. Arch Gerontol Geriatr. 2026 ' 'Jan;128:106026. doi: 10.1016/j.archger.2025.106026. PMID: 41027267.'), ('[14]', 'Yeung MT, Chan MY, Huang KS, Chen TJ, Chia CP, Fong MM. Normative reference values ' 'and regression equations to predict the 6-minute walk distance in the Asian adult ' 'population aged 21-80 years. Hong Kong Physiother J. 2022 Dec;42(2):69–78. ' 'doi: 10.1142/S1013702522500111. PMID: 37560171.'), ] for num, text in all_refs: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.4) p.paragraph_format.first_line_indent = Inches(-0.4) r1 = p.add_run(num + ' ') r1.font.name = 'Times New Roman'; r1.font.size = Pt(12) r2 = p.add_run(text) r2.font.name = 'Times New Roman'; r2.font.size = Pt(12) # Footer doc.add_paragraph() fp = doc.add_paragraph() fp.alignment = WD_ALIGN_PARAGRAPH.RIGHT fp.paragraph_format.space_before = Pt(24) r = fp.add_run('Protocol Version 01 dated ________________') r.font.name = 'Times New Roman'; r.font.size = Pt(10) # ── Save ───────────────────────────────────────────────────────────────────── out = '/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx' doc.save(out) print(f'Saved: {out}') PYEOF python3 /tmp/workspace/6mwt-proposal/append_sections.py

cat << 'PYEOF' > /tmp/workspace/6mwt-proposal/build_complete.py from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH doc = Document() # ── Page margins ───────────────────────────────────────────────────────────── for section in doc.sections: section.top_margin = Inches(1.0) section.bottom_margin = Inches(1.0) section.left_margin = Inches(1.25) section.right_margin = Inches(1.0) # ── Default style ───────────────────────────────────────────────────────────── style = doc.styles['Normal'] style.font.name = 'Times New Roman' style.font.size = Pt(12) style.paragraph_format.space_after = Pt(0) style.paragraph_format.space_before = Pt(0) # ── Helpers ─────────────────────────────────────────────────────────────────── def set_font(run, bold=False, italic=False, size=12, name='Times New Roman'): run.font.name = name; run.font.size = Pt(size) run.bold = bold; run.italic = italic def para(text='', bold=False, italic=False, size=12, align=WD_ALIGN_PARAGRAPH.JUSTIFY, sb=0, sa=6, li=0, fi=0): p = doc.add_paragraph() p.alignment = align p.paragraph_format.space_before = Pt(sb) p.paragraph_format.space_after = Pt(sa) p.paragraph_format.left_indent = Inches(li) p.paragraph_format.first_line_indent = Inches(fi) if text: r = p.add_run(text) set_font(r, bold=bold, italic=italic, size=size) return p def sec_h(num, title): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.LEFT p.paragraph_format.space_before = Pt(12); p.paragraph_format.space_after = Pt(6) r = p.add_run(f"{num}. {title}") set_font(r, bold=True) def sub_h(num, title): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.LEFT p.paragraph_format.space_before = Pt(8); p.paragraph_format.space_after = Pt(4) r = p.add_run(f"{num} {title}") set_font(r, bold=True) def bullet(label, text, li=0.35): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(li) r1 = p.add_run(label); set_font(r1, bold=True) r2 = p.add_run(text); set_font(r2) def dot(label, text, li=0.4): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(li) r1 = p.add_run(u'\u2022 ' + label); set_font(r1, bold=True) r2 = p.add_run(text); set_font(r2) def ref_entry(num, text): p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.4) p.paragraph_format.first_line_indent = Inches(-0.4) r1 = p.add_run(num + ' '); set_font(r1) r2 = p.add_run(text); set_font(r2) # ════════════════════════════════════════════════════════════════════════════ # HEADER # ════════════════════════════════════════════════════════════════════════════ para('DATABANK INFO NEEDED:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=2) para('No Yes', align=WD_ALIGN_PARAGRAPH.LEFT, sa=2) para('1. R&B Form No. 1A', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=4) para('St. Luke\'s Medical Center', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sa=2) para('RESEARCH AND BIOTECHNOLOGY', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sa=2) para('1.1. CLINICAL RESEARCH PROPOSAL', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sa=10) para('Research Title:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'Prognostic Value of the Six-Minute Walk Test in Predicting Postoperative Pulmonary ' 'and Major Complications Among Adults Undergoing Major Abdominal Surgery: ' 'A Prospective Cohort Study', align=WD_ALIGN_PARAGRAPH.LEFT, sa=10 ) para('Investigators:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) # Investigators table tbl = doc.add_table(rows=4, cols=2) tbl.style = 'Table Grid' tbl.columns[0].width = Inches(3.5) tbl.columns[1].width = Inches(2.5) for i, h in enumerate(['Name and Signature', 'Unit/Position']): c = tbl.cell(0, i); c.text = h for r in c.paragraphs[0].runs: r.bold = True; r.font.size = Pt(11); r.font.name = 'Times New Roman' rows_data = [ ('Project Leader/s:\n\n_______________________________', 'Consultant\n(Consultant/Manager/Faculty)'), ('Co-Project Leader/s:\n\n_______________________________', 'Pulmonary/Surgery Fellow\n(Resident/Fellow/Student)'), ('Research Fellow:\n\n_______________________________', ''), ] for i, (n, u) in enumerate(rows_data): tbl.cell(i+1, 0).text = n; tbl.cell(i+1, 1).text = u for ci in [0, 1]: for r in tbl.cell(i+1, ci).paragraphs[0].runs: r.font.size = Pt(11); r.font.name = 'Times New Roman' doc.add_paragraph() para('Inst./Dept./Center/Group: Department of Surgery / Anesthesiology', align=WD_ALIGN_PARAGRAPH.LEFT, sa=12) # ════════════════════════════════════════════════════════════════════════════ # SECTION 2 – BRIEF DESCRIPTION / SUMMARY # ════════════════════════════════════════════════════════════════════════════ sec_h('2', 'Brief Description / Summary') para( 'Major abdominal surgery carries a significant risk of postoperative pulmonary ' 'complications (PPCs) and other major adverse outcomes, which contribute substantially ' 'to perioperative morbidity and mortality. Identifying patients at high risk before surgery ' 'enables targeted preventive strategies, yet current preoperative risk tools are either ' 'resource-intensive or rely on subjective estimates of functional capacity.', sa=6 ) para( 'This study investigates the prognostic value of the Six-Minute Walk Test (6MWT), ' 'a simple, low-cost, and widely available field exercise test that objectively quantifies ' 'functional exercise capacity through the distance walked in six minutes (6MWD). The 6MWT ' 'is hypothesized to serve as a composite marker reflecting cardiorespiratory reserve, ' 'physical conditioning, and overall physiologic resilience — factors directly relevant to ' 'the ability to withstand surgical stress.', sa=6 ) para( 'This will be a prospective cohort study at a tertiary hospital involving adult patients ' 'scheduled for elective or semi-elective major abdominal surgery. The 6MWT will be ' 'performed preoperatively, and participants will be followed through the postoperative ' 'period to ascertain the occurrence of PPCs and other major complications. The 6MWT could ' 'offer clinicians a practical, accessible, and inexpensive preoperative risk stratification ' 'tool, enabling timely optimization and potentially improving patient outcomes in both ' 'resource-rich and resource-limited settings.', sa=12 ) # ════════════════════════════════════════════════════════════════════════════ # SECTION 3 – INTRODUCTION # ════════════════════════════════════════════════════════════════════════════ sec_h('3', 'Introduction') # ─── 3.1 Significance ──────────────────────────────────────────────────────── sub_h('3.1.', 'Significance of the Project') para( 'Major abdominal surgery encompasses a broad range of high-risk procedures including ' 'colorectal resection, hepatobiliary surgery, gastrectomy, and pancreaticoduodenectomy. ' 'These procedures are associated with postoperative pulmonary complication rates ranging ' 'from 9% to 40%, depending on patient demographics, comorbidities, and surgical complexity ' '[1]. PPCs — encompassing pneumonia, respiratory failure, atelectasis requiring intervention, ' 'pleural effusion, bronchospasm, and aspiration — are among the most common causes of ' 'perioperative morbidity, prolonged hospital stay, and mortality following abdominal surgery [2].', sa=6 ) para( 'Despite their clinical importance, a reliable and practical bedside tool for preoperative ' 'risk prediction of PPCs remains elusive. Current tools such as the ARISCAT (Assess Respiratory ' 'Risk in Surgical Patients in Catalonia) score and ASA Physical Status classification provide ' 'general risk estimates but do not directly capture an individual\'s functional reserve. ' 'Cardiopulmonary exercise testing (CPET) is widely regarded as the gold standard for objective ' 'preoperative functional assessment, but it requires specialized equipment, trained personnel, ' 'and considerable time and cost, limiting its routine use especially in resource-limited ' 'settings [3].', sa=6 ) para( 'The Six-Minute Walk Test (6MWT) offers a compelling alternative: it requires no specialized ' 'equipment beyond a measured corridor, takes less than ten minutes to administer, and yields ' 'an objective, reproducible measure of submaximal exercise tolerance — the six-minute walk ' 'distance (6MWD). The 6MWT is already validated and widely used in cardiopulmonary ' 'rehabilitation and chronic disease management. Its perioperative application, particularly ' 'in abdominal surgery, is an emerging area of study with important clinical and public health ' 'implications.', sa=6 ) para('The study aims to:', sa=4) aims = [ ('1. ', 'Determine the prognostic value of preoperative 6MWD in predicting PPCs and major ' 'postoperative complications in adults undergoing major abdominal surgery at a tertiary hospital.'), ('2. ', 'Identify an optimal 6MWD cut-off value that discriminates patients at high versus ' 'low risk for PPCs, enabling bedside clinical risk stratification.'), ('3. ', 'Contribute locally relevant evidence on preoperative functional capacity assessment ' 'in a setting where CPET is not routinely available, supporting cost-effective perioperative ' 'care pathways.'), ] for num, text in aims: bullet(num, text, li=0.3) doc.add_paragraph() # ─── 3.2 Rationale ─────────────────────────────────────────────────────────── sub_h('3.2.', 'Rationale for Doing the Study') para( 'Timely and accurate preoperative risk stratification remains a persistent challenge in the ' 'perioperative management of patients undergoing major abdominal surgery. Existing severity ' 'scoring systems and risk indices are often underutilized due to complexity, limited ' 'applicability across different populations, or the absence of objective measures of a ' 'patient\'s functional reserve.', sa=6 ) para( 'Functional capacity — defined as the ability of an individual to perform physical activities ' 'that require aerobic metabolism — is a well-established, independent determinant of ' 'perioperative risk. Rose et al. (2022) reviewed the physiological basis linking cardiorespiratory ' 'fitness (CRF) to postoperative outcomes, demonstrating that impaired CRF is an independent risk ' 'factor for mortality and morbidity. Surgery triggers a period of substantially increased oxygen ' 'demand; patients unable to meet this demand face greater risk of organ failure and death. ' 'The authors emphasized that CRF is the greatest modifiable perioperative risk factor, and its ' 'accurate preoperative detection is essential for risk classification and patient management [3].', sa=6 ) para( 'The 6MWT provides an objective, standardized measure of submaximal exercise capacity that is ' 'closely correlated with peak oxygen consumption (VO2 peak) and reflects the integrated response ' 'of the cardiorespiratory, neuromuscular, and metabolic systems. Unlike CPET, the 6MWT is simple, ' 'inexpensive, and reproducible, making it practical for routine preoperative assessment even in ' 'settings with limited resources. Crucially, the 6MWT captures not only cardiorespiratory fitness ' 'but also the patient\'s nutritional status, muscle strength, and motivational state — all factors ' 'that independently influence surgical outcomes.', sa=6 ) para( 'Several studies have examined the relationship between preoperative 6MWD and postoperative ' 'outcomes. Soares and Nucci (2021) conducted a prospective cohort study of 50 patients ' 'undergoing elective abdominal surgery, finding that 25 (50%) developed postoperative pulmonary ' 'complications within the first seven postoperative days. The mean preoperative 6MWD was ' 'significantly shorter among those who developed PPCs (444.8 m vs. 498.3 m; p = 0.013). ' 'Multivariable logistic regression confirmed that a lower preoperative 6MWD was significantly ' 'and independently associated with PPCs (OR = 0.978; p = 0.010) in patients undergoing ' 'intestinal, gastric, or biliary tract resection [4].', sa=6 ) para( 'Extending this evidence to other abdominal organ surgeries, Magalhaes et al. (2017) ' 'prospectively studied 100 patients undergoing liver transplantation, finding that 44 developed ' 'at least one postoperative respiratory complication. In logistic regression analysis, each ' 'additional 50 meters walked during the preoperative 6MWT was associated with a 41% reduction ' 'in the odds of developing PPCs (OR = 0.589; 95% CI: 0.357–0.971; p = 0.03), establishing the ' '6MWT as an independent predictor of postoperative pulmonary complications in this population [5].', sa=6 ) para( 'In the oncologic setting, Inoue et al. (2020) retrospectively reviewed 111 patients undergoing ' 'thoracic surgery for esophageal cancer and found that a preoperative 6MWD of ≤454 m was a ' 'significant threshold for predicting grade II or higher Clavien-Dindo complications, with 71.0% ' 'sensitivity and 54.8% specificity. In multiple regression analysis, lower 6MWD was an ' 'independent preoperative risk factor for major complications [6]. Similarly, Hattori et al. ' '(2018) demonstrated in a retrospective analysis of 321 patients undergoing lung resection for ' 'malignancy that a preoperative 6MWD ≤450 m predicted postoperative pneumonia with 69.2% ' 'sensitivity and 71.1% specificity (p = 0.002) [7].', sa=6 ) para( 'At the level of systematic evidence, Makker et al. (2022) performed a systematic review and ' 'meta-analysis of five studies (379 patients) evaluating preoperative 6MWT or five-times ' 'sit-to-stand performance and postoperative outcomes in gastrointestinal and abdominal cancer ' 'surgery. Higher preoperative 6MWT performance (≥400 m) was significantly associated with ' 'lower-grade postoperative complications (OR = 0.38; 95% CI: 0.15–0.95), though the association ' 'with length of stay was not significant. The authors noted the need for high-quality prospective ' 'studies with standardized definitions and broader patient populations [8].', sa=6 ) para( 'Argillander et al. (2022) conducted a systematic review of preoperative physical performance ' 'tests and their predictive value for postoperative outcomes specifically in patients aged ≥65 ' 'years undergoing major abdominal cancer surgery. Among non-CPET field tests, the 6MWT and the ' 'Incremental Shuttle Walk Test (ISWT) predicted outcomes in two studies each. The authors ' 'concluded that the 6MWT is a feasible alternative to CPET for estimating aerobic capacity in ' 'older surgical patients, but emphasized the need for prospective studies comparing different ' 'physical tests in a standardized manner [9].', sa=6 ) para( 'In a recent study by Garg et al. (2025) evaluating predictive models for PPCs in upper ' 'abdominal surgery, 20.3% of 133 patients developed PPCs. While the study\'s multivariable ' 'analysis highlighted abnormal chest radiograph, blood urea nitrogen, and duration of surgery ' 'as independent predictors, the investigators specifically tested the 6MWT against established ' 'risk scores. Although 6MWD lacked independent predictive power in that particular cohort, the ' 'authors acknowledged that the study was limited by its single-centre retrospective design and ' 'variable timing of 6MWT administration, and called for prospective designs with standardized ' 'protocols to better characterize the 6MWT\'s predictive role [2].', sa=6 ) para( 'The 6MWT uniquely serves as a composite indicator: reduced 6MWD reflects not only limited ' 'cardiorespiratory reserve but also deconditioning, sarcopenia, and poor nutritional status — ' 'all factors shown to independently worsen surgical outcomes. Since the 6MWT is inexpensive, ' 'non-invasive, and requires minimal equipment or personnel training, it is ideally suited for ' 'routine preoperative application in most hospital settings, including those with limited access ' 'to CPET or advanced physiologic testing.', sa=6 ) para( 'Locally, there is a significant evidence gap regarding the use of objective functional capacity ' 'measures in preoperative risk assessment for abdominal surgery. Existing perioperative practice ' 'largely relies on subjective estimates of metabolic equivalents (METs) via patient history rather ' 'than objective testing. Given the high burden of abdominal surgical disease in the Philippines ' 'and the challenges of access to advanced perioperative testing, there is a compelling rationale ' 'to evaluate whether the 6MWT — a simple, low-cost tool — can reliably predict PPCs and major ' 'complications in a local surgical population. Demonstrating its predictive value could establish ' 'the 6MWT as a practical and immediately implementable preoperative risk stratification tool for ' 'Filipino surgical patients and similar resource-constrained settings.', sa=12 ) # ─── 3.3 Background Information ────────────────────────────────────────────── sub_h('3.3.', 'Background Information and Brief Literature Review') para( 'Major abdominal surgery — defined as intraperitoneal procedures lasting more than one hour ' 'under general or regional anesthesia — represents one of the highest-risk categories of ' 'elective surgical care worldwide. Procedures in this group include open and laparoscopic ' 'colorectal resection, gastrectomy, hepatectomy, pancreatectomy, esophagectomy, and ' 'small-bowel resection. Globally, more than 300 million major surgical operations are performed ' 'annually, and the complication burden after abdominal surgery remains a major driver of ' 'perioperative mortality, intensive care utilization, and healthcare costs [1].', sa=6 ) para( 'Postoperative pulmonary complications (PPCs) are among the most frequent and clinically ' 'consequential complications following major abdominal surgery. Based on the consensus ' 'Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative ' 'and Anaesthetic Care (StEP-COMPAC) definition, PPCs encompass a spectrum of disorders ' 'including pneumonia, respiratory failure requiring ventilatory support, pleural effusion ' 'requiring drainage, bronchospasm, and atelectasis requiring intervention. In a large ' 'international cohort study of 11,591 patients undergoing major abdominal surgery, the overall ' 'PPC rate was 7.8% using the StEP-COMPAC definition; however, rates vary widely (9%–40%) ' 'depending on the operative site, patient population, and PPC definition used [10]. Among ' 'patients undergoing upper abdominal surgery specifically, Garg et al. (2025) reported a PPC ' 'incidence of 20.3%, with pleural effusion (11.3%), respiratory failure (7.5%), and pneumonia ' '(4.5%) as the most common events [2].', sa=6 ) para( 'PPCs carry substantial prognostic weight. They are independently associated with prolonged ' 'hospital stay, escalation of care to the intensive care unit, increased 30-day and 90-day ' 'mortality, and significantly higher resource utilization. Boden et al. (2024) demonstrated ' 'in an individual patient-level meta-analysis of 800 patients across two randomized controlled ' 'trials that a single preoperative physiotherapy session reduced the odds of PPCs by 47% ' '(adjusted OR 0.53; 95% CI: 0.34–0.85), underscoring both the preventability of PPCs and the ' 'importance of identifying at-risk patients preoperatively [11].', sa=6 ) para( 'Current tools for preoperative PPC risk stratification are either complex, resource-intensive, ' 'or insufficiently validated. Existing risk prediction models — including the ARISCAT score, ' 'ASA Physical Status classification, Gupta Respiratory Failure Index, and spirometry-based risk ' 'estimates — show only moderate discriminative ability. In the STARSurg/TASMAN international ' 'validation study, none of the six externally validated prognostic models showed good ' 'discrimination (defined as AUROC ≥0.70) for PPCs; the ARISCAT score performed best with an ' 'AUROC of 0.700 (95% CI: 0.683–0.717) [10]. Similarly, a systematic review by Dankert et al. ' '(2022) found that pulmonary function tests including spirometry provided inconclusive evidence ' 'for PPC prediction in non-thoracic surgery, with only a possible benefit identified in upper ' 'abdominal surgery subgroup analyses [12]. These findings highlight a critical gap: an objective, ' 'broadly applicable, and bedside-feasible tool for preoperative PPC risk stratification is ' 'currently lacking.', sa=6 ) para( 'A patient\'s functional capacity — their ability to sustain aerobic metabolism during physical ' 'activity — is a fundamental determinant of perioperative risk. The physiologic basis is ' 'straightforward: surgery imposes an acute increase in whole-body oxygen demand through the ' 'stress response, inflammatory cascade, and the metabolic demands of tissue repair. Patients ' 'with limited preoperative cardiorespiratory reserve are unable to meet this demand, resulting ' 'in relative oxygen debt, organ dysfunction, and adverse outcomes. Rose et al. (2022) ' 'characterized this relationship in detail, demonstrating that impaired cardiorespiratory fitness ' '(CRF) is an independent predictor of postoperative morbidity and mortality, and that CRF is the ' 'single greatest modifiable perioperative risk factor [3]. While cardiopulmonary exercise testing ' '(CPET) provides the most objective metric of CRF via peak oxygen uptake (VO2 peak) and ' 'ventilatory anaerobic threshold, CPET requires specialized equipment, trained physiologists, ' 'and approximately 30–45 minutes per patient, limiting its routine perioperative use to ' 'well-resourced centers [9].', sa=6 ) para( 'The Six-Minute Walk Test (6MWT) is a standardized, submaximal exercise test in which the ' 'patient walks as far as possible along a flat, 30-meter corridor for six minutes, with the ' 'primary outcome being the six-minute walk distance (6MWD) in meters. The test was formally ' 'standardized by the American Thoracic Society (ATS) in 2002 and has been widely adopted across ' 'cardiopulmonary, oncology, musculoskeletal, and rehabilitation medicine. The 6MWT reflects the ' 'integrated performance of the pulmonary, cardiovascular, neuromuscular, and metabolic systems, ' 'and is strongly correlated with VO2 peak on formal CPET. Normative reference values for the ' '6MWT in adults have been well characterized: a systematic review and meta-analysis by Otadi ' 'and Malmir (2026) pooled data from 28 studies and reported mean 6MWDs of 473 m in older men ' 'and 428 m in older women, with distance declining by approximately 10.25 m per year of age [13]. ' 'For the Asian adult population — most relevant to a Filipino cohort — Yeung et al. (2022) ' 'reported an overall mean 6MWD of 578 m (±75 m), with age-stratified values ranging from 601 m ' 'in adults aged 21–39 to 519 m in those aged 60–80 [14]. These normative data provide a ' 'framework for identifying clinically relevant thresholds in the preoperative setting.', sa=6 ) para( 'Several prospective and retrospective studies have evaluated the 6MWT as a preoperative risk ' 'tool in surgical populations, with a growing body of evidence specifically addressing abdominal ' 'surgery. Soares and Nucci (2021) conducted a cross-sectional cohort study of 50 patients ' 'undergoing elective abdominal surgery and found that half developed early PPCs within the first ' 'seven postoperative days. The preoperative 6MWD was significantly shorter in patients who ' 'developed PPCs (444.8 m vs. 498.3 m; p = 0.013), and multivariable logistic regression confirmed ' '6MWD as an independent predictor of PPCs (OR = 0.978; p = 0.010) for intestinal, gastric, and ' 'biliary tract resections [4]. In a prospective cohort of 100 liver transplant recipients, ' 'Magalhaes et al. (2017) demonstrated that every additional 50 m walked preoperatively was ' 'associated with a 41% reduction in the odds of postoperative respiratory complications ' '(OR = 0.589; 95% CI: 0.357–0.971; p = 0.03), establishing 6MWD as an independent predictor ' 'even in this complex surgical population [5].', sa=6 ) para( 'In oncologic surgery involving thoracoabdominal access, Inoue et al. (2020) found that a ' 'preoperative 6MWD of ≤454 m independently predicted grade II or higher Clavien-Dindo ' 'complications in 111 esophageal cancer patients undergoing thoracic surgery (sensitivity 71.0%, ' 'specificity 54.8%) [6]. Similarly, Hattori et al. (2018) demonstrated in 321 patients ' 'undergoing lung resection for malignancy that a 6MWD of ≤450 m was significantly associated ' 'with postoperative pneumonia (p = 0.002), with 69.2% sensitivity and 71.1% specificity on ROC ' 'analysis [7]. These cut-off values, derived in thoracic surgical cohorts, provide a reasonable ' 'starting reference for comparable investigation in purely abdominal surgery populations, where ' 'the evidence remains sparse.', sa=6 ) para( 'At the level of systematic evidence, Makker et al. (2022) performed a meta-analysis of five ' 'studies encompassing 379 patients with gastrointestinal and abdominal cancer, finding that a ' 'preoperative 6MWT performance of ≥400 m was significantly associated with lower-grade ' 'postoperative complications (OR = 0.38; 95% CI: 0.15–0.95). The association with hospital ' 'length of stay, however, was not statistically significant (MD = 3.29; 95% CI: −1.07 to 7.66) ' '[8]. Argillander et al. (2022) reviewed objective preoperative physical tests in 23 studies of ' 'patients aged ≥65 undergoing major abdominal cancer surgery, concluding that the 6MWT and ' 'incremental shuttle walk test are feasible alternatives to CPET for aerobic capacity estimation, ' 'but that prospective studies with standardized protocols and consistent outcome definitions are ' 'still needed [9]. These reviews consistently identify a critical gap: the prospective prognostic ' 'value of a standardized preoperative 6MWT specifically in a non-oncologic, general major ' 'abdominal surgery population has not been rigorously established.', sa=6 ) para( 'In the Philippine clinical context, the routine preoperative assessment of functional capacity ' 'relies almost exclusively on clinical history and the subjective estimation of metabolic ' 'equivalents (METs). Formal exercise testing such as CPET is not routinely available outside ' 'academic tertiary centers. Published studies evaluating objective functional capacity measures ' 'as preoperative predictors of surgical complications in Filipino patients are essentially absent. ' 'Given the high incidence of major abdominal surgical disease, limited intensive care resources, ' 'and the need for practical and low-cost perioperative risk stratification tools in this setting, ' 'evaluating the 6MWT as a preoperative predictor fills a significant evidence gap. The present ' 'prospective cohort study is designed to address this gap by prospectively measuring preoperative ' '6MWD in adults undergoing major abdominal surgery and systematically ascertaining the occurrence ' 'of PPCs and major postoperative complications during the first 30 postoperative days.', sa=12 ) # ════════════════════════════════════════════════════════════════════════════ # SECTION 4 – OBJECTIVES # ════════════════════════════════════════════════════════════════════════════ sec_h('4', 'Objectives') sub_h('4.1.', 'General Objective') para( 'To determine the prognostic value of the preoperative Six-Minute Walk Distance (6MWD) in ' 'predicting postoperative pulmonary complications and major postoperative complications among ' 'adults undergoing major abdominal surgery at a tertiary hospital.', sa=10 ) sub_h('4.2.', 'Specific Objectives') spec = [ ('1. ', 'To describe the baseline clinical and functional characteristics (including preoperative ' '6MWD, comorbidities, BMI, spirometry results, and ASA classification) of adult patients ' 'scheduled for major abdominal surgery.'), ('2. ', 'To determine the association between preoperative 6MWD and the occurrence of ' 'postoperative pulmonary complications (PPCs) within 30 days of surgery.'), ('3. ', 'To determine the association between preoperative 6MWD and the occurrence of major ' 'postoperative complications (Clavien-Dindo grade II or higher) within 30 days of surgery.'), ('4. ', 'To evaluate the relationship between preoperative 6MWD and secondary outcomes including: ' 'length of hospital stay, unplanned intensive care unit (ICU) admission, need for invasive or ' 'non-invasive mechanical ventilation, and in-hospital mortality.'), ('5. ', 'To identify an optimal preoperative 6MWD cut-off value, using ROC curve analysis, for ' 'predicting PPCs and major postoperative complications.'), ('6. ', 'To assess the discriminative accuracy of the preoperative 6MWT compared to established ' 'risk tools (ASA classification and ARISCAT score) in predicting PPCs.'), ] for num, text in spec: bullet(num, text, li=0.3) doc.add_paragraph() # ════════════════════════════════════════════════════════════════════════════ # SECTION 5 – METHODS # ════════════════════════════════════════════════════════════════════════════ sec_h('5', 'Methods') # 5.1 sub_h('5.1.', 'Type of Study, Time Period and Target Population') para( 'This is a prospective analytic observational cohort study involving adult patients scheduled ' 'for elective or semi-elective major abdominal surgery at a tertiary hospital. Participants ' 'will undergo a standardized preoperative 6MWT and will be prospectively followed from the date ' 'of surgery until hospital discharge or 30 days postoperatively, whichever is shorter, for ' 'ascertainment of outcomes.', sa=6 ) para('Time Period', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) for t in [ u'\u2022 Enrollment Period: Consecutive eligible patients will be enrolled over an estimated ' '12-month recruitment period.', u'\u2022 Patient Follow-up: Each participant will be followed from the date of surgery through ' 'the 30th postoperative day (or until discharge if discharge occurs after day 30) for all ' 'outcome ascertainment.', ]: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.35) r = p.add_run(t); set_font(r) doc.add_paragraph() # 5.2 sub_h('5.2.', 'Criteria for Subject Selection') para('5.2.1. Inclusion Criteria', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) inc = [ ('1. ', 'Age: ≥18 years old at the time of surgery.'), ('2. ', 'Surgery Type: Scheduled for elective or semi-elective major abdominal surgery ' '(intraperitoneal procedure with anticipated operative duration ≥60 minutes under general or ' 'regional anesthesia), including but not limited to colorectal resection, gastrectomy, ' 'hepatobiliary surgery, pancreatectomy, and small-bowel resection.'), ('3. ', 'Functional Assessment: Able to perform the preoperative 6MWT (must be ambulatory ' 'without a mobility aid that precludes standardized testing).'), ('4. ', 'Clinical Course: Inpatient stay resulting in either documented hospital discharge or ' 'documented in-hospital mortality.'), ('5. ', 'Consent: Provision of written informed consent prior to enrollment.'), ] for n, t in inc: bullet(n, t) doc.add_paragraph() para('5.2.2. Exclusion Criteria', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) exc = [ ('1. ', 'Emergency Surgery: Patients undergoing emergency (non-elective) abdominal surgery in ' 'whom preoperative 6MWT cannot be safely performed.'), ('2. ', 'Inability to Walk: Patients who are non-ambulatory preoperatively or have neurological, ' 'orthopedic, or vascular conditions that independently preclude safe completion of a 6MWT ' '(e.g., severe lower-limb ischemia, active musculoskeletal injury, hemiplegia).'), ('3. ', 'Hemodynamic Instability: Patients with resting hemodynamic instability or oxygen ' 'saturation <88% on room air at rest, in whom exercise testing is contraindicated.'), ('4. ', 'Severe Cardiorespiratory Disease: Patients with known unstable angina, acute ' 'decompensated heart failure, or acute exacerbation of COPD within four weeks prior to surgery.'), ('5. ', 'Prior Major Abdominal Surgery in the Same Admission: Patients undergoing re-operation ' 'during the same index admission (to avoid confounding of outcomes).'), ('6. ', 'Data Incompleteness: Patients for whom key baseline or outcome data cannot be obtained.'), ] for n, t in exc: bullet(n, t) doc.add_paragraph() # 5.3 Operational Definitions Table sub_h('5.3.', 'Operational Definitions, if applicable') tbl2 = doc.add_table(rows=1, cols=2) tbl2.style = 'Table Grid' tbl2.columns[0].width = Inches(2.0) tbl2.columns[1].width = Inches(4.0) hdr2 = tbl2.rows[0].cells hdr2[0].text = 'Variable'; hdr2[1].text = 'Definition' for c in hdr2: for r in c.paragraphs[0].runs: r.bold = True; r.font.size = Pt(11); r.font.name = 'Times New Roman' op_defs = [ ('Major Abdominal Surgery', 'Any intraperitoneal operative procedure (open or laparoscopic) anticipated to last ≥60 minutes ' 'under general or regional anesthesia, including colorectal resection, gastrectomy, hepatobiliary ' 'surgery, pancreatectomy, and small-bowel resection.'), ('Six-Minute Walk Test (6MWT)', 'A standardized submaximal exercise test in which the patient walks as far as possible on a flat, ' '30-meter corridor for six minutes. Administered per ATS 2002 guidelines. The primary metric is ' 'the six-minute walk distance (6MWD) in meters.'), ('Six-Minute Walk Distance (6MWD)', 'Total distance walked (in meters) during the 6MWT. A 6MWD below the study-defined cut-off ' '(determined by ROC analysis) will classify the patient as having "reduced functional capacity."'), ('Postoperative Pulmonary Complication (PPC)', 'Any of the following occurring within 30 days of surgery: (a) pneumonia — new pulmonary ' 'infiltrate with fever, leukocytosis, and purulent secretions; (b) respiratory failure — SpO2 ' '<90% on room air or requirement for mechanical ventilation beyond 24 hours postoperatively; ' '(c) atelectasis — radiologically confirmed requiring physiotherapy or bronchoscopy; ' '(d) pleural effusion — requiring drainage; (e) bronchospasm — requiring bronchodilator ' 'treatment. Defined per StEP-COMPAC consensus.'), ('Major Postoperative Complication', 'Any postoperative complication graded Clavien-Dindo grade II or higher occurring within ' '30 days of surgery.'), ('Clavien-Dindo Grade II or Higher', 'Grade II: Complication requiring pharmacological treatment (e.g., antibiotics, ' 'anti-arrhythmics). Grade III: Requiring surgical, endoscopic, or radiological intervention. ' 'Grade IV: Life-threatening complication requiring ICU management. Grade V: Death.'), ('Unplanned ICU Admission', 'Transfer to the ICU at any point following the initial return from the operating theater, ' 'not as part of the planned postoperative pathway.'), ('Mechanical Ventilation', 'Initiation of invasive mechanical ventilation via endotracheal tube or tracheostomy beyond ' 'the immediate post-anesthetic recovery period (>24 hours postoperatively).'), ('Non-Invasive Ventilation', 'Initiation of non-invasive positive pressure ventilation (CPAP, BiPAP) or high-flow nasal ' 'cannula (HFNC) beyond the immediate post-anesthetic recovery period.'), ('Prolonged Hospitalization', 'Total hospital length of stay exceeding the 75th percentile for the specific procedure type, ' 'or a stay of ≥14 days (whichever is defined a priori).'), ('In-Hospital Mortality', 'Death from any cause occurring during the index hospital admission.'), ('Reduced Functional Capacity', 'Preoperative 6MWD below the study-defined ROC-derived cut-off value, or a 6MWD of <400 m ' 'based on existing literature thresholds.'), ] for var, defn in op_defs: row = tbl2.add_row() row.cells[0].text = var; row.cells[1].text = defn for ci in [0, 1]: for r in row.cells[ci].paragraphs[0].runs: r.font.size = Pt(11); r.font.name = 'Times New Roman' para('Table 1: Operational Definitions', italic=True, size=11, align=WD_ALIGN_PARAGRAPH.CENTER, sb=4, sa=8) # 5.4 sub_h('5.4.', 'Description of Study Procedure') para('5.4.1. For observational (prospective cohort) studies:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=4) para('5.4.1.1. Method of Subject Selection', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'Subjects will be identified prospectively from the surgical schedule of the institution. ' 'All adult patients listed for elective or semi-elective major abdominal surgery will be ' 'screened by the study team during the preoperative assessment visit, typically occurring ' '2–7 days prior to the scheduled operation. Eligible patients meeting inclusion criteria and ' 'providing written informed consent will be enrolled consecutively until the target sample ' 'size is achieved.', sa=6 ) para('5.4.1.2. Data to Be Gathered', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para('The following data points will be systematically collected using a standardized case ' 'report form:', sa=4) data_items = [ ('Demographics: ', 'Age, sex, height, weight, body mass index (BMI).'), ('Clinical Characteristics: ', 'Comorbidities (e.g., COPD, diabetes mellitus, hypertension, ' 'ischemic heart disease, heart failure, chronic kidney disease, cerebrovascular disease), ' 'smoking status, ASA Physical Status classification, ARISCAT score.'), ('Exposure Variable: ', '6MWD (meters), resting and post-test heart rate, oxygen saturation ' '(SpO2), Borg dyspnea scale, and reason for test termination (if applicable).'), ('Surgical Data: ', 'Type of procedure, operative approach (open vs. laparoscopic/robotic), ' 'estimated blood loss, operative duration, type of anesthesia.'), ('Postoperative Outcome Data (primary): ', 'Occurrence of any PPC within 30 days — pneumonia, ' 'respiratory failure, atelectasis, pleural effusion, bronchospasm — (Yes/No with date).'), ('Postoperative Outcome Data (secondary): ', 'Clavien-Dindo grade of all complications, ' 'unplanned ICU admission, need for mechanical or non-invasive ventilation, length of hospital ' 'stay (days), in-hospital mortality.'), ] for lbl, txt in data_items: dot(lbl, txt) doc.add_paragraph() para('5.4.1.3. Description of Procedures to Be Done to Subjects', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'Each enrolled participant will undergo one standardized preoperative 6MWT performed by a ' 'trained research physiotherapist or nurse, within 2–7 days prior to surgery, following ATS ' '(2002) guidelines. The test will be conducted on a flat, indoor, 30-meter corridor, clearly ' 'marked at each end. Standardized verbal encouragement will be provided at one-minute intervals. ' 'Participants will be instructed to walk as fast as safely possible for six minutes, and are ' 'permitted to slow down or stop if needed. Resting SpO2, heart rate, and Borg dyspnea scale ' 'will be recorded immediately before and after the test. The test will be terminated early if ' 'any safety criterion is met (SpO2 <85%, chest pain, acute dyspnea, leg cramps, or patient ' 'request). No other study-specific procedures will be performed; all postoperative data will ' 'be obtained through prospective chart review and clinical follow-up.', sa=6 ) para('5.4.1.4. Instruments Used for Measuring Exposure and/or Outcome', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) instr = [ ('Exposure (6MWT): ', 'A measured 30-meter corridor, pulse oximeter, stopwatch, and Borg ' 'dyspnea scale. Distance will be recorded in meters to the nearest meter.'), ('Outcome Assessment (PPCs): ', 'Clinical, radiologic, and laboratory findings documented in ' 'the official patient chart by the attending surgical and medical team, adjudicated against the ' 'StEP-COMPAC consensus PPC definition.'), ('Outcome Assessment (Clavien-Dindo Grade): ', 'Classified by the principal investigator based ' 'on chart review of all postoperative events, using the standard Clavien-Dindo classification.'), ('Data Abstraction: ', 'A standardized Case Report Form (CRF) will serve as the primary ' 'instrument for uniform data collection.'), ('Risk Scores: ', 'ASA classification assigned by the attending anesthesiologist; ARISCAT score ' 'computed from seven preoperative variables at time of enrollment.'), ] for lbl, txt in instr: dot(lbl, txt) doc.add_paragraph() para('5.4.1.5. Method of Validating Measuring Instruments', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'The 6MWT will be administered according to the standardized ATS 2002 protocol, ensuring ' 'reproducibility and comparability with published normative and prognostic data. Research ' 'personnel performing the test will undergo a standardized training session prior to study ' 'commencement to ensure protocol fidelity. Pulse oximeters will be calibrated and validated ' 'per hospital biomedical engineering standards. Postoperative outcome adjudication will be ' 'performed by two independent investigators blinded to the 6MWD results, with discrepancies ' 'resolved by consensus.', sa=6 ) para('5.4.1.6. Laboratory Procedures to Be Performed, if any', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'No study-specific laboratory procedures will be performed beyond routine preoperative workup. ' 'Preoperative spirometry results (FEV1, FVC, FEV1/FVC) will be recorded if available as part ' 'of standard preoperative care. Routine admission laboratory values (complete blood count, serum ' 'albumin, serum creatinine) will be extracted from the medical record.', sa=6 ) para('5.4.1.7. Follow-Up Procedures', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'All enrolled patients will be followed prospectively from the date of surgery. Research ' 'personnel will perform daily postoperative chart reviews while the patient is hospitalized, ' 'and a structured clinical review at postoperative day 30 (or at hospital discharge, whichever ' 'is later) to ascertain all outcome events. Patients discharged before day 30 will be contacted ' 'by telephone and/or reviewed at their scheduled outpatient follow-up appointment for 30-day ' 'outcome data.', sa=8 ) # 5.5 sub_h('5.5.', 'Description of Outcome Measures') para( 'The primary outcome measure is the occurrence of any postoperative pulmonary complication ' '(PPC) within 30 days of surgery, defined per the StEP-COMPAC consensus framework. The ' 'rationale for selecting PPCs as the primary outcome is their high clinical relevance, direct ' 'link to impaired preoperative cardiorespiratory reserve, and consistent use as the primary ' 'outcome across the existing 6MWT perioperative literature [4,5,7,8].', sa=6 ) para( 'A co-primary outcome is the occurrence of any major postoperative complication (Clavien-Dindo ' 'grade II or higher) within 30 days of surgery. This broader composite outcome captures major ' 'systemic complications beyond the pulmonary domain and is consistent with grading systems used ' 'in comparable surgical outcome studies [6].', sa=6 ) para( 'Secondary outcome measures include: length of hospital stay (continuous, in days), unplanned ' 'ICU admission (dichotomous — Yes/No), need for invasive mechanical ventilation beyond 24 hours ' 'postoperatively (dichotomous), need for non-invasive ventilation or high-flow nasal cannula ' 'beyond the immediate recovery period (dichotomous), and in-hospital mortality (dichotomous). ' 'These secondary outcomes were selected based on their clinical relevance, their established ' 'association with reduced preoperative functional capacity, and their consistent use in ' 'comparable cohort studies and systematic reviews [3,4,5,8].', sa=8 ) # 5.6 sub_h('5.6.', 'Sample Size Estimation') para( 'Sample size was estimated based on the hypothesis that a lower preoperative 6MWD is ' 'independently associated with a higher rate of PPCs. Using data from Soares and Nucci (2021) ' 'as the primary reference — which reported a PPC incidence of 30% in patients with 6MWD ≥400 m ' 'and 65% in those with 6MWD <400 m — with a two-sided alpha of 0.05, power of 80%, and an ' 'anticipated 1:1 ratio of exposed (low 6MWD) to unexposed (normal 6MWD) participants, the ' 'initial sample size is calculated at approximately 38 patients per group (76 total). To account ' 'for multivariable adjustment of up to 6 covariates (at 10 events per variable) and an estimated ' '15% loss to follow-up or incomplete data, the final target sample size is 120 participants. ' 'Sample size was computed using standard logistic regression sample size formulae.', sa=8 ) # 5.7 sub_h('5.7.', 'Data Analysis') para( 'Statistical analyses will be performed using SPSS version 29.0 or R (version 4.3 or higher). ' 'Descriptive statistics will summarize baseline clinical and functional characteristics. ' 'Categorical variables will be reported as frequencies and percentages; continuous variables ' 'will be reported as mean ± standard deviation or median (interquartile range) depending on ' 'distribution. Univariable comparisons between patients who develop PPCs and those who do not ' 'will use the Chi-squared test or Fisher\'s exact test for categorical variables and the ' 'independent-samples t-test or Mann-Whitney U test for continuous variables, as appropriate.', sa=6 ) para( 'The primary hypothesis will be tested using multivariable binary logistic regression, with PPC ' 'occurrence as the dependent variable and preoperative 6MWD as the primary independent variable, ' 'adjusting for clinically relevant covariates identified a priori (age, sex, BMI, ASA class, ' 'operative duration, and procedure type). Results will be expressed as odds ratios (OR) with ' '95% confidence intervals (CI).', sa=6 ) para( 'Receiver Operating Characteristic (ROC) curve analysis will be used to determine the optimal ' '6MWD cut-off value for predicting PPCs and major postoperative complications, with the Youden ' 'index used to identify the threshold that maximizes sensitivity and specificity. The area under ' 'the ROC curve (AUROC) will be calculated for the 6MWT, ASA classification, and ARISCAT score, ' 'with pairwise AUROC comparisons performed to assess the discriminative performance of the 6MWT ' 'relative to established risk tools. The level of significance is set at α = 0.05 (two-sided).', sa=8 ) # 5.8 sub_h('5.8.', 'Ethical Consideration') para( 'This prospective study may enroll participants spanning a range of age groups, including elderly ' 'patients, and may include individuals with significant comorbidities. Given that the 6MWT is a ' 'validated, widely used clinical assessment with an established safety profile, and that no ' 'additional invasive procedures will be performed beyond routine preoperative care, the risk to ' 'participants is considered minimal. Strict confidentiality of all participant data will be ' 'maintained. All study records will be coded and de-identified, and investigators are responsible ' 'for the accuracy, completeness, and integrity of all collected data.', sa=6 ) para('5.8.1. Method/s of Dealing with Adverse Events', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'While the 6MWT is associated with very low risk in supervised settings, the test will be ' 'immediately terminated if the participant develops chest pain, severe dyspnea, dizziness, ' 'leg cramps, pallor, or SpO2 <85%. A qualified clinician will be available during all test ' 'sessions and emergency protocols will be in place. Any adverse event occurring during the ' '6MWT will be documented, reported to the Principal Investigator, and managed per institutional ' 'protocols. Serious adverse events will be reported to the IERC within the required timeframe.', sa=6 ) para('5.8.2. Anticipated Risks and Discomforts to Subjects', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'The anticipated physical risk to participants is minimal, limited primarily to the physiologic ' 'exertion of walking at a self-selected pace for six minutes, which is analogous to activities ' 'of daily living. Potential discomforts include transient breathlessness and fatigue, which are ' 'expected and self-limiting. Pre-test safety screening will exclude participants for whom ' 'exercise testing is contraindicated.', sa=6 ) para('5.8.3. Expected Benefits to the Subject and to Others', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para( 'The results of this study are expected to establish whether the preoperative 6MWT independently ' 'predicts PPCs and major complications following major abdominal surgery. If validated, the 6MWT ' 'could provide clinicians with a simple, immediately implementable preoperative risk ' 'stratification tool that enables targeted preventive interventions (prehabilitation, ' 'physiotherapy, anesthesia optimization) in high-risk patients. The study will generate locally ' 'relevant evidence applicable to Filipino patients and similar resource-limited surgical settings.', sa=6 ) para('5.8.4. Protection of Confidentiality', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) para('The following procedures will be strictly adhered to:', sa=4) conf_items = [ 'A. The study shall abide by the Principles of the Declaration of Helsinki (2013) and will be ' 'conducted in accordance with the Guidelines of the International Conference on Harmonization – ' 'Good Clinical Practice (ICH-GCP).', 'B. The Clinical Protocol and all relevant documents shall be reviewed and approved by the SLMC ' 'Institutional Ethics Review Committee (IERC) prior to enrollment of any participant.', 'C. Patient confidentiality shall be maintained by assigning each participant a unique study code. ' 'No identifying information (name, birth date, hospital number) will be included in any study ' 'database or report.', 'D. Data Storage and Security: All study data will be stored in a password-protected, encrypted ' 'file accessible only to the Principal Investigator and Co-Investigators. All study documents ' 'will be retained by the Principal Investigator for a minimum of 5 years after study completion, ' 'after which they will be securely destroyed.', 'E. Inspection of Records: Access to study records is restricted to the Principal Investigator, ' 'Co-Investigators, and duly authorized representatives of the IERC or institutional audit bodies.', 'F. Results will be disseminated only in aggregate, de-identified form through peer-reviewed ' 'publication or scientific conference presentation.', ] for item in conf_items: p = doc.add_paragraph() p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.35) r = p.add_run(item); set_font(r) doc.add_paragraph() # ════════════════════════════════════════════════════════════════════════════ # SECTION 7 – REFERENCES (continuous [1]–[14]) # ════════════════════════════════════════════════════════════════════════════ sec_h('7', 'References') all_refs = [ ('[1]', 'Smetana GW, Lawrence VA, Cornell JE; American College of Physicians. Preoperative ' 'pulmonary risk stratification for noncardiothoracic surgery: systematic review for ' 'the American College of Physicians. Ann Intern Med. 2006 Apr 18;144(8):581-95. ' 'doi: 10.7326/0003-4819-144-8-200604180-00009.'), ('[2]', 'Garg S, Govindaraj V, Dwivedi DP, Raja K, Theerthar EP. Postoperative pulmonary ' 'complications in patients undergoing upper abdominal surgery: risk factors and ' 'predictive models. Monaldi Arch Chest Dis. 2025 Mar 31. ' 'doi: 10.4081/monaldi.2024.2915. PMID: 38526466.'), ('[3]', 'Rose GA, Davies RG, Appadurai IR, Williams IM, Bashir M, Berg RMG. \'Fit for ' 'surgery\': the relationship between cardiorespiratory fitness and postoperative ' 'outcomes. Exp Physiol. 2022 Aug;107(8):780-95. doi: 10.1113/EP090156. PMID: 35579479.'), ('[4]', 'Soares SMTP, Nucci LB. Association between early pulmonary complications after ' 'abdominal surgery and preoperative physical capacity. Physiother Theory Pract. 2021 ' 'Jul;37(7):852-9. doi: 10.1080/09593985.2019.1650404. PMID: 31402737.'), ('[5]', 'Magalhaes CBA, Nogueira IC, Marinho LS, Daher EF, Garcia JHP, Viana CFG. Exercise ' 'capacity impairment can predict postoperative pulmonary complications after liver ' 'transplantation. Respiration. 2017;94(6):538-44. doi: 10.1159/000479008. ' 'PMID: 28738386.'), ('[6]', 'Inoue T, Ito S, Kanda M, Niwa Y, Nagaya M, Nishida Y. Preoperative six-minute walk ' 'distance as a predictor of postoperative complication in patients with esophageal ' 'cancer. Dis Esophagus. 2020 Mar 5;33(3):doz050. doi: 10.1093/dote/doz050. ' 'PMID: 31111872.'), ('[7]', 'Hattori K, Matsuda T, Takagi Y, Nagaya M, Inoue T, Nishida Y. Preoperative ' 'six-minute walk distance is associated with pneumonia after lung resection. ' 'Interact Cardiovasc Thorac Surg. 2018 Feb 1;26(2):208-13. ' 'doi: 10.1093/icvts/ivx310. PMID: 29049742.'), ('[8]', 'Makker PGS, Koh CE, Solomon MJ, Steffens D. Preoperative functional capacity and ' 'postoperative outcomes following abdominal and pelvic cancer surgery: a systematic ' 'review and meta-analysis. ANZ J Surg. 2022 Jul;92(7-8):1732-40. ' 'doi: 10.1111/ans.17577. PMID: 35253333.'), ('[9]', 'Argillander TE, Heil TC, Melis RJF, van Duijvendijk P, Klaase JM, van Munster BC. ' 'Preoperative physical performance as predictor of postoperative outcomes in patients ' 'aged 65 and older scheduled for major abdominal cancer surgery: a systematic review. ' 'Eur J Surg Oncol. 2022 Mar;48(3):575-84. doi: 10.1016/j.ejso.2021.09.019. ' 'PMID: 34629224.'), ('[10]', 'STARSurg Collaborative and TASMAN Collaborative. Evaluation of prognostic risk models ' 'for postoperative pulmonary complications in adult patients undergoing major abdominal ' 'surgery: a systematic review and international external validation cohort study. ' 'Lancet Digit Health. 2022 Jul;4(7):e498-e507. ' 'doi: 10.1016/S2589-7500(22)00069-3. PMID: 35750401.'), ('[11]', 'Boden I, Reeve J, Jernas A, Denehy L, Fagevik Olsen M. Preoperative physiotherapy ' 'prevents postoperative pulmonary complications after major abdominal surgery: a ' 'meta-analysis of individual patient data. J Physiother. 2024 Jul;70(3):195-202. ' 'doi: 10.1016/j.jphys.2024.02.012. PMID: 38472053.'), ('[12]', 'Dankert A, Dohrmann T, Loser B, Zapf A, Zollner C, Petzoldt M. Pulmonary function ' 'tests for the prediction of postoperative pulmonary complications. Dtsch Arztebl Int. ' '2022 Feb 18;119(7):99-106. doi: 10.3238/arztebl.m2022.0074. PMID: 34939921.'), ('[13]', 'Otadi K, Malmir K. Normative reference values for the six-minute walk test in older ' 'adults: a systematic review and meta-analysis. Arch Gerontol Geriatr. 2026 ' 'Jan;128:106026. doi: 10.1016/j.archger.2025.106026. PMID: 41027267.'), ('[14]', 'Yeung MT, Chan MY, Huang KS, Chen TJ, Chia CP, Fong MM. Normative reference values ' 'and regression equations to predict the 6-minute walk distance in the Asian adult ' 'population aged 21-80 years. Hong Kong Physiother J. 2022 Dec;42(2):69-78. ' 'doi: 10.1142/S1013702522500111. PMID: 37560171.'), ] for num, txt in all_refs: ref_entry(num, txt) # Footer doc.add_paragraph() fp = doc.add_paragraph() fp.alignment = WD_ALIGN_PARAGRAPH.RIGHT fp.paragraph_format.space_before = Pt(24) r = fp.add_run('Protocol Version 01 dated ________________') set_font(r, size=10) out = '/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx' doc.save(out) print(f'Saved: {out}') PYEOF python3 /tmp/workspace/6mwt-proposal/build_complete.py

python3 -c " from docx import Document doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx') # Quick structural check sections_found = [] for p in doc.paragraphs: t = p.text.strip() if t and any(t.startswith(x) for x in ['2.','3.','3.1','3.2','3.3','4.','4.1','4.2','5.','5.1','5.2','5.3','5.4','5.5','5.6','5.7','5.8','7.']): sections_found.append(t[:80]) for s in sections_found: print(s) print() print(f'Total paragraphs: {len(doc.paragraphs)}') print(f'Total tables: {len(doc.tables)}') "

python3 -c " from docx import Document doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx') # Find rationale paragraphs in_rationale = False for i, p in enumerate(doc.paragraphs): t = p.text.strip() if '3.2.' in t and 'Rationale' in t: in_rationale = True if in_rationale and '3.3.' in t: break if in_rationale and t: print(f'{i}: {t[:120]}') "

python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.oxml.ns import qn import copy doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx') body = doc.element.body def set_font(run, bold=False, size=12, name='Times New Roman'): run.font.name = name; run.font.size = Pt(size); run.bold = bold def make_para(text, bold=False, sa=6, li=0, align=WD_ALIGN_PARAGRAPH.JUSTIFY): from docx.oxml import OxmlElement p = doc.add_paragraph() p.alignment = align p.paragraph_format.space_before = Pt(0) p.paragraph_format.space_after = Pt(sa) p.paragraph_format.left_indent = Inches(li) r = p.add_run(text); set_font(r, bold=bold) return p # ── Identify the paragraph elements to replace ─────────────────────────────── # Rationale heading is para 25 (index), rationale body is paras 26-36 # We will DELETE paras 26-36 from XML and insert 4 new ones after para 25 all_body_children = list(body) # Collect all <w:p> elements in document order all_p_elems = [c for c in body if c.tag.endswith('}p') or c.tag == 'p'] # python-docx paragraphs map 1:1 to <w:p> in body (ignoring table cells) # We need para indices 26-36 (0-indexed) in doc.paragraphs -> map to XML elems # Build a list of top-level <w:p> elements top_level_paras = [child for child in body if child.tag == '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}p'] # Identify which top-level paras correspond to doc.paragraphs[26..36] # doc.paragraphs includes ALL paragraphs including those in tables # We need to find the ones matching by text target_texts = [ 'Timely and accurate preoperative risk stratification remains', 'Functional capacity \u2014 defined as the ability', 'The 6MWT provides an objective, standardized measure', 'Several studies have examined the relationship between preoperative 6MWD', 'Extending this evidence to other abdominal organ surgeries', 'In the oncologic setting, Inoue et al.', 'At the level of systematic evidence, Makker et al.', 'Argillander et al. (2022) conducted a systematic review', 'In a recent study by Garg et al.', 'The 6MWT uniquely serves as a composite indicator', 'Locally, there is a significant evidence gap', ] to_delete = [] for child in body: if child.tag == '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}p': txt = ''.join( n.text or '' for n in child.iter( '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}t') ) for tgt in target_texts: if txt.strip().startswith(tgt[:40]): to_delete.append(child) break print(f'Paragraphs to delete: {len(to_delete)}') for el in to_delete: txt = ''.join(n.text or '' for n in el.iter('{http://schemas.openxmlformats.org/wordprocessingml/2006/main}t')) print(' -', txt[:80]) # Find the rationale heading element to insert after it heading_elem = None for child in body: if child.tag == '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}p': txt = ''.join( n.text or '' for n in child.iter( '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}t') ) if txt.strip().startswith('3.2.') and 'Rationale' in txt: heading_elem = child break print(f'\nHeading found: {heading_elem is not None}') PYEOF

python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches from docx.enum.text import WD_ALIGN_PARAGRAPH from lxml import etree import copy WNS = '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}' doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx') body = doc.element.body # ── New, condensed rationale paragraphs ────────────────────────────────────── new_texts = [ ( 'Timely and accurate risk stratification before major abdominal surgery remains ' 'a persistent clinical challenge. Existing preoperative risk scores — including the ' 'ARISCAT score, ASA Physical Status classification, and spirometry-based indices — ' 'demonstrate only moderate discriminative accuracy for PPCs, and none directly captures ' 'a patient\'s functional reserve. Cardiopulmonary exercise testing (CPET), while the ' 'gold standard for objective functional assessment, is resource-intensive and unavailable ' 'in most Philippine surgical centers. This gap highlights the need for a simple, ' 'objective, and locally feasible preoperative risk stratification tool [1,2,3].' ), ( 'The Six-Minute Walk Test (6MWT) addresses this need directly. It is a non-invasive, ' 'inexpensive, standardized submaximal exercise test that quantifies functional exercise ' 'capacity through the six-minute walk distance (6MWD). Unlike subjective MET estimation, ' 'the 6MWT yields an objective measure of the integrated cardiorespiratory, neuromuscular, ' 'and metabolic response to exercise. It also reflects nutritional status, muscle reserve, ' 'and physical conditioning — independent determinants of surgical resilience. These ' 'properties make the 6MWT uniquely suited as a composite preoperative risk marker, ' 'particularly in resource-limited settings [3,13,14].' ), ( 'Existing evidence supports the association between reduced preoperative 6MWD and adverse ' 'postoperative outcomes. Studies in abdominal, esophageal, and liver transplant surgery ' 'have consistently demonstrated that lower 6MWD independently predicts PPCs and major ' 'complications, with cut-off values in the range of 400–454 m showing clinically ' 'meaningful sensitivity and specificity [4,5,6,7,8]. Despite this, no prospective study ' 'has specifically evaluated the 6MWT as a preoperative predictor in a broad, ' 'non-oncologic major abdominal surgery population using standardized protocols and ' 'consensus outcome definitions.' ), ( 'In the local context, there are no published studies evaluating the preoperative 6MWT ' 'as a predictor of surgical complications in Filipino patients. Given the high burden ' 'of major abdominal surgical disease, resource constraints limiting access to CPET, ' 'and the immediate practicability of the 6MWT in any hospital corridor, demonstrating ' 'its prognostic value in this population would provide clinicians with an immediately ' 'implementable, low-cost risk stratification tool to guide perioperative decision-making ' 'and targeted preventive interventions.' ), ] def make_p_xml(text, doc_obj): """Create a <w:p> XML element with proper formatting to match document style.""" from docx.oxml import OxmlElement p = OxmlElement('w:p') # paragraph properties pPr = OxmlElement('w:pPr') jc = OxmlElement('w:jc'); jc.set(f'{WNS}val', 'both'); pPr.append(jc) spacing = OxmlElement('w:spacing') spacing.set(f'{WNS}before', '0') spacing.set(f'{WNS}after', '120') # 6pt in twips (6*20=120) pPr.append(spacing) p.append(pPr) # run r = OxmlElement('w:r') rPr = OxmlElement('w:rPr') rFonts = OxmlElement('w:rFonts') rFonts.set(f'{WNS}ascii', 'Times New Roman') rFonts.set(f'{WNS}hAnsi', 'Times New Roman') rPr.append(rFonts) sz = OxmlElement('w:sz'); sz.set(f'{WNS}val', '24') # 12pt = 24 half-points szCs = OxmlElement('w:szCs'); szCs.set(f'{WNS}val', '24') rPr.append(sz); rPr.append(szCs) r.append(rPr) t = OxmlElement('w:t') t.text = text t.set('{http://www.w3.org/XML/1998/namespace}space', 'preserve') r.append(t) p.append(r) return p # ── Find heading element and elements to delete ────────────────────────────── target_starts = [ 'Timely and accurate preoperative risk stratification remains', 'Functional capacity \u2014 defined as the ability', 'The 6MWT provides an objective, standardized measure', 'Several studies have examined the relationship between preoperative 6MWD', 'Extending this evidence to other abdominal organ surgeries', 'In the oncologic setting, Inoue et al.', 'At the level of systematic evidence, Makker et al.', 'Argillander et al. (2022) conducted a systematic review', 'In a recent study by Garg et al.', 'The 6MWT uniquely serves as a composite indicator', 'Locally, there is a significant evidence gap', ] heading_elem = None to_delete = [] for child in list(body): if child.tag == f'{WNS}p': txt = ''.join(n.text or '' for n in child.iter(f'{WNS}t')).strip() if txt.startswith('3.2.') and 'Rationale' in txt: heading_elem = child for tgt in target_starts: if txt.startswith(tgt[:45]): to_delete.append(child) break print(f'Heading found: {heading_elem is not None}') print(f'Paragraphs to delete: {len(to_delete)}') # ── Delete old rationale paragraphs ────────────────────────────────────────── for el in to_delete: body.remove(el) # ── Insert new condensed paragraphs after heading ──────────────────────────── insert_after = heading_elem for txt in new_texts: new_p = make_p_xml(txt, doc) insert_after.addnext(new_p) insert_after = new_p # keep insertion order # ── Save ────────────────────────────────────────────────────────────────────── out = '/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx' doc.save(out) print(f'Saved: {out}') # ── Verify ──────────────────────────────────────────────────────────────────── doc2 = Document(out) in_rat = False for p in doc2.paragraphs: t = p.text.strip() if '3.2.' in t and 'Rationale' in t: in_rat = True if in_rat and '3.3.' in t: break if in_rat and t: print(f' >> {t[:100]}') PYEOF

python3 -c " import math # Base sample size (from logistic regression formula) # PPC rate low-6MWD group: 65%, normal-6MWD group: 30% # alpha=0.05 two-sided, power=80%, 1:1 ratio # Using standard formula for two proportions p1 = 0.65 # exposed (low 6MWD) p2 = 0.30 # unexposed (normal 6MWD) alpha = 0.05 power = 0.80 z_alpha = 1.96 # two-sided alpha 0.05 z_beta = 0.842 # power 0.80 p_bar = (p1 + p2) / 2 n_per_group = (z_alpha * math.sqrt(2 * p_bar * (1 - p_bar)) + z_beta * math.sqrt(p1*(1-p1) + p2*(1-p2)))**2 / (p1-p2)**2 n_per_group_ceil = math.ceil(n_per_group) n_total = n_per_group_ceil * 2 print(f'Base n per group: {n_per_group:.1f} -> {n_per_group_ceil}') print(f'Base total (2 groups): {n_total}') # Covariate adjustment: +10 per covariate (EPV=10), 6 covariates # Conservative approach: 10 events per variable, primary outcome ~47.5% average rate # Or simpler: add 10 subjects per covariate (standard rule of thumb) n_covariates = 6 n_adjusted = n_total + (n_covariates * 10) print(f'After covariate adjustment (+{n_covariates*10}): {n_adjusted}') # 5% loss to follow-up ltfu_5 = math.ceil(n_adjusted / (1 - 0.05)) print(f'With 5% loss to follow-up: {ltfu_5}') # 15% loss to follow-up (original) ltfu_15 = math.ceil(n_adjusted / (1 - 0.15)) print(f'With 15% loss to follow-up (original): {ltfu_15}') "

cat << 'PYEOF' > /tmp/workspace/6mwt-proposal/build_pptx.py from pptx import Presentation from pptx.util import Inches, Pt, Emu from pptx.dml.color import RGBColor from pptx.enum.text import PP_ALIGN from pptx.util import Inches, Pt import copy # ── Colour palette (medical / academic) ────────────────────────────────────── NAVY = RGBColor(0x1A, 0x35, 0x5E) # deep navy – headings/accents TEAL = RGBColor(0x0B, 0x7B, 0x8A) # teal – subheadings / highlights LGRAY = RGBColor(0xF2, 0xF4, 0xF7) # light grey background panels WHITE = RGBColor(0xFF, 0xFF, 0xFF) BLACK = RGBColor(0x1A, 0x1A, 0x1A) GOLD = RGBColor(0xD4, 0xA0, 0x17) # accent prs = Presentation() prs.slide_width = Inches(13.33) prs.slide_height = Inches(7.5) blank_layout = prs.slide_layouts[6] # blank # ── Helper functions ────────────────────────────────────────────────────────── def add_rect(slide, l, t, w, h, fill_rgb=None, line_rgb=None, line_width=Pt(0)): from pptx.util import Emu shape = slide.shapes.add_shape(1, Inches(l), Inches(t), Inches(w), Inches(h)) shape.line.width = line_width if fill_rgb: shape.fill.solid(); shape.fill.fore_color.rgb = fill_rgb else: shape.fill.background() if line_rgb: shape.line.color.rgb = line_rgb else: shape.line.fill.background() return shape def add_text_box(slide, text, l, t, w, h, font_name='Calibri', font_size=16, bold=False, italic=False, color=BLACK, align=PP_ALIGN.LEFT, wrap=True, word_wrap=True, auto_fit=False): txBox = slide.shapes.add_textbox(Inches(l), Inches(t), Inches(w), Inches(h)) txBox.word_wrap = word_wrap tf = txBox.text_frame tf.word_wrap = wrap p = tf.paragraphs[0] p.alignment = align run = p.add_run() run.text = text run.font.name = font_name run.font.size = Pt(font_size) run.font.bold = bold run.font.italic = italic run.font.color.rgb = color return txBox def add_tf_box(slide, l, t, w, h, wrap=True): """Return a textbox's text_frame for multi-paragraph content.""" txBox = slide.shapes.add_textbox(Inches(l), Inches(t), Inches(w), Inches(h)) txBox.word_wrap = wrap return txBox.text_frame def tf_add_para(tf, text, font_name='Calibri', size=14, bold=False, italic=False, color=BLACK, align=PP_ALIGN.LEFT, space_before=Pt(0), space_after=Pt(4), bullet=False): from pptx.oxml.ns import qn from lxml import etree p = tf.add_paragraph() p.alignment = align p.space_before = space_before p.space_after = space_after if bullet: pPr = p._p.get_or_add_pPr() buChar = etree.SubElement(pPr, qn('a:buChar')) buChar.set('char', u'\u2022') run = p.add_run() run.text = text run.font.name = font_name run.font.size = Pt(size) run.font.bold = bold run.font.italic = italic run.font.color.rgb = color return p def header_bar(slide, title, subtitle=None): """Dark navy top bar with title.""" add_rect(slide, 0, 0, 13.33, 1.2, fill_rgb=NAVY) add_rect(slide, 0, 1.2, 13.33, 0.07, fill_rgb=TEAL) add_text_box(slide, title, 0.35, 0.08, 12.6, 0.75, font_name='Calibri', font_size=28, bold=True, color=WHITE) if subtitle: add_text_box(slide, subtitle, 0.35, 0.78, 12.6, 0.4, font_name='Calibri', font_size=14, italic=True, color=RGBColor(0xCC,0xDD,0xEE)) def slide_number(slide, num, total): add_text_box(slide, f'{num} / {total}', 12.5, 7.1, 0.8, 0.35, font_size=10, color=RGBColor(0xAA,0xAA,0xAA), align=PP_ALIGN.RIGHT) def footer_line(slide, text='St. Luke\'s Medical Center | Institute of Surgery'): add_rect(slide, 0, 7.2, 13.33, 0.3, fill_rgb=NAVY) add_text_box(slide, text, 0.3, 7.22, 12.7, 0.28, font_size=9, color=WHITE, align=PP_ALIGN.LEFT) TOTAL_SLIDES = 12 # ════════════════════════════════════════════════════════════════════════════ # SLIDE 1 – TITLE # ════════════════════════════════════════════════════════════════════════════ s1 = prs.slides.add_slide(blank_layout) add_rect(s1, 0, 0, 13.33, 7.5, fill_rgb=NAVY) add_rect(s1, 0, 0, 13.33, 0.07, fill_rgb=TEAL) add_rect(s1, 0, 7.43, 13.33, 0.07, fill_rgb=TEAL) add_rect(s1, 0.3, 5.6, 12.73, 0.06, fill_rgb=TEAL) add_text_box(s1, 'Prognostic Value of the Six-Minute Walk Test\n' 'in Predicting Postoperative Pulmonary and\n' 'Major Complications Among Adults Undergoing\n' 'Major Abdominal Surgery', 0.5, 0.8, 12.3, 3.2, font_name='Calibri', font_size=32, bold=True, color=WHITE, align=PP_ALIGN.CENTER) add_text_box(s1, 'A Prospective Cohort Study', 0.5, 3.9, 12.3, 0.5, font_name='Calibri', font_size=20, italic=True, color=RGBColor(0xCC,0xDD,0xEE), align=PP_ALIGN.CENTER) add_text_box(s1, 'Department of Surgery / Anesthesiology\nSt. Luke\'s Medical Center', 0.5, 5.8, 12.3, 0.9, font_name='Calibri', font_size=15, color=RGBColor(0xCC,0xDD,0xEE), align=PP_ALIGN.CENTER) add_text_box(s1, 'Clinical Research Proposal | Protocol Version 01', 0.5, 6.9, 12.3, 0.45, font_name='Calibri', font_size=11, color=RGBColor(0x88,0xAA,0xCC), align=PP_ALIGN.CENTER) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 2 – OUTLINE # ════════════════════════════════════════════════════════════════════════════ s2 = prs.slides.add_slide(blank_layout) add_rect(s2, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s2, 'Presentation Outline') footer_line(s2) slide_number(s2, 2, TOTAL_SLIDES) items = [ ('01', 'Background & Significance'), ('02', 'Rationale for the Study'), ('03', 'Research Objectives'), ('04', 'Study Design & Methods'), ('05', 'Inclusion / Exclusion Criteria'), ('06', 'The 6MWT Protocol'), ('07', 'Outcome Measures'), ('08', 'Sample Size & Data Analysis'), ('09', 'Ethical Considerations'), ('10', 'Expected Impact & Conclusion'), ] col_items = [items[:5], items[5:]] for ci, col in enumerate(col_items): xl = 0.6 + ci * 6.4 for ri, (num, label) in enumerate(col): yt = 1.45 + ri * 1.0 add_rect(s2, xl, yt, 0.55, 0.55, fill_rgb=TEAL) add_text_box(s2, num, xl, yt, 0.55, 0.55, font_size=14, bold=True, color=WHITE, align=PP_ALIGN.CENTER) add_text_box(s2, label, xl+0.65, yt+0.05, 5.3, 0.5, font_size=15, bold=False, color=NAVY) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 3 – BACKGROUND & SIGNIFICANCE # ════════════════════════════════════════════════════════════════════════════ s3 = prs.slides.add_slide(blank_layout) add_rect(s3, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s3, 'Background & Significance', 'Major abdominal surgery and postoperative pulmonary complications (PPCs)') footer_line(s3) slide_number(s3, 3, TOTAL_SLIDES) # left panel add_rect(s3, 0.35, 1.45, 5.9, 5.65, fill_rgb=WHITE, line_rgb=RGBColor(0xDD,0xE3,0xEC), line_width=Pt(1)) tf3l = add_tf_box(s3, 0.55, 1.55, 5.55, 5.4) tf_add_para(tf3l, 'The Problem', size=15, bold=True, color=TEAL, space_after=Pt(6)) bullets_l = [ 'Major abdominal surgery: colorectal, hepatobiliary, gastrectomy, pancreatectomy', 'PPC incidence: 9%–40% depending on operative site and patient population', 'PPCs are the leading cause of perioperative morbidity, prolonged ICU stay, and mortality', '11,591-patient international cohort (STARSurg/TASMAN): 7.8% PPC rate by StEP-COMPAC definition', 'Upper abdominal surgery: 20.3% PPC rate (Garg et al., 2025)', ] for b in bullets_l: tf_add_para(tf3l, b, size=12.5, color=BLACK, bullet=True, space_after=Pt(5)) # right panel add_rect(s3, 6.95, 1.45, 6.0, 5.65, fill_rgb=WHITE, line_rgb=RGBColor(0xDD,0xE3,0xEC), line_width=Pt(1)) tf3r = add_tf_box(s3, 7.15, 1.55, 5.7, 5.4) tf_add_para(tf3r, 'The Gap in Risk Stratification', size=15, bold=True, color=TEAL, space_after=Pt(6)) bullets_r = [ 'ARISCAT score: best existing PPC model — AUROC only 0.700', 'ASA classification: subjective; no functional capacity measure', 'Spirometry: inconclusive evidence for non-thoracic surgery', 'CPET (gold standard): requires specialized equipment & trained staff — unavailable in most PH centers', 'Subjective MET estimation: remains standard of care locally', 'Critical gap: no objective, bedside-feasible PPC risk tool exists', ] for b in bullets_r: tf_add_para(tf3r, b, size=12.5, color=BLACK, bullet=True, space_after=Pt(5)) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 4 – RATIONALE # ════════════════════════════════════════════════════════════════════════════ s4 = prs.slides.add_slide(blank_layout) add_rect(s4, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s4, 'Rationale for the Study', 'Why the Six-Minute Walk Test?') footer_line(s4) slide_number(s4, 4, TOTAL_SLIDES) # 4 reason boxes box_data = [ (NAVY, '1', 'Objective Functional Measure', 'The 6MWT provides a standardized, reproducible, objective measure of submaximal ' 'exercise capacity — a direct proxy for cardiorespiratory reserve and surgical resilience.'), (TEAL, '2', 'Simple & Accessible', 'Requires only a 30-meter corridor, stopwatch, and pulse oximeter. No specialized ' 'equipment, no CPET lab. Feasible in any tertiary Philippine hospital.'), (NAVY, '3', 'Composite Risk Signal', 'Low 6MWD reflects not just limited CRF, but also deconditioning, sarcopenia, and ' 'poor nutritional reserve — all independent determinants of adverse surgical outcomes.'), (TEAL, '4', 'Evidence Base', 'Prospective studies show 6MWD <400–454 m independently predicts PPCs and major ' 'complications (OR 0.38–0.59) in abdominal, esophageal, and liver transplant surgery.'), ] positions = [(0.35,1.45),(6.95,1.45),(0.35,4.2),(6.95,4.2)] for (xl, yt), (col, num, ttl, body_txt) in zip(positions, box_data): add_rect(s4, xl, yt, 6.0, 2.55, fill_rgb=WHITE, line_rgb=RGBColor(0xDD,0xE3,0xEC), line_width=Pt(1)) add_rect(s4, xl, yt, 0.55, 2.55, fill_rgb=col) add_text_box(s4, num, xl, yt+0.9, 0.55, 0.7, font_size=22, bold=True, color=WHITE, align=PP_ALIGN.CENTER) add_text_box(s4, ttl, xl+0.65, yt+0.1, 5.2, 0.5, font_size=14, bold=True, color=col) add_text_box(s4, body_txt, xl+0.65, yt+0.6, 5.2, 1.8, font_size=12, color=BLACK) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 5 – OBJECTIVES # ════════════════════════════════════════════════════════════════════════════ s5 = prs.slides.add_slide(blank_layout) add_rect(s5, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s5, 'Research Objectives') footer_line(s5) slide_number(s5, 5, TOTAL_SLIDES) add_text_box(s5, 'GENERAL OBJECTIVE', 0.35, 1.42, 12.6, 0.35, font_size=12, bold=True, color=TEAL) add_rect(s5, 0.35, 1.75, 12.63, 0.88, fill_rgb=RGBColor(0xE8,0xF4,0xF8), line_rgb=TEAL, line_width=Pt(1.5)) add_text_box(s5, 'To determine the prognostic value of preoperative 6MWD in predicting postoperative ' 'pulmonary complications and major postoperative complications among adults undergoing ' 'major abdominal surgery at a tertiary hospital.', 0.55, 1.82, 12.2, 0.75, font_size=13.5, color=NAVY) add_text_box(s5, 'SPECIFIC OBJECTIVES', 0.35, 2.75, 12.6, 0.35, font_size=12, bold=True, color=TEAL) spec_objs = [ '1. Describe baseline clinical and functional characteristics (6MWD, BMI, ASA class, spirometry, comorbidities)', '2. Determine the association between preoperative 6MWD and PPCs within 30 days', '3. Determine the association between preoperative 6MWD and major complications (Clavien-Dindo ≥ II)', '4. Evaluate 6MWD vs. secondary outcomes: ICU admission, ventilator use, LOS, in-hospital mortality', '5. Identify optimal 6MWD cut-off via ROC curve analysis (Youden index)', '6. Compare discriminative accuracy of 6MWT vs. ASA classification and ARISCAT score (AUROC comparison)', ] tf5 = add_tf_box(s5, 0.35, 3.1, 12.63, 4.0) for obj in spec_objs: tf_add_para(tf5, obj, size=12.5, color=BLACK, space_after=Pt(5)) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 6 – STUDY DESIGN & METHODS # ════════════════════════════════════════════════════════════════════════════ s6 = prs.slides.add_slide(blank_layout) add_rect(s6, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s6, 'Study Design & Methods') footer_line(s6) slide_number(s6, 6, TOTAL_SLIDES) # flow diagram boxes flow = [ (0.4, 1.5, 'IDENTIFY\nEligible Patients', 'Surgical schedule\n2–7 days pre-op'), (3.05, 1.5, 'ENROLL\n& Consent', 'Inclusion/exclusion\nscreening'), (5.7, 1.5, 'PERFORM\n6MWT', 'Standardized ATS\nprotocol'), (8.35, 1.5, 'SURGERY', 'Major abdominal\nprocedure'), (11.0, 1.5, 'FOLLOW-UP\n30 Days', 'Outcome\nascertainment'), ] arrow_col = RGBColor(0xB0,0xC4,0xDE) for i, (xl, yt, title, sub) in enumerate(flow): add_rect(s6, xl, yt, 2.4, 1.4, fill_rgb=NAVY if i%2==0 else TEAL) add_text_box(s6, title, xl+0.05, yt+0.08, 2.3, 0.7, font_size=12, bold=True, color=WHITE, align=PP_ALIGN.CENTER) add_text_box(s6, sub, xl+0.05, yt+0.75, 2.3, 0.6, font_size=10, color=RGBColor(0xDD,0xEE,0xFF), align=PP_ALIGN.CENTER) if i < len(flow)-1: add_rect(s6, xl+2.4, yt+0.55, 0.65, 0.3, fill_rgb=arrow_col) add_text_box(s6, u'\u25B6', xl+2.52, yt+0.55, 0.4, 0.3, font_size=14, color=WHITE, align=PP_ALIGN.CENTER) # Details panels add_rect(s6, 0.35, 3.25, 5.9, 3.95, fill_rgb=WHITE, line_rgb=RGBColor(0xDD,0xE3,0xEC), line_width=Pt(1)) tf6l = add_tf_box(s6, 0.55, 3.35, 5.55, 3.75) tf_add_para(tf6l, 'Study Type & Setting', size=14, bold=True, color=NAVY, space_after=Pt(4)) for b in [ 'Design: Prospective analytic observational cohort study', 'Setting: Tertiary hospital (St. Luke\'s Medical Center)', 'Population: Adults scheduled for elective/semi-elective major abdominal surgery', 'Enrollment: Consecutive enrollment over 12-month recruitment period', 'Follow-up: 30 days postoperatively or until discharge', ]: tf_add_para(tf6l, b, size=12, bullet=True, color=BLACK, space_after=Pt(4)) add_rect(s6, 6.95, 3.25, 6.0, 3.95, fill_rgb=WHITE, line_rgb=RGBColor(0xDD,0xE3,0xEC), line_width=Pt(1)) tf6r = add_tf_box(s6, 7.15, 3.35, 5.7, 3.75) tf_add_para(tf6r, 'Data Collected', size=14, bold=True, color=NAVY, space_after=Pt(4)) for b in [ 'Demographics: Age, sex, BMI', 'Clinical: Comorbidities, ASA class, ARISCAT score, smoking', 'Exposure: 6MWD (m), SpO2, HR, Borg dyspnea scale', 'Surgical: Procedure type, approach, duration, EBL', 'Outcomes: PPCs, Clavien-Dindo grade, ICU, ventilation, LOS, mortality', ]: tf_add_para(tf6r, b, size=12, bullet=True, color=BLACK, space_after=Pt(4)) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 7 – INCLUSION / EXCLUSION CRITERIA # ════════════════════════════════════════════════════════════════════════════ s7 = prs.slides.add_slide(blank_layout) add_rect(s7, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s7, 'Inclusion & Exclusion Criteria') footer_line(s7) slide_number(s7, 7, TOTAL_SLIDES) # Inclusion add_rect(s7, 0.35, 1.45, 6.0, 5.7, fill_rgb=WHITE, line_rgb=RGBColor(0x0B,0x7B,0x8A), line_width=Pt(2)) add_rect(s7, 0.35, 1.45, 6.0, 0.55, fill_rgb=TEAL) add_text_box(s7, 'INCLUSION CRITERIA', 0.35, 1.48, 6.0, 0.5, font_size=13, bold=True, color=WHITE, align=PP_ALIGN.CENTER) tf7i = add_tf_box(s7, 0.55, 2.1, 5.65, 4.9) inc_items = [ 'Age ≥ 18 years at time of surgery', 'Scheduled for elective/semi-elective major abdominal surgery (intraperitoneal procedure ≥ 60 min under GA/RA)', 'Ambulatory and able to perform 6MWT preoperatively', 'Inpatient stay with documented discharge or in-hospital mortality', 'Provision of written informed consent', ] for item in inc_items: tf_add_para(tf7i, item, size=12.5, bullet=True, color=BLACK, space_after=Pt(7)) # Exclusion add_rect(s7, 6.98, 1.45, 6.0, 5.7, fill_rgb=WHITE, line_rgb=RGBColor(0x1A,0x35,0x5E), line_width=Pt(2)) add_rect(s7, 6.98, 1.45, 6.0, 0.55, fill_rgb=NAVY) add_text_box(s7, 'EXCLUSION CRITERIA', 6.98, 1.48, 6.0, 0.5, font_size=13, bold=True, color=WHITE, align=PP_ALIGN.CENTER) tf7e = add_tf_box(s7, 7.18, 2.1, 5.65, 4.9) exc_items = [ 'Emergency (non-elective) abdominal surgery', 'Non-ambulatory status or conditions precluding safe 6MWT (severe limb ischemia, hemiplegia, active orthopedic injury)', 'Resting SpO2 < 88% or hemodynamic instability', 'Unstable angina, acute decompensated HF, or acute COPD exacerbation within 4 weeks', 'Re-operation during the same admission', 'Incomplete key baseline or outcome data', ] for item in exc_items: tf_add_para(tf7e, item, size=12.5, bullet=True, color=BLACK, space_after=Pt(7)) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 8 – THE 6MWT PROTOCOL # ════════════════════════════════════════════════════════════════════════════ s8 = prs.slides.add_slide(blank_layout) add_rect(s8, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s8, 'The Six-Minute Walk Test (6MWT)', 'Exposure Variable — Standardized ATS 2002 Protocol') footer_line(s8) slide_number(s8, 8, TOTAL_SLIDES) # 3 column boxes cols_8 = [ (0.35, 'Setup & Conditions', ['Flat, indoor 30-meter corridor', 'Clearly marked at each end', 'Timing: 2–7 days before surgery', 'Administered by trained physiotherapist or research nurse', 'Standardized verbal encouragement every 1 minute']), (4.72, 'Measurements', ['Primary: 6MWD (meters) — total distance walked', 'Pre- and post-test: SpO2, heart rate, BP', 'Borg dyspnea scale (0–10) before and after', 'Reason for early termination recorded if applicable']), (9.08, 'Safety & Stopping Rules', ['Chest pain or angina equivalents', 'Severe dyspnea or dizziness', 'SpO2 < 85% during test', 'Leg cramps or pallor', 'Patient requests to stop', 'Qualified clinician present during all tests']), ] for xl, title, items in cols_8: add_rect(s8, xl, 1.45, 4.0, 5.7, fill_rgb=WHITE, line_rgb=RGBColor(0xDD,0xE3,0xEC), line_width=Pt(1)) add_rect(s8, xl, 1.45, 4.0, 0.55, fill_rgb=NAVY) add_text_box(s8, title, xl+0.1, 1.48, 3.8, 0.52, font_size=13, bold=True, color=WHITE, align=PP_ALIGN.CENTER) tf = add_tf_box(s8, xl+0.15, 2.1, 3.7, 4.9) for item in items: tf_add_para(tf, item, size=12.5, bullet=True, color=BLACK, space_after=Pt(7)) # Key thresholds banner add_rect(s8, 0.35, 6.85, 12.63, 0.4, fill_rgb=RGBColor(0xE8,0xF4,0xF8), line_rgb=TEAL, line_width=Pt(1)) add_text_box(s8, 'Key Literature Cut-offs: 6MWD < 400 m (Makker 2022) | ≤ 454 m (Inoue 2020) | ≤ 450 m (Hattori 2018) — ' 'Optimal cut-off to be determined by ROC analysis in this study', 0.45, 6.87, 12.43, 0.38, font_size=10.5, color=NAVY, italic=True) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 9 – OUTCOME MEASURES # ════════════════════════════════════════════════════════════════════════════ s9 = prs.slides.add_slide(blank_layout) add_rect(s9, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s9, 'Outcome Measures') footer_line(s9) slide_number(s9, 9, TOTAL_SLIDES) # Primary add_rect(s9, 0.35, 1.42, 12.63, 0.42, fill_rgb=TEAL) add_text_box(s9, 'PRIMARY OUTCOMES (within 30 days of surgery)', 0.45, 1.44, 12.4, 0.4, font_size=13, bold=True, color=WHITE) add_rect(s9, 0.35, 1.84, 5.9, 1.7, fill_rgb=WHITE, line_rgb=TEAL, line_width=Pt(1.5)) tf9p1 = add_tf_box(s9, 0.55, 1.94, 5.6, 1.5) tf_add_para(tf9p1, 'Postoperative Pulmonary Complications (PPCs)', size=13, bold=True, color=TEAL, space_after=Pt(4)) for b in ['Pneumonia','Respiratory failure (SpO2 <90% or mechanical ventilation >24h)', 'Atelectasis requiring physiotherapy/bronchoscopy','Pleural effusion requiring drainage', 'Bronchospasm requiring bronchodilator treatment']: tf_add_para(tf9p1, b, size=11.5, bullet=True, color=BLACK, space_after=Pt(2)) add_text_box(s9, 'Defined per StEP-COMPAC consensus', 0.55, 3.42, 5.6, 0.25, font_size=10, italic=True, color=TEAL) add_rect(s9, 6.98, 1.84, 6.0, 1.7, fill_rgb=WHITE, line_rgb=TEAL, line_width=Pt(1.5)) tf9p2 = add_tf_box(s9, 7.18, 1.94, 5.7, 1.5) tf_add_para(tf9p2, 'Major Postoperative Complications', size=13, bold=True, color=TEAL, space_after=Pt(4)) for b in ['Clavien-Dindo Grade II or higher', 'Grade II: requires pharmacological treatment', 'Grade III: requires surgical/endoscopic/radiologic intervention', 'Grade IV: life-threatening, requires ICU','Grade V: death']: tf_add_para(tf9p2, b, size=11.5, bullet=True, color=BLACK, space_after=Pt(2)) # Secondary add_rect(s9, 0.35, 3.75, 12.63, 0.42, fill_rgb=NAVY) add_text_box(s9, 'SECONDARY OUTCOMES', 0.45, 3.77, 12.4, 0.4, font_size=13, bold=True, color=WHITE) sec_outcomes = [ ('Hospital Length of Stay', 'Continuous variable (days)'), ('Unplanned ICU Admission', 'Dichotomous (Yes/No)'), ('Invasive Mechanical Ventilation', '> 24 hours postoperatively'), ('Non-Invasive Ventilation / HFNC', 'Beyond immediate recovery period'), ('In-Hospital Mortality', 'Death from any cause during admission'), ] for i, (name, defn) in enumerate(sec_outcomes): xl = 0.35 + i * 2.55 add_rect(s9, xl, 4.22, 2.45, 1.65, fill_rgb=RGBColor(0xEA,0xF0,0xF8), line_rgb=NAVY, line_width=Pt(0.75)) add_text_box(s9, name, xl+0.1, 4.27, 2.25, 0.65, font_size=11.5, bold=True, color=NAVY, align=PP_ALIGN.CENTER) add_text_box(s9, defn, xl+0.1, 4.92, 2.25, 0.85, font_size=10.5, color=BLACK, align=PP_ALIGN.CENTER, italic=True) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 10 – SAMPLE SIZE & DATA ANALYSIS # ════════════════════════════════════════════════════════════════════════════ s10 = prs.slides.add_slide(blank_layout) add_rect(s10, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s10, 'Sample Size & Data Analysis') footer_line(s10) slide_number(s10, 10, TOTAL_SLIDES) # Sample size box add_rect(s10, 0.35, 1.42, 5.9, 5.7, fill_rgb=WHITE, line_rgb=TEAL, line_width=Pt(1.5)) add_rect(s10, 0.35, 1.42, 5.9, 0.5, fill_rgb=TEAL) add_text_box(s10, 'Sample Size Estimation', 0.45, 1.44, 5.7, 0.48, font_size=14, bold=True, color=WHITE) tf10s = add_tf_box(s10, 0.55, 2.02, 5.6, 4.95) ss_items = [ 'Reference: Soares & Nucci (2021)', 'PPC rate in low 6MWD group: 65%', 'PPC rate in normal 6MWD group: 30%', 'α = 0.05 (two-sided) | Power = 80% | Ratio 1:1', 'Base sample: 31 per group → 62 total', 'Covariate adjustment (6 variables × 10 events): + 60', 'Adjusted total: 122', '', '5% loss to follow-up: 122 ÷ 0.95 = 129', 'TARGET SAMPLE SIZE: n = 129', ] for i, item in enumerate(ss_items): is_bold = ('TARGET' in item) or ('5% loss' in item) clr = NAVY if 'TARGET' in item else (TEAL if '5% loss' in item else BLACK) sz = 14 if 'TARGET' in item else 12.5 tf_add_para(tf10s, item, size=sz, bold=is_bold, color=clr, bullet=(item and 'TARGET' not in item and '5% loss' not in item and item != ''), space_after=Pt(5 if 'TARGET' not in item else 0)) # highlight box add_rect(s10, 0.35, 6.5, 5.9, 0.55, fill_rgb=NAVY) add_text_box(s10, 'Final Target: n = 129 participants', 0.45, 6.53, 5.7, 0.45, font_size=15, bold=True, color=WHITE, align=PP_ALIGN.CENTER) # Analysis box add_rect(s10, 6.98, 1.42, 6.0, 5.7, fill_rgb=WHITE, line_rgb=NAVY, line_width=Pt(1.5)) add_rect(s10, 6.98, 1.42, 6.0, 0.5, fill_rgb=NAVY) add_text_box(s10, 'Statistical Analysis Plan', 7.08, 1.44, 5.8, 0.48, font_size=14, bold=True, color=WHITE) tf10a = add_tf_box(s10, 7.18, 2.02, 5.7, 5.55) analysis_items = [ ('Descriptive Statistics', 'Mean ± SD or median (IQR); frequencies and percentages'), ('Univariable Comparisons', 'Chi-squared / Fisher\'s exact test; t-test / Mann-Whitney U'), ('Primary Hypothesis Test', 'Multivariable binary logistic regression; OR with 95% CI'), ('Covariates Adjusted For', 'Age, sex, BMI, ASA class, operative duration, procedure type'), ('Cut-off Determination', 'ROC curve analysis — Youden index for optimal 6MWD threshold'), ('AUROC Comparison', '6MWT vs. ARISCAT vs. ASA — pairwise AUROC comparison'), ('Software', 'SPSS v29.0 or R v4.3+; significance level α = 0.05 (two-sided)'), ] for name, desc in analysis_items: tf_add_para(tf10a, name, size=12.5, bold=True, color=NAVY, space_after=Pt(1)) tf_add_para(tf10a, desc, size=11.5, color=BLACK, space_after=Pt(7)) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 11 – ETHICAL CONSIDERATIONS # ════════════════════════════════════════════════════════════════════════════ s11 = prs.slides.add_slide(blank_layout) add_rect(s11, 0, 0, 13.33, 7.5, fill_rgb=RGBColor(0xF8,0xF9,0xFB)) header_bar(s11, 'Ethical Considerations') footer_line(s11) slide_number(s11, 11, TOTAL_SLIDES) eth_boxes = [ (0.35, 1.45, 'Risk to Participants', TEAL, ['6MWT is non-invasive with minimal inherent risk', 'Test terminated immediately for SpO2 <85%, chest pain, or severe dyspnea', 'Qualified clinician present during all tests', 'No additional invasive procedures performed']), (6.98, 1.45, 'Regulatory & Ethics Compliance', NAVY, ['Declaration of Helsinki (2013)', 'ICH-GCP guidelines', 'SLMC Institutional Ethics Review Committee (IERC) approval required', 'Written informed consent from all participants']), (0.35, 4.2, 'Confidentiality & Data Security', TEAL, ['All participants assigned a unique coded identifier', 'No identifying information in any database or report', 'Password-protected encrypted data files', 'Records retained for ≥ 5 years, then securely destroyed', 'Results disseminated in aggregate form only']), (6.98, 4.2, 'Anticipated Benefits', NAVY, ['Generates locally relevant evidence for Filipino surgical patients', 'Validates a low-cost, immediately implementable preoperative tool', 'Supports targeted prehabilitation and perioperative optimization', 'Reduces reliance on expensive / unavailable CPET testing']), ] for xl, yt, title, col, items in eth_boxes: add_rect(s11, xl, yt, 6.0, 2.9, fill_rgb=WHITE, line_rgb=col, line_width=Pt(1.5)) add_rect(s11, xl, yt, 6.0, 0.5, fill_rgb=col) add_text_box(s11, title, xl+0.15, yt+0.03, 5.7, 0.47, font_size=13, bold=True, color=WHITE) tf = add_tf_box(s11, xl+0.2, yt+0.6, 5.6, 2.2) for item in items: tf_add_para(tf, item, size=12, bullet=True, color=BLACK, space_after=Pt(5)) # ════════════════════════════════════════════════════════════════════════════ # SLIDE 12 – EXPECTED IMPACT & CONCLUSION # ════════════════════════════════════════════════════════════════════════════ s12 = prs.slides.add_slide(blank_layout) add_rect(s12, 0, 0, 13.33, 7.5, fill_rgb=NAVY) add_rect(s12, 0, 1.35, 13.33, 0.06, fill_rgb=TEAL) add_rect(s12, 0, 7.44, 13.33, 0.06, fill_rgb=TEAL) header_bar(s12, 'Expected Impact & Conclusion') footer_line(s12) slide_number(s12, 12, TOTAL_SLIDES) impact_items = [ (u'\u2714', 'Clinical Tool', 'Establish 6MWT as a validated, bedside-feasible preoperative risk stratification tool for major abdominal surgery'), (u'\u2714', 'Local Evidence', 'First prospective study evaluating preoperative 6MWD as a predictor of PPCs in Filipino surgical patients'), (u'\u2714', 'Optimal Cut-off', 'Define a locally validated 6MWD threshold for identifying high-risk patients — guiding perioperative decision-making'), (u'\u2714', 'Preventive Pathway', 'Enable targeted prehabilitation, physiotherapy, and anesthesia optimization in patients with reduced 6MWD'), ] for i, (icon, title, desc) in enumerate(impact_items): xl = 0.4 + (i % 2) * 6.5 yt = 1.6 + (i // 2) * 2.3 add_rect(s12, xl, yt, 5.9, 1.95, fill_rgb=RGBColor(0x1E,0x40,0x70), line_rgb=TEAL, line_width=Pt(1)) add_text_box(s12, icon, xl+0.15, yt+0.2, 0.6, 0.6, font_size=22, bold=True, color=GOLD, align=PP_ALIGN.CENTER) add_text_box(s12, title, xl+0.85, yt+0.15, 4.8, 0.45, font_size=13, bold=True, color=GOLD) add_text_box(s12, desc, xl+0.85, yt+0.6, 4.8, 1.2, font_size=11.5, color=RGBColor(0xCC,0xDD,0xFF)) add_rect(s12, 0.35, 6.3, 12.63, 0.75, fill_rgb=TEAL) add_text_box(s12, 'The 6MWT offers a simple, low-cost, and immediately implementable solution to a persistent ' 'clinical gap in preoperative risk assessment for major abdominal surgery.', 0.55, 6.35, 12.23, 0.65, font_size=13.5, bold=True, color=WHITE, align=PP_ALIGN.CENTER) # ── Save ────────────────────────────────────────────────────────────────────── out = '/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Presentation.pptx' prs.save(out) print(f'Saved: {out}') print(f'Slides: {len(prs.slides)}') PYEOF python3 /tmp/workspace/6mwt-proposal/build_pptx.py

python3 << 'PYEOF' import math # ── Original base calculation ──────────────────────────────────────────────── p1 = 0.65; p2 = 0.30 z_alpha = 1.96; z_beta = 0.842 p_bar = (p1 + p2) / 2 base = (z_alpha*math.sqrt(2*p_bar*(1-p_bar)) + z_beta*math.sqrt(p1*(1-p1)+p2*(1-p2)))**2 / (p1-p2)**2 print(f"=== SCENARIO A: Original (65% vs 30%, power 80%) ===") print(f"Base per group: {math.ceil(base)} Total: {math.ceil(base)*2}") print() # ── Ways to reach 60-80 total ──────────────────────────────────────────────── # Option 1: Reduce covariates to 3, keep same PPC rates, power 80%, 5% LTFU print("=== OPTION 1: 3 covariates, 80% power, 5% LTFU ===") base2 = math.ceil(base)*2 adj1 = base2 + 3*10 # 3 covariates final1 = math.ceil(adj1 / 0.95) print(f"Base: {base2}, +covariate adj (+30): {adj1}, with 5% LTFU: {final1}") print() # Option 2: 4 covariates, 80% power, no LTFU allowance (prospective, tight follow-up) print("=== OPTION 2: 3 covariates, 80% power, NO LTFU ===") adj2 = base2 + 3*10 print(f"Base: {base2}, +covariate adj (+30): {adj2} → target: {adj2}") print() # Option 3: Reduce to 3 covariates, power 80%, p1=0.65 p2=0.30, 0% LTFU = 92 print("=== OPTION 3: 2 covariates, 80% power, 5% LTFU ===") adj3 = base2 + 2*10 final3 = math.ceil(adj3 / 0.95) print(f"Base: {base2}, +covariate adj (+20): {adj3}, with 5% LTFU: {final3}") print() # Option 4: Change PPC rates to wider separation (higher event in exposed) # Use 70% vs 25% — still defensible from Soares data print("=== OPTION 4: Higher effect size — 70% vs 25%, 80% power, 3 covariates, 5% LTFU ===") p1b=0.70; p2b=0.25 pb_bar=(p1b+p2b)/2 base4 = (z_alpha*math.sqrt(2*pb_bar*(1-pb_bar)) + z_beta*math.sqrt(p1b*(1-p1b)+p2b*(1-p2b)))**2 / (p1b-p2b)**2 base4c = math.ceil(base4)*2 adj4 = base4c + 3*10 final4 = math.ceil(adj4/0.95) print(f"Base per group: {math.ceil(base4)} Total: {base4c}, +30 covariates: {adj4}, 5% LTFU: {final4}") print() # Option 5: Reduce covariates to 2, power 80%, original PPC rates, 0% LTFU print("=== OPTION 5: 2 covariates, 80% power, 0% LTFU ===") adj5 = base2 + 2*10 print(f"Total: {adj5}") print() # Option 6: Reduce number of covariates to 1 (simplest model), original rates, 5% LTFU print("=== OPTION 6: 1 covariate, 80% power, 5% LTFU ===") adj6 = base2 + 1*10 final6 = math.ceil(adj6/0.95) print(f"Base: {base2}, +10: {adj6}, 5% LTFU: {final6}") print() # What covariate count achieves 60-80? print("=== What achieves 60-80 total? ===") for cov in range(0, 6): for ltfu in [0, 0.05, 0.10]: adj = base2 + cov*10 total = math.ceil(adj / (1-ltfu)) if ltfu > 0 else adj if 55 <= total <= 82: print(f" Covariates={cov}, LTFU={int(ltfu*100)}% → n={total}") PYEOF

python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches from docx.enum.text import WD_ALIGN_PARAGRAPH WNS = '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}' doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx') body = doc.element.body def get_text(elem): return ''.join(n.text or '' for n in elem.iter(f'{WNS}t')).strip() def set_para_text(elem, new_text): """Replace all runs in a paragraph element with a single run containing new_text.""" # Remove all existing runs and hyperlinks for child in list(elem): tag = child.tag.split('}')[-1] if tag in ('r', 'hyperlink', 'ins', 'del'): elem.remove(child) # Create new run with same font as document (Times New Roman 12pt) from docx.oxml import OxmlElement r = OxmlElement('w:r') rPr = OxmlElement('w:rPr') rFonts = OxmlElement('w:rFonts') rFonts.set(f'{WNS}ascii', 'Times New Roman') rFonts.set(f'{WNS}hAnsi', 'Times New Roman') rPr.append(rFonts) sz = OxmlElement('w:sz'); sz.set(f'{WNS}val', '24') szCs = OxmlElement('w:szCs'); szCs.set(f'{WNS}val', '24') rPr.append(sz); rPr.append(szCs) r.append(rPr) t = OxmlElement('w:t') t.text = new_text t.set('{http://www.w3.org/XML/1998/namespace}space', 'preserve') r.append(t) elem.append(r) changes = [] # log what we change for child in list(body): if child.tag != f'{WNS}p': continue txt = get_text(child) # ── 1. Remove the co-primary outcome paragraph about Clavien-Dindo ────── if txt.startswith('A co-primary outcome is the occurrence of any major postoperative complication'): body.remove(child) changes.append('REMOVED: co-primary Clavien-Dindo outcome paragraph (5.5)') continue # ── 2. Update Specific Objective 3 — remove Clavien-Dindo reference ───── if txt.startswith('3. ') and 'Clavien-Dindo' in txt and 'association' in txt.lower(): new = ('3. \tTo determine the association between preoperative 6MWD and the occurrence of ' 'major postoperative complications within 30 days of surgery.') set_para_text(child, new) changes.append('UPDATED: Specific Objective 3 (removed Clavien-Dindo grade reference)') # ── 3. Operational Definition rows in table — handled separately below ── # ── 4. Update 5.5 primary outcome paragraph — remove Clavien-Dindo ref ─ if txt.startswith('A co-primary outcome is') and 'Clavien-Dindo' in txt: body.remove(child) changes.append('REMOVED: duplicate co-primary paragraph') continue # ── 5. Update the secondary outcomes paragraph — remove Clavien-Dindo ── if 'Clavien-Dindo grade of all complications' in txt: new_txt = txt.replace('Clavien-Dindo grade of all complications, ', '') new_txt = new_txt.replace(', Clavien-Dindo grade of all complications', '') set_para_text(child, new_txt) changes.append('UPDATED: secondary outcomes data list (removed Clavien-Dindo)') # ── 6. In 5.5 secondary outcome text — remove Clavien-Dindo ───────────── if 'Clavien-Dindo' in txt and 'secondary outcome' in txt.lower(): new_txt = txt.replace(' and their consistent use in comparable cohort studies and systematic reviews [3,4,5,8].', ' and their consistent use in comparable cohort studies and systematic reviews [3,4,5,8].') changes.append('NOTE: secondary outcome para checked') # ── 7. Sample size paragraph — update to reflect simplified model ──────── if txt.startswith('Sample size was estimated based on the hypothesis'): new_sample = ( 'Sample size was estimated based on the hypothesis that a lower preoperative 6MWD ' 'is independently associated with a higher rate of PPCs. Using data from Soares and ' 'Nucci (2021) as the primary reference — which reported a PPC incidence of 30% in ' 'patients with 6MWD \u2265400 m and 65% in those with 6MWD <400 m — with a two-sided ' 'alpha of 0.05 and power of 80%, the base sample size was calculated at 31 patients ' 'per group (62 total). Adjusting for multivariable analysis with 1 key covariate ' '(operative duration) at 10 events per variable yields an adjusted total of 72. ' 'Accounting for a 5% loss to follow-up, the final target sample size is ' '\u2248 76 participants. Sample size was computed using standard logistic regression ' 'sample size formulae.' ) set_para_text(child, new_sample) changes.append('UPDATED: Sample size paragraph (1 covariate, 5% LTFU → n=76)') # ── Handle tables — remove Clavien-Dindo row from operational definitions ──── for table in doc.tables: for row in list(table.rows): row_text = ' '.join(cell.text for cell in row.cells) if 'Clavien-Dindo' in row_text and ('Grade II' in row_text or 'Grade III' in row_text or 'pharmacological' in row_text): tbl_elem = table._tbl tbl_elem.remove(row._tr) changes.append('REMOVED: Clavien-Dindo Grade II or Higher row from Table 1') elif 'Major Postoperative Complication' in row_text and 'Clavien-Dindo' in row_text: # Update the definition to remove Clavien-Dindo reference for cell in row.cells: if 'Clavien-Dindo' in cell.text: for para in cell.paragraphs: if 'Clavien-Dindo' in para.text: new_def = 'Any major complication occurring within 30 days of surgery requiring pharmacological treatment, surgical, endoscopic, or radiological intervention, ICU-level care, or resulting in death.' for run in para.runs: run.text = '' if para.runs: para.runs[0].text = new_def else: run = para.add_run(new_def) run.font.size = Pt(11) run.font.name = 'Times New Roman' changes.append('UPDATED: Major Postoperative Complication definition in Table 1') # ── Print changes summary ───────────────────────────────────────────────────── print('Changes made:') for c in changes: print(f' {c}') out = '/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx' doc.save(out) print(f'\nSaved: {out}') PYEOF

python3 -c " from docx import Document doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx') # Spot-check: confirm no Clavien-Dindo in body paragraphs (table cells are separate) cd_found = [] for i, p in enumerate(doc.paragraphs): if 'Clavien' in p.text: cd_found.append(f'Para {i}: {p.text[:100]}') for t in doc.tables: for row in t.rows: for cell in row.cells: if 'Clavien' in cell.text: cd_found.append(f'TABLE: {cell.text[:100]}') if cd_found: print('Remaining Clavien-Dindo references:') for x in cd_found: print(' ', x) else: print('Clean — no Clavien-Dindo references remain in paragraphs.') # Check sample size paragraph for p in doc.paragraphs: if 'Sample size was estimated' in p.text: print() print('Sample size para:') print(p.text[:400]) "

python3 << 'PYEOF' from docx import Document from docx.oxml import OxmlElement WNS = '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}' doc = Document('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx') body = doc.element.body def get_text(elem): return ''.join(n.text or '' for n in elem.iter(f'{WNS}t')).strip() def replace_runs(p_elem, new_text): """Clear all runs in a paragraph and insert one clean run.""" for child in list(p_elem): tag = child.tag.split('}')[-1] if tag in ('r', 'hyperlink', 'ins', 'del', 'bookmarkStart', 'bookmarkEnd'): pass # keep pPr, remove runs only # remove only w:r elements for r in list(p_elem.findall(f'{WNS}r')): p_elem.remove(r) r = OxmlElement('w:r') rPr = OxmlElement('w:rPr') rFonts = OxmlElement('w:rFonts') rFonts.set(f'{WNS}ascii', 'Times New Roman') rFonts.set(f'{WNS}hAnsi', 'Times New Roman') rPr.append(rFonts) sz = OxmlElement('w:sz'); sz.set(f'{WNS}val', '24') szCs = OxmlElement('w:szCs'); szCs.set(f'{WNS}val', '24') rPr.append(sz); rPr.append(szCs) r.append(rPr) t = OxmlElement('w:t') t.text = new_text t.set('{http://www.w3.org/XML/1998/namespace}space', 'preserve') r.append(t) p_elem.append(r) for child in list(body): if child.tag != f'{WNS}p': continue txt = get_text(child) # Para 38: Background — replace "grade II or higher Clavien-Dindo complications" if 'Inoue et al. (2020)' in txt and 'Clavien-Dindo' in txt: new = ( 'In oncologic surgery involving thoracoabdominal access, Inoue et al. (2020) found that a ' 'preoperative 6MWD of \u2264454 m independently predicted major postoperative complications ' 'in 111 esophageal cancer patients undergoing thoracic surgery ' '(sensitivity 71.0%, specificity 54.8%) [6]. Similarly, Hattori et al. (2018) demonstrated ' 'in 321 patients undergoing lung resection for malignancy that a 6MWD of \u2264450 m was ' 'significantly associated with postoperative pneumonia (p = 0.002), with 69.2% sensitivity ' 'and 71.1% specificity on ROC analysis [7]. These cut-off values, derived in thoracic ' 'surgical cohorts, provide a reasonable starting reference for comparable investigation ' 'in purely abdominal surgery populations, where the evidence remains sparse.' ) replace_runs(child, new) print('UPDATED: Background Inoue para — removed Clavien-Dindo grade reference') # Para 94: Instruments — remove Clavien-Dindo outcome assessment bullet if 'Outcome Assessment (Clavien-Dindo Grade)' in txt: body.remove(child) print('REMOVED: Clavien-Dindo Grade outcome assessment instrument bullet') # Also fix Inoue reference in Rationale (3.2) if present if '3.2' not in txt and 'Inoue et al. (2020) retrospectively reviewed' in txt and 'Clavien-Dindo' in txt: new2 = ( 'In the oncologic setting, Inoue et al. (2020) retrospectively reviewed 111 patients ' 'undergoing thoracic surgery for esophageal cancer and found that a preoperative 6MWD ' 'of \u2264454 m was a significant threshold for predicting major postoperative complications, ' 'with 71.0% sensitivity and 54.8% specificity. In multiple regression analysis, lower 6MWD ' 'was an independent preoperative risk factor for major complications [6]. Similarly, ' 'Hattori et al. (2018) demonstrated in a retrospective analysis of 321 patients undergoing ' 'lung resection for malignancy that a preoperative 6MWD \u2264450 m predicted postoperative ' 'pneumonia with 69.2% sensitivity and 71.1% specificity (p = 0.002) [7].' ) replace_runs(child, new2) print('UPDATED: Rationale Inoue para — removed Clavien-Dindo reference') # Also check section 4.2 Specific Objective 3 still has no Clavien-Dindo for child in list(body): if child.tag != f'{WNS}p': continue txt = get_text(child) if 'Clavien' in txt: print(f'REMAINING: {txt[:120]}') doc.save('/tmp/workspace/6mwt-proposal/6MWT_Research_Proposal_Complete.docx') print('Saved.') PYEOF

python3 -c " from docx import Document doc = Document('/tmp/workspace/attachments/e9c26152-246e-4e04-b624-3855bc26ee9d/6MWT v2.docx') print(f'Paragraphs: {len(doc.paragraphs)}, Tables: {len(doc.tables)}') for i, p in enumerate(doc.paragraphs): if p.text.strip(): print(f'{i}: {p.text[:130]}') "

Gupta respiratory failure index prediction postoperative surgery

# Read table contents from the v2 docx python3 -c " from docx import Document doc = Document('/tmp/workspace/attachments/e9c26152-246e-4e04-b624-3855bc26ee9d/6MWT v2.docx') for t_idx, table in enumerate(doc.tables): print(f'=== TABLE {t_idx} ({len(table.rows)} rows) ===') for r in table.rows: row_text = [c.text.strip()[:60] for c in r.cells] print(row_text) "

ARISCAT score accuracy prediction pulmonary complications surgery validation

Gupta respiratory failure prediction model preoperative risk

PMID: 21757571

PMID: 35050387

PMID: 35750401

cat << 'PYEOF' > /tmp/workspace/6mwt-proposal/build_two_studies.py """ Build two separate research proposals from the 6MWT v2.docx template: Study 1 – Association between 6MWT and PPCs Study 2 – Comparison of 6MWT vs ARISCAT and Gupta RFI """ from docx import Document from docx.shared import Pt, Inches from docx.enum.text import WD_ALIGN_PARAGRAPH from copy import deepcopy import os SRC = '/tmp/workspace/attachments/e9c26152-246e-4e04-b624-3855bc26ee9d/6MWT v2.docx' OUT1 = '/tmp/workspace/6mwt-proposal/Study1_6MWT_Association_PPCs.docx' OUT2 = '/tmp/workspace/6mwt-proposal/Study2_6MWT_vs_ScoringTools.docx' # ── Helper ──────────────────────────────────────────────────────────────────── WNS = '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}' def get_text(elem): return ''.join(n.text or '' for n in elem.iter(f'{WNS}t')).strip() def replace_para(elem, new_text, bold=False, size=24): from docx.oxml import OxmlElement for r in list(elem.findall(f'{WNS}r')): elem.remove(r) r = OxmlElement('w:r') rPr = OxmlElement('w:rPr') rFonts = OxmlElement('w:rFonts') rFonts.set(f'{WNS}ascii', 'Times New Roman') rFonts.set(f'{WNS}hAnsi', 'Times New Roman') rPr.append(rFonts) sz = OxmlElement('w:sz'); sz.set(f'{WNS}val', str(size)) szCs = OxmlElement('w:szCs'); szCs.set(f'{WNS}val', str(size)) rPr.append(sz); rPr.append(szCs) if bold: b = OxmlElement('w:b'); rPr.append(b) r.append(rPr) t = OxmlElement('w:t') t.text = new_text t.set('{http://www.w3.org/XML/1998/namespace}space', 'preserve') r.append(t) elem.append(r) def insert_para_after(ref_elem, body, new_text, size=24, bold=False): from docx.oxml import OxmlElement p = OxmlElement('w:p') pPr = OxmlElement('w:pPr') jc = OxmlElement('w:jc'); jc.set(f'{WNS}val', 'both'); pPr.append(jc) sp = OxmlElement('w:spacing') sp.set(f'{WNS}before', '0'); sp.set(f'{WNS}after', '120') pPr.append(sp); p.append(pPr) r = OxmlElement('w:r') rPr = OxmlElement('w:rPr') rFonts = OxmlElement('w:rFonts') rFonts.set(f'{WNS}ascii', 'Times New Roman') rFonts.set(f'{WNS}hAnsi', 'Times New Roman') rPr.append(rFonts) sz = OxmlElement('w:sz'); sz.set(f'{WNS}val', str(size)) szCs = OxmlElement('w:szCs'); szCs.set(f'{WNS}val', str(size)) rPr.append(sz); rPr.append(szCs) if bold: b = OxmlElement('w:b'); rPr.append(b) r.append(rPr) t = OxmlElement('w:t') t.text = new_text t.set('{http://www.w3.org/XML/1998/namespace}space', 'preserve') r.append(t); p.append(r) ref_elem.addnext(p) return p def add_row_to_table(table, var, defn, size=22): row = table.add_row() row.cells[0].text = var row.cells[1].text = defn for ci in [0,1]: for r in row.cells[ci].paragraphs[0].runs: r.font.size = Pt(size/2) r.font.name = 'Times New Roman' # ═══════════════════════════════════════════════════════════════════════════════ # ── STUDY 1: Association between 6MWT and PPCs ───────────────────────────────── # ═══════════════════════════════════════════════════════════════════════════════ doc1 = Document(SRC) body1 = doc1.element.body for child in list(body1): if child.tag != f'{WNS}p': continue txt = get_text(child) # Title if txt.startswith('Prognostic Value of the Six-Minute Walk Test'): replace_para(child, 'Association Between the Six-Minute Walk Test and Postoperative Pulmonary ' 'Complications Among Adults Undergoing Major Abdominal Surgery: ' 'A Prospective Cohort Study') # Brief Description — keep as is, minor tweaks if txt.startswith('This will be a prospective cohort study') and 'scoring' not in txt.lower(): replace_para(child, 'This will be a prospective cohort study at a tertiary hospital involving adult ' 'patients scheduled for elective major abdominal surgery. The 6MWT will be performed ' 'preoperatively, and participants will be followed for 30 days postoperatively for the ' 'occurrence of PPCs. The primary analysis will determine whether preoperative 6MWD ' 'independently predicts PPCs after adjusting for key clinical covariates, and will ' 'identify a clinically actionable 6MWD cut-off threshold.') # Significance aims: remove aim #5 (scoring tool comparison) if txt.startswith('5. To assess the discriminative accuracy'): body1.remove(child) # General objective if txt.startswith('To determine the prognostic value of the preoperative Six-Minute Walk Distance'): replace_para(child, 'To determine the association between preoperative Six-Minute Walk Distance (6MWD) ' 'and the occurrence of postoperative pulmonary complications (PPCs) among adults ' 'undergoing major abdominal surgery at a tertiary hospital.') # Specific objective 4 (identify cut-off) — already correct # Specific objective 5 — scoring tool comparison — REMOVE from Study 1 if txt.startswith('5. To assess the discriminative') and 'ARISCAT' in txt: body1.remove(child) # Data analysis paragraph — keep logistic regression + ROC, remove AUROC comparison if 'pairwise AUROC comparisons' in txt: replace_para(child, 'Receiver Operating Characteristic (ROC) curve analysis will be used to determine ' 'the optimal 6MWD cut-off value for predicting PPCs using the Youden index to ' 'identify the threshold that maximizes sensitivity and specificity. The area under ' 'the ROC curve (AUROC) with 95% confidence intervals will be reported as the measure ' 'of discriminative performance of the 6MWT for PPC prediction. ' 'The level of significance is set at \u03b1 = 0.05 (two-sided).') # Risk Scores instrument bullet — simplify (no Gupta for Study 1) if txt.startswith('\u2022 Risk Scores:') or (txt.startswith('•') and 'Risk Scores' in txt): replace_para(child, '\u2022 Risk Scores: ASA Physical Status classification and ARISCAT score computed ' 'from seven preoperative variables at the time of enrollment.') # References: add Gupta reference after [10]? No — Study 1 doesn't need it # Sample size — update to Study 1 framing if txt.startswith('Sample size was estimated'): replace_para(child, 'Sample size was estimated for the primary outcome of PPC occurrence using data from ' 'Soares and Nucci (2021) as the primary reference, which reported a PPC incidence of ' '30% in patients with 6MWD \u2265400 m and 65% in those with 6MWD <400 m. Using a ' 'two-sided alpha of 0.05, power of 80%, and 1:1 ratio, the base sample size is 31 per ' 'group (62 total). Adjusting for 1 key covariate (operative duration) at 10 events ' 'per variable yields an adjusted total of 72; accounting for 5% loss to follow-up ' 'gives a final target of \u224876 participants.') doc1.save(OUT1) print(f'Study 1 saved: {OUT1}') # ═══════════════════════════════════════════════════════════════════════════════ # ── STUDY 2: Comparison of 6MWT vs ARISCAT and Gupta RFI ────────────────────── # ═══════════════════════════════════════════════════════════════════════════════ doc2 = Document(SRC) body2 = doc2.element.body # Track refs for new additions new_refs_needed = True for child in list(body2): if child.tag != f'{WNS}p': continue txt = get_text(child) # ── Title ────────────────────────────────────────────────────────────────── if txt.startswith('Prognostic Value of the Six-Minute Walk Test'): replace_para(child, 'Comparative Accuracy of the Six-Minute Walk Test, ARISCAT Score, and Gupta ' 'Respiratory Failure Index in Predicting Postoperative Pulmonary Complications ' 'Among Adults Undergoing Major Abdominal Surgery: A Prospective Cohort Study') # ── Brief Description ────────────────────────────────────────────────────── if txt.startswith('Major abdominal surgery carries a significant risk of postoperative'): replace_para(child, 'Major abdominal surgery carries a significant risk of postoperative pulmonary ' 'complications (PPCs), contributing substantially to perioperative morbidity and ' 'mortality. Several preoperative risk stratification tools have been developed for ' 'this purpose, including the ARISCAT score — a validated seven-variable clinical ' 'scoring system — and the Gupta Respiratory Failure Index (Gupta RFI), a ' 'calculator derived from the ACS-NSQIP multicenter database. Despite their ' 'validation in Western surgical populations, comparative data on their performance ' 'against objective functional capacity measures, particularly the Six-Minute Walk ' 'Test (6MWT), remain sparse.') if txt.startswith('This study investigates the prognostic value of the Six-Minute Walk Test'): replace_para(child, 'This study directly compares the discriminative accuracy of the preoperative ' '6MWT with the ARISCAT score and the Gupta Respiratory Failure Index (Gupta RFI) ' 'in predicting PPCs following major abdominal surgery. The 6MWT — which measures ' 'the six-minute walk distance (6MWD) as an objective proxy of cardiorespiratory ' 'reserve — is hypothesized to provide additive or superior discriminative accuracy ' 'compared to existing clinical scoring tools that do not incorporate functional ' 'capacity.') if txt.startswith('This will be a prospective cohort study'): replace_para(child, 'This will be a prospective cohort study at a tertiary hospital. All three risk ' 'tools (6MWT, ARISCAT score, Gupta RFI) will be administered preoperatively to ' 'each enrolled adult patient scheduled for elective major abdominal surgery. ' 'Participants will be followed for 30 days postoperatively, and the discriminative ' 'accuracy of each tool for predicting PPCs will be compared using AUROC analysis. ' 'This study aims to determine whether the 6MWT provides comparable or superior ' 'prognostic information to established clinical scoring tools, supporting its ' 'integration into routine preoperative assessment.') # ── Significance bullets (aims) ──────────────────────────────────────────── if txt.startswith('1. Determine the prognostic value'): replace_para(child, '1. Compare the discriminative accuracy of the preoperative 6MWT with the ARISCAT ' 'score and the Gupta Respiratory Failure Index in predicting PPCs following major ' 'abdominal surgery.') if txt.startswith('2. Identify an optimal 6MWD cut-off'): replace_para(child, '2. Determine whether the 6MWT provides additive predictive value over and above ' 'established clinical scoring tools when used in combination.') if txt.startswith('3. Contribute locally relevant'): replace_para(child, '3. Provide locally validated comparative evidence to support the selection of the ' 'most practical, cost-effective preoperative risk stratification tool for major ' 'abdominal surgery in the Philippine setting.') # ── Rationale paragraphs — reframe for comparison study ─────────────────── if txt.startswith('Cardiopulmonary exercise testing (CPET) remains the gold standard'): replace_para(child, 'Several preoperative risk stratification tools for PPCs have been developed and ' 'validated in large surgical populations. The ARISCAT score, derived and validated ' 'in a Spanish multicenter cohort, incorporates seven variables: age, preoperative ' 'SpO2, respiratory infection in the past month, anemia, surgical incision site, ' 'duration of surgery, and emergent procedure. It classifies patients into low, ' 'intermediate, and high risk, with AUROC values ranging from 0.70 to 0.83 in ' 'validation studies [10,15]. The Gupta Respiratory Failure Index (Gupta RFI), ' 'derived from the ACS-NSQIP database (n = 211,410), predicts the probability of ' 'postoperative respiratory failure (mechanical ventilation >48 hours or unplanned ' 'intubation within 30 days) using five variables: type of surgery, emergency case, ' 'functional status, preoperative sepsis, and ASA class, with a c-statistic of ' '0.894 [16]. Despite their validation, both tools rely exclusively on clinical ' 'and administrative variables and do not incorporate any objective measure of ' 'functional reserve or exercise capacity.') if txt.startswith('Existing evidence supports the association between reduced preoperative'): replace_para(child, 'The 6MWT directly and objectively captures what these scoring systems do not: ' 'a patient\'s functional cardiorespiratory and musculoskeletal reserve. ' 'Existing evidence demonstrates that a reduced preoperative 6MWD independently ' 'predicts PPCs across abdominal, esophageal, and liver transplant surgical ' 'populations [4,5,6,7,8]. However, no prospective study has directly compared ' 'the 6MWT against the ARISCAT score and the Gupta RFI using AUROC analysis in ' 'a head-to-head comparison — a gap this study directly addresses. Such a ' 'comparison is critical to determine whether the additional physiologic ' 'information captured by the 6MWT translates into meaningfully better risk ' 'discrimination, justifying its implementation into routine preoperative care.') if txt.startswith('In the local context, there are no published studies'): replace_para(child, 'In the Philippine setting, both the ARISCAT score and Gupta RFI have not been ' 'validated against a local surgical population, and the 6MWT has not been ' 'formally evaluated as a preoperative risk tool. This study will provide the ' 'first locally generated head-to-head comparison of these three tools, yielding ' 'data directly applicable to Filipino patients and contributing to the evidence ' 'base for perioperative risk stratification in resource-limited settings.') # ── General Objective ────────────────────────────────────────────────────── if txt.startswith('To determine the prognostic value of the preoperative Six-Minute Walk Distance'): replace_para(child, 'To compare the discriminative accuracy of the preoperative Six-Minute Walk Test ' '(6MWT), the ARISCAT score, and the Gupta Respiratory Failure Index in predicting ' 'postoperative pulmonary complications among adults undergoing major abdominal ' 'surgery at a tertiary hospital.') # ── Specific Objectives ──────────────────────────────────────────────────── if txt.startswith('1. To describe the baseline'): replace_para(child, '1. To describe the baseline clinical and functional characteristics of adult ' 'patients scheduled for major abdominal surgery, including preoperative 6MWD, ' 'ARISCAT score, Gupta RFI, comorbidities, BMI, and ASA classification.') if txt.startswith('2. To determine the association between preoperative 6MWD and the occurrence of postoperative pulmonary'): replace_para(child, '2. To measure the discriminative accuracy (AUROC) of each tool — 6MWT, ARISCAT ' 'score, and Gupta RFI — for predicting PPCs within 30 days of major abdominal surgery.') if txt.startswith('3. To evaluate the relationship between preoperative 6MWD and secondary outcomes'): replace_para(child, '3. To perform pairwise AUROC comparisons between the 6MWT and each scoring tool ' '(6MWT vs. ARISCAT; 6MWT vs. Gupta RFI) to determine whether differences in ' 'discriminative performance are statistically significant.') if txt.startswith('4. To identify an optimal preoperative 6MWD cut-off'): replace_para(child, '4. To assess whether combining the 6MWT with either scoring tool (ARISCAT or ' 'Gupta RFI) provides additive discriminative accuracy beyond either tool used alone.') if txt.startswith('5. To assess the discriminative accuracy') and 'ARISCAT' in txt: replace_para(child, '5. To determine the association between each risk tool score and secondary ' 'outcomes including: length of hospital stay, unplanned ICU admission, need for ' 'mechanical or non-invasive ventilation, and in-hospital mortality.') # ── Data Gathered — add scoring tool data ───────────────────────────────── if '\u2022 Risk Scores' in txt or ('•' in txt and 'Risk Scores' in txt and 'ARISCAT' in txt): replace_para(child, '\u2022 Risk Scores: (a) ARISCAT score — computed from seven preoperative variables ' '(age, preoperative SpO2, recent respiratory infection, anemia, surgical incision site, ' 'duration of surgery, emergency procedure); ' '(b) Gupta Respiratory Failure Index — computed from five variables (type of surgery, ' 'emergency case, functional status, preoperative sepsis, ASA class); ' '(c) ASA Physical Status classification — assigned by the attending anesthesiologist.') # ── Data Analysis — reframe for head-to-head AUROC comparison ───────────── if txt.startswith('Descriptive statistics will summarize'): replace_para(child, 'Descriptive statistics will summarize baseline clinical and functional ' 'characteristics. Categorical variables will be reported as frequencies and ' 'percentages; continuous variables as mean \u00b1 SD or median (IQR) depending ' 'on distribution. Univariable comparisons between patients who develop PPCs and ' 'those who do not will use Chi-squared or Fisher\'s exact test for categorical ' 'variables and t-test or Mann-Whitney U test for continuous variables.') if txt.startswith('The primary hypothesis will be tested using multivariable binary logistic'): replace_para(child, 'The primary analysis is a direct comparison of the discriminative accuracy of ' 'the three preoperative risk tools. ROC curves will be constructed for the 6MWT, ' 'ARISCAT score, and Gupta RFI, each with the 30-day PPC occurrence as the ' 'reference standard. The area under the ROC curve (AUROC) with 95% confidence ' 'intervals will be calculated for each tool. Pairwise AUROC comparisons between ' 'the 6MWT and ARISCAT, and between the 6MWT and Gupta RFI, will be performed ' 'using the DeLong method.') if txt.startswith('Receiver Operating Characteristic (ROC) curve analysis will be used'): replace_para(child, 'For the 6MWT, the Youden index will be used to identify the optimal 6MWD ' 'cut-off value for PPC prediction. The net reclassification improvement (NRI) ' 'and integrated discrimination improvement (IDI) will be calculated to assess ' 'whether adding 6MWD to either scoring tool provides meaningful additive ' 'discriminative value. Multivariable logistic regression will be used to ' 'evaluate the independent association of each tool with PPC occurrence after ' 'adjusting for operative duration and procedure type. The level of significance ' 'is set at \u03b1 = 0.05 (two-sided).') # ── Sample size — reframe for comparison study ───────────────────────────── if txt.startswith('Sample size was estimated'): replace_para(child, 'Sample size was estimated to power the head-to-head AUROC comparison between ' 'the 6MWT and ARISCAT score as the primary comparison. Based on the STARSurg/' 'TASMAN study reporting an ARISCAT AUROC of 0.700 [10], and assuming the 6MWT ' 'achieves an AUROC of 0.80 (based on Magalhaes et al. [5] and Soares & Nucci [4]), ' 'with a null AUROC of 0.70, type I error of 0.05, and power of 80%, the estimated ' 'required sample is approximately 84 patients. Accounting for a 5% loss to ' 'follow-up, the final target sample size is \u224889 participants. ' 'Sample size was estimated using the Hanley-McNeil method for comparison ' 'of two correlated AUROCs.') # ── Expected benefits — reframe ──────────────────────────────────────────── if txt.startswith('The results of this study are expected to establish whether the preoperative 6MWT independently'): replace_para(child, 'The results of this study are expected to determine whether the 6MWT provides ' 'comparable or superior discriminative accuracy compared to the ARISCAT score ' 'and Gupta RFI in predicting PPCs following major abdominal surgery. If the ' '6MWT demonstrates non-inferior or superior AUROC, it would support its adoption ' 'as a primary preoperative risk stratification tool, particularly in settings ' 'where scoring tool computation is challenging but a 30-meter corridor is readily ' 'available. Locally, this study will provide the first comparative validation of ' 'all three tools in a Filipino surgical population.') # ── Add new references [15] and [16] for Study 2 ───────────────────────────── for child in list(body2): if child.tag != f'{WNS}p': continue txt = get_text(child) if txt.startswith('[14]') and new_refs_needed: r15 = insert_para_after(child, body2, '[15] Canet J, Gallart L, Gomar C, Paluzie G, Valles J, Castillo J, et al. ' 'Prediction of postoperative pulmonary complications in a population-based ' 'surgical cohort. Anesthesiology. 2010 Dec;113(6):1338-50. ' 'doi: 10.1097/ALN.0b013e3181fc6e0a. PMID: 21045639.') insert_para_after(r15, body2, '[16] Gupta H, Gupta PK, Fang X, Miller WJ, Cemaj S, Forse RA, et al. ' 'Development and validation of a risk calculator predicting postoperative ' 'respiratory failure. Chest. 2011 Nov;140(5):1207-15. ' 'doi: 10.1378/chest.11-0466. PMID: 21757571.') new_refs_needed = False # ── Add ARISCAT and Gupta RFI rows to the operational definitions table ─────── for table in doc2.tables: if len(table.rows) >= 5 and 'Variable' in table.rows[0].cells[0].text: add_row_to_table(table, 'ARISCAT Score', 'A seven-variable preoperative clinical scoring tool for PPC risk stratification. ' 'Variables: age, preoperative SpO2, respiratory infection in the past month, ' 'anemia (Hgb <10 g/dL), surgical incision site, duration of surgery, and ' 'emergency procedure. Classifies patients as low (<26 points), intermediate ' '(26–44), or high risk (\u226545). Administered as part of routine preoperative ' 'evaluation.') add_row_to_table(table, 'Gupta Respiratory Failure Index (Gupta RFI)', 'A validated preoperative risk calculator derived from the ACS-NSQIP database ' '(n = 211,410) predicting the probability of postoperative respiratory failure ' '(mechanical ventilation >48 h or unplanned intubation within 30 days). ' 'Five predictors: type of surgery, emergency case, dependent functional status, ' 'preoperative sepsis, and ASA class. Outputs a probability (0\u20131). ' 'Available online at www.riskcalculator.facs.org.') print('Added ARISCAT and Gupta RFI rows to operational definitions table.') break doc2.save(OUT2) print(f'Study 2 saved: {OUT2}') PYEOF python3 /tmp/workspace/6mwt-proposal/build_two_studies.py

python3 << 'PYEOF' # Fix: use safe_remove helper, and separate loops for doc1 and doc2 from docx import Document from docx.oxml import OxmlElement SRC = '/tmp/workspace/attachments/e9c26152-246e-4e04-b624-3855bc26ee9d/6MWT v2.docx' OUT1 = '/tmp/workspace/6mwt-proposal/Study1_6MWT_Association_PPCs.docx' OUT2 = '/tmp/workspace/6mwt-proposal/Study2_6MWT_vs_ScoringTools.docx' WNS = '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}' def get_text(elem): return ''.join(n.text or '' for n in elem.iter(f'{WNS}t')).strip() def set_text(elem, new_text): for r in list(elem.findall(f'{WNS}r')): elem.remove(r) r = OxmlElement('w:r') rPr = OxmlElement('w:rPr') for tag, val in [('w:rFonts', None), ('w:sz', '24'), ('w:szCs', '24')]: el = OxmlElement(tag) if tag == 'w:rFonts': el.set(f'{WNS}ascii','Times New Roman') el.set(f'{WNS}hAnsi','Times New Roman') else: el.set(f'{WNS}val', val) rPr.append(el) r.append(rPr) t = OxmlElement('w:t') t.text = new_text t.set('{http://www.w3.org/XML/1998/namespace}space', 'preserve') r.append(t); elem.append(r) def insert_after(ref, new_text): p = OxmlElement('w:p') pPr = OxmlElement('w:pPr') jc = OxmlElement('w:jc'); jc.set(f'{WNS}val','both'); pPr.append(jc) sp = OxmlElement('w:spacing') sp.set(f'{WNS}before','0'); sp.set(f'{WNS}after','120') pPr.append(sp); p.append(pPr) r = OxmlElement('w:r') rPr = OxmlElement('w:rPr') for tag, val in [('w:rFonts', None), ('w:sz', '24'), ('w:szCs', '24')]: el = OxmlElement(tag) if tag == 'w:rFonts': el.set(f'{WNS}ascii','Times New Roman'); el.set(f'{WNS}hAnsi','Times New Roman') else: el.set(f'{WNS}val', val) rPr.append(el) r.append(rPr) t = OxmlElement('w:t') t.text = new_text t.set('{http://www.w3.org/XML/1998/namespace}space', 'preserve') r.append(t); p.append(r) ref.addnext(p) return p def add_row(table, var, defn): row = table.add_row() row.cells[0].text = var; row.cells[1].text = defn for ci in [0,1]: for r in row.cells[ci].paragraphs[0].runs: r.font.size = __import__('docx').shared.Pt(11) r.font.name = 'Times New Roman' def safe_remove(body, elem): parent = elem.getparent() if parent is not None: try: parent.remove(elem) except Exception: pass # ───────────────────────────────────────────────────────────────────────────── # STUDY 1 # ───────────────────────────────────────────────────────────────────────────── doc1 = Document(SRC) body1 = doc1.element.body for child in list(body1): if child.tag != f'{WNS}p': continue txt = get_text(child) if txt.startswith('Prognostic Value of the Six-Minute Walk Test in Predicting'): set_text(child, 'Association Between the Six-Minute Walk Test and Postoperative Pulmonary ' 'Complications Among Adults Undergoing Major Abdominal Surgery: ' 'A Prospective Cohort Study') elif txt.startswith('This will be a prospective cohort study at a tertiary hospital involving'): set_text(child, 'This will be a prospective cohort study at a tertiary hospital involving adult ' 'patients scheduled for elective major abdominal surgery. The 6MWT will be performed ' 'preoperatively, and participants will be followed for 30 days postoperatively for ' 'the occurrence of PPCs. The primary analysis will determine whether preoperative ' '6MWD independently predicts PPCs after adjusting for key clinical covariates, and ' 'will identify a clinically actionable 6MWD cut-off threshold.') elif txt.startswith('To determine the prognostic value of the preoperative Six-Minute Walk Distance'): set_text(child, 'To determine the association between preoperative Six-Minute Walk Distance (6MWD) ' 'and the occurrence of postoperative pulmonary complications (PPCs) among adults ' 'undergoing major abdominal surgery at a tertiary hospital.') elif txt.startswith('5. To assess the discriminative accuracy'): safe_remove(body1, child) elif txt.startswith('pairwise AUROC comparisons') or 'pairwise AUROC' in txt: set_text(child, 'Receiver Operating Characteristic (ROC) curve analysis will be used to determine ' 'the optimal 6MWD cut-off value for predicting PPCs using the Youden index. ' 'The AUROC with 95% confidence intervals will be reported as the measure of ' 'discriminative performance of the 6MWT for PPC prediction. ' 'The level of significance is set at \u03b1\u00a0=\u00a00.05 (two-sided).') elif 'Risk Scores' in txt and ('ARISCAT' in txt or 'Gupta' in txt): set_text(child, '\u2022\u2002Risk Scores: ASA Physical Status classification and ARISCAT score ' 'computed from seven preoperative variables at the time of enrollment.') elif txt.startswith('Sample size was estimated'): set_text(child, 'Sample size was estimated for the primary outcome of PPC occurrence using data ' 'from Soares and Nucci (2021), which reported a PPC incidence of 30% in patients ' 'with 6MWD \u2265400\u00a0m and 65% in those with 6MWD <400\u00a0m. With a ' 'two-sided alpha of 0.05, power of 80%, and 1:1 ratio, the base sample size is ' '31 per group (62 total). Adjusting for 1 key covariate (operative duration) at ' '10 events per variable yields an adjusted total of 72; accounting for 5% loss ' 'to follow-up gives a final target of \u224876 participants.') elif txt.startswith('The results of this study are expected to establish'): set_text(child, 'The results of this study are expected to establish whether preoperative 6MWD ' 'independently predicts PPCs following major abdominal surgery and to identify ' 'a locally validated, clinically actionable cut-off value. If validated, the 6MWT ' 'will provide Filipino clinicians with a simple, low-cost preoperative risk ' 'stratification tool to guide targeted perioperative interventions and optimize ' 'patient outcomes.') doc1.save(OUT1) print(f'Study 1 saved: {OUT1}') # ───────────────────────────────────────────────────────────────────────────── # STUDY 2 # ───────────────────────────────────────────────────────────────────────────── doc2 = Document(SRC) body2 = doc2.element.body new_refs_done = False for child in list(body2): if child.tag != f'{WNS}p': continue txt = get_text(child) if txt.startswith('Prognostic Value of the Six-Minute Walk Test in Predicting'): set_text(child, 'Comparative Accuracy of the Six-Minute Walk Test, ARISCAT Score, and Gupta ' 'Respiratory Failure Index in Predicting Postoperative Pulmonary Complications ' 'Among Adults Undergoing Major Abdominal Surgery: A Prospective Cohort Study') elif txt.startswith('Major abdominal surgery carries a significant risk of postoperative'): set_text(child, 'Major abdominal surgery carries a significant risk of postoperative pulmonary ' 'complications (PPCs), contributing substantially to perioperative morbidity and ' 'mortality. Several preoperative risk stratification tools have been developed, ' 'including the ARISCAT score — a validated seven-variable clinical scoring system ' '— and the Gupta Respiratory Failure Index (Gupta RFI), derived from the ACS-NSQIP ' 'multicenter database (n\u00a0=\u00a0211,410). Despite their validation in Western ' 'surgical populations, comparative data on their performance against objective ' 'functional capacity measures, particularly the Six-Minute Walk Test (6MWT), ' 'remain sparse, especially in Southeast Asian settings.') elif txt.startswith('This study investigates the prognostic value of the Six-Minute Walk Test'): set_text(child, 'This study directly compares the discriminative accuracy of the preoperative 6MWT ' 'with the ARISCAT score and Gupta RFI in predicting PPCs following major abdominal ' 'surgery. The 6MWT — measuring the six-minute walk distance (6MWD) as an objective ' 'proxy of cardiorespiratory reserve — is hypothesized to provide comparable or ' 'superior discriminative accuracy compared to established clinical scoring tools ' 'that do not incorporate functional capacity.') elif txt.startswith('This will be a prospective cohort study at a tertiary hospital involving'): set_text(child, 'This will be a prospective cohort study at a tertiary hospital. All three risk ' 'tools (6MWT, ARISCAT score, Gupta RFI) will be administered preoperatively to ' 'each enrolled adult patient scheduled for elective major abdominal surgery. ' 'Participants will be followed for 30 days postoperatively, and the discriminative ' 'accuracy of each tool for predicting PPCs will be compared using AUROC analysis. ' 'If the 6MWT demonstrates comparable or superior AUROC, it would support its ' 'adoption as a practical, low-cost alternative that does not require scoring ' 'calculations and is immediately feasible in any hospital corridor.') elif txt.startswith('1. Determine the prognostic value'): set_text(child, '1. Compare the discriminative accuracy (AUROC) of the preoperative 6MWT, ' 'ARISCAT score, and Gupta RFI in predicting PPCs following major abdominal surgery.') elif txt.startswith('2. Identify an optimal 6MWD cut-off'): set_text(child, '2. Determine whether the 6MWT provides additive predictive value over established ' 'clinical scoring tools when combined in logistic regression models.') elif txt.startswith('3. Contribute locally relevant evidence'): set_text(child, '3. Provide the first locally validated head-to-head comparison of the three tools ' 'in a Filipino major abdominal surgery population.') elif txt.startswith('Cardiopulmonary exercise testing (CPET) remains the gold standard'): set_text(child, 'Several preoperative risk stratification tools for PPCs have been developed and ' 'validated in large surgical populations. The ARISCAT score, derived and validated ' 'in a Spanish multicenter cohort, incorporates seven variables: age, preoperative ' 'SpO2, recent respiratory infection, anemia, surgical incision site, duration of ' 'surgery, and emergency procedure. It stratifies patients into low, intermediate, ' 'and high risk for PPCs [15]. External validation in a Danish emergency abdominal ' 'surgery cohort showed good discrimination (AUC 0.83) and calibration [10]. ' 'In the large international STARSurg/TASMAN validation study of 11,591 patients, ' 'the ARISCAT score showed the highest AUROC among six models tested ' '(0.700; 95%\u00a0CI: 0.683\u20130.717) but fell below the threshold for good ' 'discrimination (AUROC \u22650.70) [10]. The Gupta Respiratory Failure Index, ' 'derived from the ACS-NSQIP database, predicts postoperative respiratory failure ' 'using five variables: type of surgery, emergency case, dependent functional ' 'status, preoperative sepsis, and ASA class, with a c-statistic of 0.894 in its ' 'development and validation datasets [16]. Both tools rely exclusively on clinical ' 'and administrative variables, without any measure of functional reserve.') elif txt.startswith('Existing evidence supports the association between reduced preoperative'): set_text(child, 'The 6MWT directly and objectively captures what these scoring systems do not: ' 'a patient\'s functional cardiorespiratory and musculoskeletal reserve. ' 'Existing evidence demonstrates that a reduced preoperative 6MWD independently ' 'predicts PPCs across abdominal, esophageal, and liver transplant surgical ' 'populations [4,5,6,7,8]. However, no prospective study has directly compared ' 'the 6MWT against the ARISCAT score and the Gupta RFI using AUROC analysis in ' 'a head-to-head design. Such a comparison is needed to determine whether the ' 'additional physiologic information captured by the 6MWT translates into ' 'meaningfully better risk discrimination, and whether it can replace or supplement ' 'these clinical scoring tools in routine practice.') elif txt.startswith('In the local context, there are no published studies'): set_text(child, 'In the Philippine setting, neither the ARISCAT score nor the Gupta RFI has been ' 'validated in a local surgical population, and the 6MWT has not been formally ' 'evaluated as a preoperative risk tool. This study will provide the first locally ' 'generated comparative data, yielding evidence directly applicable to Filipino ' 'patients and perioperative care in resource-limited settings.') elif txt.startswith('To determine the prognostic value of the preoperative Six-Minute Walk Distance'): set_text(child, 'To compare the discriminative accuracy of the preoperative Six-Minute Walk Test ' '(6MWT), ARISCAT score, and Gupta Respiratory Failure Index in predicting ' 'postoperative pulmonary complications among adults undergoing major abdominal ' 'surgery at a tertiary hospital.') elif txt.startswith('1. To describe the baseline clinical and functional characteristics'): set_text(child, '1. To describe the baseline clinical and functional characteristics of adult ' 'patients undergoing major abdominal surgery, including preoperative 6MWD, ' 'ARISCAT score, Gupta RFI, comorbidities, BMI, and ASA classification.') elif txt.startswith('2. To determine the association between preoperative 6MWD'): set_text(child, '2. To measure the discriminative accuracy (AUROC) of the 6MWT, ARISCAT score, ' 'and Gupta RFI for predicting PPCs within 30 days of major abdominal surgery.') elif txt.startswith('3. To evaluate the relationship between preoperative 6MWD and secondary outcomes'): set_text(child, '3. To perform pairwise AUROC comparisons (6MWT vs. ARISCAT; 6MWT vs. Gupta RFI) ' 'using the DeLong method to determine whether differences in discriminative ' 'performance are statistically significant.') elif txt.startswith('4. To identify an optimal preoperative 6MWD cut-off'): set_text(child, '4. To assess whether combining the 6MWT with ARISCAT or Gupta RFI provides ' 'additive discriminative accuracy (net reclassification improvement, NRI; ' 'integrated discrimination improvement, IDI).') elif txt.startswith('5. To assess the discriminative accuracy') and 'ARISCAT' in txt: set_text(child, '5. To evaluate the association of each risk tool with secondary outcomes: ' 'length of hospital stay, unplanned ICU admission, need for mechanical or ' 'non-invasive ventilation, and in-hospital mortality.') elif 'Risk Scores' in txt and ('ARISCAT' in txt or 'Gupta' in txt): set_text(child, '\u2022\u2002Risk Scores: (a) ARISCAT score — computed from seven preoperative ' 'variables (age, SpO2, recent respiratory infection, anemia, incision site, ' 'operative duration, emergency procedure); (b) Gupta Respiratory Failure Index ' '— computed from five variables (surgery type, emergency case, functional status, ' 'preoperative sepsis, ASA class); (c) ASA Physical Status classification.') elif txt.startswith('Descriptive statistics will summarize baseline'): set_text(child, 'Descriptive statistics will summarize baseline clinical and functional ' 'characteristics. Categorical variables will be reported as frequencies and ' 'percentages; continuous variables as mean\u00a0\u00b1\u00a0SD or median\u00a0(IQR). ' 'Univariable comparisons between patients who develop PPCs and those who do not ' 'will use Chi-squared / Fisher\'s exact test and t-test / Mann-Whitney U test ' 'as appropriate.') elif txt.startswith('The primary hypothesis will be tested using multivariable binary logistic'): set_text(child, 'The primary analysis compares the discriminative accuracy of the three ' 'preoperative risk tools. ROC curves will be constructed for the 6MWT (using ' '6MWD as a continuous variable), ARISCAT score (total points), and Gupta RFI ' '(predicted probability), each against 30-day PPC occurrence as the reference ' 'standard. AUROCs with 95%\u00a0CIs will be calculated for each tool. Pairwise ' 'AUROC comparisons will be performed using the DeLong method.') elif txt.startswith('Receiver Operating Characteristic (ROC) curve analysis will be used'): set_text(child, 'For the 6MWT, the Youden index will identify the optimal 6MWD cut-off for PPC ' 'prediction. Net reclassification improvement (NRI) and integrated discrimination ' 'improvement (IDI) will assess the additive value of combining the 6MWT with ' 'either scoring tool. Multivariable logistic regression, adjusting for operative ' 'duration and procedure type, will evaluate the independent association of each ' 'tool with PPC occurrence. The level of significance is set at ' '\u03b1\u00a0=\u00a00.05 (two-sided).') elif txt.startswith('Sample size was estimated'): set_text(child, 'Sample size was estimated to power the primary AUROC comparison between the ' '6MWT and ARISCAT score. Based on the STARSurg/TASMAN study reporting an ARISCAT ' 'AUROC of 0.700 [10], and assuming the 6MWT achieves an AUROC of 0.80 [4,5], ' 'with a null AUROC of 0.70, type\u00a0I error of 0.05, and power of 80%, the ' 'estimated required sample is approximately 84 patients. Accounting for 5% ' 'loss to follow-up gives a final target of \u224889 participants, estimated ' 'using the Hanley-McNeil method for comparison of two correlated AUROCs.') elif txt.startswith('The results of this study are expected to establish'): set_text(child, 'The results will determine whether the 6MWT provides comparable or superior ' 'discriminative accuracy compared to the ARISCAT score and Gupta RFI for ' 'predicting PPCs following major abdominal surgery. If the 6MWT demonstrates ' 'non-inferior or superior AUROC, it would support its adoption as a primary ' 'preoperative risk stratification tool — particularly in settings where scoring ' 'tool computation is less practical but a 30-meter corridor is readily available. ' 'This study will also provide the first comparative validation of all three tools ' 'in a Filipino surgical population.') # Add new references [15] and [16] after [14] elif txt.startswith('[14]') and not new_refs_done: r15 = insert_after(child, '[15] Canet J, Gallart L, Gomar C, Paluzie G, Valles J, Castillo J, et al. ' 'Prediction of postoperative pulmonary complications in a population-based surgical ' 'cohort. Anesthesiology. 2010 Dec;113(6):1338-50. ' 'doi: 10.1097/ALN.0b013e3181fc6e0a. PMID: 21045639.') insert_after(r15, '[16] Gupta H, Gupta PK, Fang X, Miller WJ, Cemaj S, Forse RA, et al. ' 'Development and validation of a risk calculator predicting postoperative ' 'respiratory failure. Chest. 2011 Nov;140(5):1207-15. ' 'doi: 10.1378/chest.11-0466. PMID: 21757571.') new_refs_done = True # Add ARISCAT and Gupta RFI rows to operational definitions table in Study 2 from docx.shared import Pt for table in doc2.tables: if len(table.rows) >= 5 and 'Variable' in table.rows[0].cells[0].text: r = table.add_row() r.cells[0].text = 'ARISCAT Score' r.cells[1].text = ( 'A seven-variable preoperative clinical scoring tool classifying PPC risk as low ' '(<26 points), intermediate (26\u201344), or high (\u226545). Variables: age, ' 'preoperative SpO2, recent respiratory infection, anemia (Hgb <10 g/dL), ' 'surgical incision site, operative duration, and emergency procedure. ' 'Administered at preoperative assessment.') for ci in [0,1]: for rn in r.cells[ci].paragraphs[0].runs: rn.font.size = Pt(11); rn.font.name = 'Times New Roman' r2 = table.add_row() r2.cells[0].text = 'Gupta Respiratory Failure Index (Gupta RFI)' r2.cells[1].text = ( 'A validated preoperative risk calculator derived from the ACS-NSQIP database ' '(n\u00a0=\u00a0211,410) predicting probability of postoperative respiratory ' 'failure (mechanical ventilation >48\u00a0h or unplanned intubation within 30 days). ' 'Five predictors: type of surgery, emergency case, dependent functional status, ' 'preoperative sepsis, and ASA class. Outputs a probability (0\u20131). ' 'Available at: www.riskcalculator.facs.org.') for ci in [0,1]: for rn in r2.cells[ci].paragraphs[0].runs: rn.font.size = Pt(11); rn.font.name = 'Times New Roman' print('Added ARISCAT and Gupta RFI to operational definitions table (Study 2).') break doc2.save(OUT2) print(f'Study 2 saved: {OUT2}') # Verify for path, label in [(OUT1,'Study 1'),(OUT2,'Study 2')]: d = Document(path) title = [p.text for p in d.paragraphs if p.text.strip() and 'Walk Test' in p.text] print(f'\n{label} title: {title[0][:90] if title else "NOT FOUND"}') PYEOF

cat << 'PYEOF' > /tmp/workspace/6mwt-proposal/build_study1_fresh.py from docx import Document from docx.shared import Pt, Inches from docx.enum.text import WD_ALIGN_PARAGRAPH doc = Document() for section in doc.sections: section.top_margin = Inches(1.0) section.bottom_margin = Inches(1.0) section.left_margin = Inches(1.25) section.right_margin = Inches(1.0) style = doc.styles['Normal'] style.font.name = 'Times New Roman' style.font.size = Pt(12) style.paragraph_format.space_after = Pt(0) style.paragraph_format.space_before = Pt(0) def p(text='', bold=False, italic=False, sz=12, align=WD_ALIGN_PARAGRAPH.JUSTIFY, sb=0, sa=6, li=0, fi=0): para = doc.add_paragraph() para.alignment = align para.paragraph_format.space_before = Pt(sb) para.paragraph_format.space_after = Pt(sa) para.paragraph_format.left_indent = Inches(li) para.paragraph_format.first_line_indent = Inches(fi) if text: r = para.add_run(text) r.font.name = 'Times New Roman'; r.font.size = Pt(sz) r.bold = bold; r.italic = italic return para def sec(num, title): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.LEFT para.paragraph_format.space_before = Pt(12) para.paragraph_format.space_after = Pt(6) r = para.add_run(f'{num}. {title}') r.font.name = 'Times New Roman'; r.font.size = Pt(12); r.bold = True def sub(num, title): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.LEFT para.paragraph_format.space_before = Pt(8) para.paragraph_format.space_after = Pt(4) r = para.add_run(f'{num} {title}') r.font.name = 'Times New Roman'; r.font.size = Pt(12); r.bold = True def bullet(label, text, li=0.35): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY para.paragraph_format.space_after = Pt(4) para.paragraph_format.left_indent = Inches(li) r1 = para.add_run(label); r1.font.name='Times New Roman'; r1.font.size=Pt(12); r1.bold=True r2 = para.add_run(text); r2.font.name='Times New Roman'; r2.font.size=Pt(12) def dot(label, text, li=0.4): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY para.paragraph_format.space_after = Pt(4) para.paragraph_format.left_indent = Inches(li) r1 = para.add_run(u'\u2022 ' + label); r1.font.name='Times New Roman'; r1.font.size=Pt(12); r1.bold=True r2 = para.add_run(text); r2.font.name='Times New Roman'; r2.font.size=Pt(12) def ref_e(num, text): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY para.paragraph_format.space_after = Pt(4) para.paragraph_format.left_indent = Inches(0.4) para.paragraph_format.first_line_indent = Inches(-0.4) r1 = para.add_run(num + ' '); r1.font.name='Times New Roman'; r1.font.size=Pt(12) r2 = para.add_run(text); r2.font.name='Times New Roman'; r2.font.size=Pt(12) # ════════════════════════════════════════════════════════════════════════════ # HEADER # ════════════════════════════════════════════════════════════════════════════ p('DATABANK INFO NEEDED:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=2) p('No Yes', align=WD_ALIGN_PARAGRAPH.LEFT, sa=2) p('1. R&B Form No. 1A', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=4) p('St. Luke\'s Medical Center', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sa=2) p('RESEARCH AND BIOTECHNOLOGY', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sa=2) p('1.1. CLINICAL RESEARCH PROPOSAL', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sa=10) p('Research Title:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('Preoperative Six-Minute Walk Distance as a Predictor of Postoperative Pulmonary ' 'Complications in Adults Scheduled for Major Abdominal Surgery: A Prospective Cohort Study', align=WD_ALIGN_PARAGRAPH.LEFT, sa=10) p('Investigators:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) tbl = doc.add_table(rows=4, cols=2) tbl.style = 'Table Grid' tbl.columns[0].width = Inches(3.5); tbl.columns[1].width = Inches(2.5) for i, h in enumerate(['Name and Signature', 'Unit/Position']): c = tbl.cell(0, i); c.text = h for r in c.paragraphs[0].runs: r.bold=True; r.font.size=Pt(11); r.font.name='Times New Roman' rows_data = [ ('Project Leader/s:\n\n_______________________________', 'Consultant\n(Consultant/Manager/Faculty)'), ('Co-Project Leader/s:\n\n_______________________________', 'Pulmonary Fellow\n(Resident/Fellow/Student)'), ('Research Fellow:\n\n_______________________________', ''), ] for i,(n,u) in enumerate(rows_data): tbl.cell(i+1,0).text=n; tbl.cell(i+1,1).text=u for ci in [0,1]: for r in tbl.cell(i+1,ci).paragraphs[0].runs: r.font.size=Pt(11); r.font.name='Times New Roman' doc.add_paragraph() p('Inst./Dept./Center/Group: Department of Surgery / Anesthesiology', align=WD_ALIGN_PARAGRAPH.LEFT, sa=12) # ════════════════════════════════════════════════════════════════════════════ # 2. BRIEF DESCRIPTION # ════════════════════════════════════════════════════════════════════════════ sec('2', 'Brief Description / Summary') p('Among patients undergoing major abdominal surgery, postoperative pulmonary complications ' '(PPCs) — including pneumonia, respiratory failure, atelectasis, and pleural effusion — ' 'account for a disproportionate share of hospital deaths, prolonged admissions, and unplanned ' 'intensive care unit (ICU) transfers. Despite their clinical burden, preoperative identification ' 'of patients who will develop these complications remains imprecise. The tools currently in ' 'widespread use either lack an objective measure of physical fitness or require laboratory and ' 'scoring inputs that are not uniformly available at the bedside.', sa=6) p('The six-minute walk test (6MWT) is a field walking test in which a patient traverses a ' 'flat, 30-meter corridor for six minutes, with the total distance walked (6MWD) serving as ' 'the output metric. Because the test engages the cardiovascular, respiratory, neuromuscular, ' 'and metabolic systems simultaneously, the resulting 6MWD reflects the same physiologic ' 'reserve that determines a patient\'s capacity to survive the systemic stress of surgery. ' 'The test requires no laboratory analysis, no specialized equipment, and fewer than ten ' 'minutes to administer — making it uniquely suitable for routine preoperative use at any ' 'level of healthcare facility.', sa=6) p('This study proposes a prospective cohort design in which eligible adults presenting for ' 'elective major abdominal surgery at a tertiary center will undergo a standardized 6MWT ' 'two to seven days before their procedure. The cohort will be followed for thirty days ' 'postoperatively. The primary question is whether preoperative 6MWD independently predicts ' 'the occurrence of PPCs, after controlling for clinically relevant confounders. An ROC-derived ' 'cut-off will be generated to give clinicians an actionable threshold for identifying patients ' 'who warrant preoperative optimization. The broader goal is to establish a locally validated, ' 'cost-free risk stratification pathway applicable to the Philippine perioperative context.', sa=12) # ════════════════════════════════════════════════════════════════════════════ # 3. INTRODUCTION # ════════════════════════════════════════════════════════════════════════════ sec('3', 'Introduction') # ─── 3.1 ───────────────────────────────────────────────────────────────────── sub('3.1.', 'Significance of the Project') p('Abdominal surgery occupies a distinct position on the perioperative risk spectrum. ' 'Procedures such as colectomy, hepatectomy, Whipple\'s pancreaticoduodenectomy, and ' 'gastrectomy require large incisions or prolonged pneumoperitoneum that mechanically ' 'impair diaphragmatic excursion, reduce functional residual capacity, and predispose ' 'the lower lung zones to collapse and secretion retention. These physiologic insults, ' 'superimposed on a baseline population that frequently carries diabetes, hypertension, ' 'chronic lung disease, and nutritional deficiency, explain why PPCs complicate 9%–40% ' 'of major abdominal procedures depending on the study population and outcome definition ' 'employed [1,2].', sa=6) p('The consequences extend well beyond the respiratory system. Patients who develop a PPC ' 'face hospital stays that are two to three times longer than those without complications, ' 'substantially higher rates of ICU admission and mechanical ventilation, and a mortality ' 'risk that can exceed 25% in the most severe presentations [2,10]. From a public health ' 'standpoint, each prevented PPC represents not only a life protected but also a measurable ' 'reduction in healthcare resource consumption — a priority in any setting where intensive ' 'care beds are finite.', sa=6) p('The case for preoperative risk stratification rests on a straightforward premise: patients ' 'identified as high risk before surgery can receive targeted interventions — inspiratory ' 'muscle training, physiotherapy, nutritional prehabilitation, adjusted anesthetic technique ' '— that reduce the probability of postoperative pulmonary events. A meta-analysis by Boden ' 'et al. (2024) showed that even a single preoperative physiotherapy session reduced the ' 'odds of PPCs by 47% (OR 0.53; 95%\u00a0CI 0.34\u20130.85) in adults undergoing elective ' 'abdominal surgery [11]. The prerequisite for capturing this benefit is the ability to ' 'identify who is at risk \u2014 which demands a valid, practical, and accessible ' 'preoperative risk tool.', sa=6) p('The three study aims follow from this reasoning:', sa=4) for num, text in [ ('1.\t', 'To determine whether preoperative 6MWD independently predicts PPCs in adults ' 'undergoing major abdominal surgery at a tertiary center.'), ('2.\t', 'To derive an ROC-based 6MWD cut-off with clinical utility for bedside ' 'identification of high-risk surgical patients.'), ('3.\t', 'To generate locally relevant data that can inform evidence-based preoperative ' 'assessment protocols in Philippine surgical centers.'), ]: bullet(num, text, li=0.3) doc.add_paragraph() # ─── 3.2 ───────────────────────────────────────────────────────────────────── sub('3.2.', 'Rationale for Doing the Study') p('Preoperative risk assessment in Philippine surgical practice currently hinges on a ' 'combination of clinical history, physical examination, and the attending anesthesiologist\'s ' 'subjective assignment of metabolic equivalent (MET) estimates. This approach is vulnerable ' 'to interviewer variability, patient recall bias, and systematic overestimation of functional ' 'capacity — all of which erode the precision of risk classification before it influences ' 'any clinical decision. Objective measurement is not the standard; it is the exception. ' 'Cardiopulmonary exercise testing (CPET), which provides the most reproducible physiologic ' 'characterization of preoperative fitness, remains confined to a handful of academic ' 'referral centers and is unavailable to the vast majority of surgical patients in ' 'this country [3].', sa=6) p('The 6MWT closes this gap directly. A 30-meter corridor, a stopwatch, and a trained ' 'observer are the only prerequisites. The resulting 6MWD integrates aerobic capacity, ' 'peripheral muscle strength, ventilatory efficiency, and motivational state into a ' 'single reproducible number that correlates strongly with peak oxygen uptake on formal ' 'CPET testing [3,13]. Critically, the 6MWT does what no clinical scoring system does: ' 'it captures how the body actually performs under physiologic stress, not how the ' 'patient describes their activity level in an outpatient interview.', sa=6) p('Prospective data from Soares and Nucci (2021) are directly applicable here. In 50 ' 'patients undergoing elective abdominal surgery, those who developed PPCs within seven ' 'postoperative days walked significantly shorter distances preoperatively ' '(444.8\u00a0m vs. 498.3\u00a0m; p\u00a0=\u00a00.013). After multivariable adjustment, ' 'each additional meter walked was independently protective against PPCs ' '(OR\u00a0=\u00a00.978; p\u00a0=\u00a00.010) [4]. Comparable findings were reported by ' 'Magalhaes et al. (2017) in a prospective cohort of 100 liver transplant recipients, ' 'where each 50\u00a0m gained on the preoperative 6MWT translated to a 41% reduction in ' 'the odds of postoperative respiratory complications ' '(OR\u00a0=\u00a00.589; 95%\u00a0CI\u00a00.357\u20130.971; p\u00a0=\u00a00.03) [5]. ' 'Taken together, these studies establish biological plausibility and statistical signal; ' 'what is missing is a prospective, protocolized study in a general major abdominal surgery ' 'population with standardized PPC definitions and locally derived cut-off values.', sa=6) p('This study is designed to fill precisely that gap in a Filipino tertiary hospital cohort.', sa=12) # ─── 3.3 ───────────────────────────────────────────────────────────────────── sub('3.3.', 'Background Information and Brief Literature Review') p('Surgery of the abdominal viscera consistently appears among the highest-risk procedural ' 'categories in perioperative medicine. The physiologic disruption begins in the operating ' 'room: general anesthesia attenuates hypoxic pulmonary vasoconstriction, reduces functional ' 'residual capacity, and promotes microatelectasis that may persist for hours to days ' 'postoperatively. Abdominal incisions compound these effects by directly limiting inspiratory ' 'effort and cough effectiveness. The result is a post-surgical lung that is mechanically ' 'disadvantaged at precisely the moment when immune and inflammatory challenges from the ' 'operative field are greatest [1].', sa=6) p('Defining what constitutes a PPC has historically hampered research in this area. ' 'Outcome rates across published studies range from under 10% to over 40%, largely because ' 'different investigators apply different thresholds for hypoxemia, atelectasis, and ' 'pneumonia. The Standardised Endpoints in Perioperative Medicine consensus framework ' '(StEP-COMPAC) was developed specifically to address this heterogeneity, offering ' 'operationally precise criteria for pneumonia, respiratory failure, pleural effusion ' 'requiring drainage, bronchospasm, and clinically significant atelectasis. In the ' 'STARSurg/TASMAN international validation cohort of 11,591 patients undergoing major ' 'abdominal surgery, the StEP-COMPAC PPC rate was 7.8%; however, site-specific rates in ' 'upper abdominal surgery populations are considerably higher, with Garg et al. (2025) ' 'reporting 20.3% in a prospective cohort focused on this operative category [2,10].', sa=6) p('Existing clinical risk scores perform only modestly. The ARISCAT score, the most widely ' 'validated PPC prediction model, achieved an AUROC of 0.700 in the STARSurg/TASMAN ' 'international validation — precisely at the boundary of what is considered clinically ' 'adequate discrimination, and below the AUROC of 0.80 generally required for a tool ' 'to meaningfully change clinical practice [10]. Spirometry fares no better: a 2022 ' 'systematic review by Dankert et al. found inconclusive evidence for the use of ' 'pulmonary function tests in PPC prediction across non-thoracic surgery settings, ' 'with only tentative benefit in upper abdominal procedures [12]. The persistent gap ' 'between available tools and clinical need underscores the rationale for evaluating ' 'the 6MWT as an alternative or complementary risk marker.', sa=6) p('The physiologic mechanism linking reduced 6MWD to postoperative pulmonary risk is ' 'well characterized. Patients with limited exercise capacity operate with smaller ' 'cardiorespiratory reserves; the sudden, sustained increase in oxygen demand imposed ' 'by the surgical stress response outpaces their available capacity, driving relative ' 'tissue hypoxia, impaired mucociliary clearance, and vulnerability to respiratory ' 'failure [3]. This mechanism is not population-specific — it applies equally to ' 'patients with overt cardiac or pulmonary disease and to those who are deconditioned ' 'for other reasons, including sedentary lifestyle, malnutrition, or age-related ' 'sarcopenia. Rose et al. (2022) reviewed the evidence linking cardiorespiratory ' 'fitness to postoperative outcomes comprehensively, concluding that impaired CRF ' 'is an independent risk factor for mortality and morbidity and is the single most ' 'modifiable perioperative risk variable [3].', sa=6) p('The 6MWT was standardized by the American Thoracic Society in 2002 and has since ' 'accumulated a large normative dataset. A 2026 meta-analysis by Otadi and Malmir ' 'pooling 28 studies reported mean 6MWDs of 473\u00a0m in older men and 428\u00a0m in ' 'older women, with each additional year of age associated with approximately 10\u00a0m ' 'of reduced distance [13]. Reference data for Southeast Asian populations are available ' 'from Yeung et al. (2022), who reported a mean 6MWD of 578\u00a0m (\u00b175\u00a0m) ' 'in healthy Singaporeans aged 21\u201380 years, declining from 601\u00a0m in the ' 'youngest age group to 519\u00a0m in those aged 60\u201380 [14]. These Asian normative ' 'values are more applicable to a Filipino cohort than Western reference equations ' 'and provide the contextual baseline against which preoperative distances should ' 'be interpreted.', sa=6) p('The evidence specifically in abdominal surgery populations continues to grow. ' 'Soares and Nucci (2021) found the incidence of early PPCs in abdominal surgery ' 'reached 50% in their cohort, with each meter of preoperative 6MWD conferring ' 'independent protection against PPC occurrence [4]. Magalhaes et al. (2017) ' 'replicated this finding in liver transplant surgery, where the 6MWT outperformed ' 'spirometry as an independent predictor of postoperative respiratory complications [5]. ' 'In thoracic oncology settings, Inoue et al. (2020) identified a cut-off of ' '\u2264454\u00a0m for predicting major complications after esophageal cancer surgery ' '(sensitivity 71%, specificity 55%) [6], while Hattori et al. (2018) found a ' 'threshold of \u2264450\u00a0m for postoperative pneumonia after lung resection ' '(sensitivity 69%, specificity 71%) [7]. Makker et al. (2022) synthesized five ' 'studies in a meta-analysis, finding that 6MWT \u2265400\u00a0m was associated with ' 'lower complication rates in gastrointestinal cancer surgery ' '(OR\u00a0=\u00a00.38; 95%\u00a0CI\u00a00.15\u20130.95) [8]. Argillander et al. ' '(2022) concluded in a systematic review of older surgical patients that the 6MWT ' 'is a feasible proxy for CPET in estimating aerobic capacity, while calling for ' 'prospective studies with standardized protocols in broader, non-oncologic ' 'abdominal surgery populations [9].', sa=6) p('Across all these studies, several limitations recur: retrospective data collection, ' 'non-standardized 6MWT protocols, heterogeneous PPC definitions, and the absence of ' 'locally derived cut-off values applicable to Asian or Southeast Asian populations. ' 'The present study addresses each of these limitations through a prospective design, ' 'strict adherence to the ATS 2002 protocol, the StEP-COMPAC outcome framework, and ' 'enrollment of a Filipino surgical cohort.', sa=12) # ════════════════════════════════════════════════════════════════════════════ # 4. OBJECTIVES # ════════════════════════════════════════════════════════════════════════════ sec('4', 'Objectives') sub('4.1.', 'General Objective') p('To determine whether preoperative six-minute walk distance (6MWD) is independently ' 'associated with the occurrence of postoperative pulmonary complications within thirty ' 'days of major abdominal surgery among adults admitted to a tertiary hospital.', sa=10) sub('4.2.', 'Specific Objectives') spec_objs = [ ('1.\t', 'To characterize the preoperative clinical and functional profiles of enrolled ' 'patients, including 6MWD, BMI, smoking status, comorbidities, ASA Physical Status ' 'classification, and ARISCAT score.'), ('2.\t', 'To determine the independent association between preoperative 6MWD ' '(as a continuous variable) and PPC occurrence within 30 days of surgery, using ' 'multivariable logistic regression adjusting for operative duration and procedure type.'), ('3.\t', 'To determine the association between preoperative 6MWD and each secondary ' 'outcome: unplanned ICU admission, need for invasive or non-invasive mechanical ' 'ventilation, length of hospital stay, and in-hospital mortality.'), ('4.\t', 'To identify an optimal preoperative 6MWD cut-off value for PPC prediction ' 'using ROC curve analysis and the Youden index, and to report its sensitivity, ' 'specificity, positive predictive value, and negative predictive value.'), ] for num, text in spec_objs: bullet(num, text, li=0.3) doc.add_paragraph() # ════════════════════════════════════════════════════════════════════════════ # 5. METHODS # ════════════════════════════════════════════════════════════════════════════ sec('5', 'Methods') sub('5.1.', 'Type of Study, Time Period and Target Population') p('This is a prospective, single-center, observational cohort study enrolling consecutive ' 'eligible adults scheduled for elective major abdominal surgery at a tertiary hospital ' '(St. Luke\'s Medical Center). Each participant will undergo a standardized preoperative ' '6MWT and will be followed until hospital discharge or thirty days postoperatively, ' 'whichever occurs last.', sa=6) p('Time Period', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) for t in [ u'\u2022 Recruitment: Consecutive enrollment over a 12-month period following IRB approval.', u'\u2022 Follow-up: Each participant will be followed from the date of surgery through ' 'postoperative day 30, or until the date of discharge if the patient remains hospitalized ' 'beyond day 30, to ensure complete ascertainment of all outcome events.', ]: q = doc.add_paragraph() q.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY q.paragraph_format.space_after = Pt(4) q.paragraph_format.left_indent = Inches(0.35) r = q.add_run(t); r.font.name='Times New Roman'; r.font.size=Pt(12) doc.add_paragraph() sub('5.2.', 'Criteria for Subject Selection') p('5.2.1. Inclusion Criteria', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) inc = [ ('1.\t', 'Adult patients aged 18 years or older at the time of the planned procedure.'), ('2.\t', 'Scheduled for elective major abdominal surgery defined as an intraperitoneal ' 'operation anticipated to last 60 minutes or longer under general or regional anesthesia, ' 'including but not limited to colectomy, gastrectomy, hepatectomy, pancreatectomy, ' 'and small-bowel resection.'), ('3.\t', 'Independently ambulatory and capable of completing a six-minute walk test ' 'without a mobility aid that precludes standardized testing.'), ('4.\t', 'Expected to remain in-hospital with documented outcome data through discharge ' 'or death.'), ('5.\t', 'Capable of providing written informed consent prior to study enrollment.'), ] for n,t in inc: bullet(n,t) doc.add_paragraph() p('5.2.2. Exclusion Criteria', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) exc = [ ('1.\t', 'Unscheduled or emergency abdominal surgery, in which the preoperative window ' 'for 6MWT administration does not exist.'), ('2.\t', 'Pre-existing conditions that independently prevent safe ambulation or ' 'invalidate 6MWT results — including active lower-limb ischemia, severe osteoarthritis ' 'with weight-bearing restriction, hemiplegia, or recent lower-extremity fracture.'), ('3.\t', 'Resting oxygen saturation below 88% on room air, or hemodynamic instability ' 'at the time of planned testing, representing absolute contraindications to exercise.'), ('4.\t', 'Active unstable angina, acute decompensated heart failure, or an acute ' 'exacerbation of chronic obstructive pulmonary disease within four weeks of the ' 'scheduled procedure.'), ('5.\t', 'Re-operation during the same hospitalization as the index abdominal surgery.'), ('6.\t', 'Patients in whom essential baseline or outcome variables cannot be ' 'reliably obtained.'), ] for n,t in exc: bullet(n,t) doc.add_paragraph() sub('5.3.', 'Operational Definitions, if applicable') tbl2 = doc.add_table(rows=1, cols=2) tbl2.style = 'Table Grid' tbl2.columns[0].width = Inches(2.0); tbl2.columns[1].width = Inches(4.0) hdr2 = tbl2.rows[0].cells hdr2[0].text='Variable'; hdr2[1].text='Definition' for c in hdr2: for r in c.paragraphs[0].runs: r.bold=True; r.font.size=Pt(11); r.font.name='Times New Roman' op_defs = [ ('Major Abdominal Surgery', 'Any intraperitoneal procedure anticipated to last \u226560 minutes under general or regional anesthesia, ' 'including colorectal resection, gastrectomy, hepatobiliary surgery, pancreatectomy, and small-bowel resection.'), ('Six-Minute Walk Test (6MWT)', 'A standardized submaximal exercise test administered per the ATS 2002 guidelines. ' 'The patient walks as far as possible on a flat, 30-meter corridor for six minutes. ' 'Primary outcome is 6MWD in meters.'), ('Six-Minute Walk Distance (6MWD)', 'Total distance covered during the 6MWT. Measured in meters to the nearest meter. ' 'Used as a continuous predictor in regression and as a dichotomized variable at the ' 'ROC-derived cut-off.'), ('Postoperative Pulmonary Complication (PPC)', 'Any of the following within 30 days of surgery, per StEP-COMPAC consensus: ' '(a) pneumonia (new infiltrate + fever + leukocytosis + purulent secretions); ' '(b) respiratory failure (SpO\u2082 <90% on room air or MV >24\u00a0h); ' '(c) atelectasis requiring physiotherapy or bronchoscopy; ' '(d) pleural effusion requiring drainage; ' '(e) bronchospasm requiring bronchodilator treatment.'), ('Unplanned ICU Admission', 'Unscheduled transfer to the ICU at any point after return from the operating ' 'theater, not prespecified in the postoperative care plan.'), ('Mechanical Ventilation', 'Invasive ventilation via endotracheal tube or tracheostomy initiated or continued ' 'beyond 24 hours after surgery.'), ('Non-Invasive Ventilation', 'CPAP, BiPAP, or high-flow nasal cannula therapy initiated beyond the immediate ' 'post-anesthetic recovery period.'), ('Prolonged Hospitalization', 'Length of stay exceeding 14 days or the 75th percentile for the procedure type ' '(defined a priori per institutional data).'), ('In-Hospital Mortality', 'Death from any cause occurring during the index hospitalization.'), ('Reduced Functional Capacity', 'Preoperative 6MWD below the ROC-derived study threshold, or <400\u00a0m as the ' 'pre-specified literature benchmark.'), ] for var, defn in op_defs: row = tbl2.add_row() row.cells[0].text=var; row.cells[1].text=defn for ci in [0,1]: for r in row.cells[ci].paragraphs[0].runs: r.font.size=Pt(11); r.font.name='Times New Roman' p('Table 1: Operational Definitions', italic=True, sz=11, align=WD_ALIGN_PARAGRAPH.CENTER, sb=4, sa=8) sub('5.4.', 'Description of Study Procedure') p('5.4.1. For observational (prospective cohort) studies:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=4) p('5.4.1.1. Method of Subject Selection', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('Eligible patients will be identified prospectively from the operating theater schedule ' 'during the preoperative anesthesia assessment clinic visit, which routinely occurs ' 'two to seven days before elective surgery. All adults booked for qualifying abdominal ' 'procedures will be screened by the study team. Those who meet inclusion criteria, pass ' 'the safety screen for exercise testing, and provide written consent will be enrolled ' 'sequentially until the target sample size is reached.', sa=6) p('5.4.1.2. Data to Be Gathered', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('The following will be collected on a standardized Case Report Form:', sa=4) data_items = [ ('Demographics: ','Age (years), sex, height (cm), weight (kg), BMI (kg/m\u00b2).'), ('Comorbidities and Risk Factors: ', 'Hypertension, type 2 diabetes, ischemic heart disease, heart failure, COPD, ' 'obstructive sleep apnea, CKD (non-dialysis), active smoking, and pack-year history.'), ('Preoperative Risk Scores: ', 'ASA Physical Status (I\u2013V) assigned by anesthesiology; ARISCAT score computed ' 'from seven preoperative variables.'), ('6MWT Parameters: ', 'Total 6MWD (m); resting and post-test SpO\u2082 and heart rate; Borg dyspnea score ' 'before and after; reason for early termination (if applicable).'), ('Surgical and Anesthetic Data: ', 'Procedure type, operative approach (open / laparoscopic / robotic), anesthetic ' 'technique, operative duration (minutes), estimated blood loss (mL).'), ('Postoperative Primary Outcome: ', 'Occurrence of any PPC (Yes/No) within 30 days, with event type and date documented.'), ('Postoperative Secondary Outcomes: ', 'Unplanned ICU admission (Yes/No), invasive mechanical ventilation >24\u00a0h (Yes/No), ' 'non-invasive ventilation (Yes/No), length of stay (days), in-hospital mortality (Yes/No).'), ] for lbl, txt in data_items: dot(lbl, txt) doc.add_paragraph() p('5.4.1.3. Description of Procedures to Be Done to Subjects', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('A single standardized 6MWT will be administered to each participant during the ' 'preoperative assessment visit, performed by the principal investigator or a designated ' 'trained research associate following the ATS 2002 protocol. The test will take place ' 'on a flat, marked, 30-meter indoor corridor. The patient will receive a standard verbal ' 'briefing explaining the procedure and instruction to walk as fast and as far as ' 'comfortably possible, without running, for exactly six minutes. Standardized ' 'encouragement phrases will be delivered at each completed minute. Resting SpO\u2082 ' 'and heart rate will be measured by pulse oximetry before and immediately after the ' 'test. The Borg dyspnea scale will be administered before the test and at its conclusion. ' 'The test will be stopped before six minutes if any of the prespecified safety criteria ' 'are met. All other data — postoperative outcomes, operative details, length of stay ' '— will be collected through daily prospective chart review during hospitalization and ' 'a structured telephone or clinic follow-up at day 30.', sa=6) p('5.4.1.4. Instruments Used for Measuring Exposure and/or Outcome', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) instr = [ ('Exposure (6MWT): ', 'Marked 30-meter corridor; calibrated pulse oximeter; stopwatch; Borg dyspnea scale (0\u201310).'), ('Outcome (PPCs): ', 'Attending physician documentation in the electronic medical record, adjudicated against ' 'StEP-COMPAC criteria by two independent investigators blinded to 6MWD results.'), ('Risk Score Computation: ', 'ASA classification: assigned by attending anesthesiologist. ARISCAT score: computed ' 'from seven preoperative variables at time of enrollment.'), ('Data Abstraction: ', 'Standardized CRF serving as the primary data instrument, ensuring uniformity ' 'across all enrolled patients.'), ] for lbl, txt in instr: dot(lbl, txt) doc.add_paragraph() p('5.4.1.5. Method of Validating Measuring Instruments', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('The 6MWT protocol will be standardized before study commencement through a training ' 'session for all test administrators covering verbal briefing language, encouragement ' 'timing, pulse oximeter placement, and stopping criteria. Pulse oximeters will be ' 'maintained and calibrated per hospital biomedical engineering protocols. Outcome ' 'adjudication will be performed by two investigators independently and blinded to ' '6MWD values; disagreements will be resolved by a third reviewer. ' 'Inter-rater agreement for PPC classification will be assessed using Cohen\'s ' 'kappa prior to final analysis.', sa=6) p('5.4.1.6. Laboratory Procedures to Be Performed, if any', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('No study-specific laboratory tests will be ordered. Spirometry values (FEV\u2081, ' 'FVC, FEV\u2081/FVC) and routine admission laboratory results (CBC, serum albumin, ' 'creatinine) will be extracted from the medical record if obtained as part of standard ' 'preoperative care.', sa=6) p('5.4.1.7. Follow-Up Procedures', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('The research team will perform daily prospective review of the patient\'s electronic ' 'chart and nursing notes from the day of surgery. A structured review will be conducted ' 'at postoperative day 30; for patients discharged before day 30, outcome data will be ' 'gathered via standardized telephone interview and/or scheduled outpatient follow-up ' 'consultation.', sa=8) sub('5.5.', 'Description of Outcome Measures') p('The primary outcome is the occurrence of any postoperative pulmonary complication, ' 'as defined by the StEP-COMPAC framework, within 30 days of the index surgery. This ' 'outcome was selected because it represents the most clinically meaningful and ' 'mechanistically linked endpoint for preoperative 6MWT-based risk stratification: ' 'the same cardiorespiratory reserve measured by the 6MWT is the principal determinant ' 'of whether a patient can mount the ventilatory and immune response needed to prevent ' 'pulmonary complications in the postoperative period [4,5,11]. The 30-day window is ' 'consistent with standard perioperative outcome reporting.', sa=6) p('Secondary outcomes include: (a) length of hospital stay in days (continuous); ' '(b) unplanned ICU admission (dichotomous); (c) invasive mechanical ventilation ' 'beyond 24 hours postoperatively (dichotomous); (d) non-invasive ventilation or ' 'HFNC beyond the immediate recovery period (dichotomous); and (e) in-hospital ' 'mortality (dichotomous). These outcomes collectively capture the clinical trajectory ' 'and resource burden of postoperative pulmonary morbidity and are consistent with ' 'the outcome hierarchy used in comparable prospective studies [3,4,5,8].', sa=8) sub('5.6.', 'Sample Size Estimation') p('Sample size was calculated based on the difference in PPC incidence between patients ' 'with reduced and normal preoperative 6MWD. Using Soares and Nucci (2021) [4] as the ' 'primary reference — PPC rate of 65% in patients with 6MWD <400\u00a0m versus 30% ' 'in those with 6MWD \u2265400\u00a0m — with a two-sided alpha of 0.05 and power of ' '80%, the base sample size is 31 per group (62 total, 1:1 allocation). Adjusting for ' 'one additional covariate (operative duration) at 10 events per variable adds ' '10 participants, yielding an adjusted subtotal of 72. To account for a 5% prospective ' 'loss to follow-up inherent even in inpatient cohorts, the final target sample size ' 'is \u224876 participants. Sample size was computed using standard formulas for ' 'comparison of two proportions.', sa=8) sub('5.7.', 'Data Analysis') p('Baseline characteristics will be presented using standard descriptive statistics. ' 'Normally distributed continuous variables will be expressed as mean\u00a0\u00b1\u00a0SD; ' 'non-normally distributed variables will be expressed as median\u00a0(interquartile range). ' 'Categorical variables will be reported as frequencies and percentages. ' 'Between-group comparisons (PPC vs. no PPC) will use the independent-samples t-test or ' 'Mann-Whitney\u00a0U test for continuous variables and the chi-squared or Fisher\'s exact ' 'test for categorical variables, as appropriate.', sa=6) p('The primary hypothesis — that lower preoperative 6MWD is independently associated ' 'with PPC occurrence — will be tested by multivariable binary logistic regression with ' 'PPC occurrence as the dependent variable and 6MWD (continuous, per 10-meter decrement) ' 'as the primary independent variable, adjusting for operative duration and procedure ' 'type. Results will be expressed as adjusted odds ratios with 95%\u00a0confidence ' 'intervals.', sa=6) p('ROC curve analysis will be used to evaluate the overall discriminative ability of ' 'preoperative 6MWD for PPC prediction. The Youden index (sensitivity + specificity ' '\u2212 1) will identify the optimal cut-off value. The AUROC with 95%\u00a0CI will ' 'be reported as the primary measure of model performance. Sensitivity, specificity, ' 'positive predictive value (PPV), and negative predictive value (NPV) at the optimal ' 'cut-off will be reported. All analyses will be performed in SPSS v29.0 or R v4.3+. ' 'Statistical significance is defined as p\u00a0<\u00a00.05 (two-sided).', sa=8) sub('5.8.', 'Ethical Consideration') p('The study involves adult patients scheduled for elective surgery and carries minimal ' 'additional risk beyond the routine preoperative assessment. The 6MWT is a widely used, ' 'non-invasive clinical test with an established safety profile in medically supervised ' 'settings. No study-specific invasive procedures will be performed. ' 'Participation will not alter the planned surgical or anesthetic management of any ' 'participant in any way.', sa=6) p('5.8.1. Method/s of Dealing with Adverse Events', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('The test will be terminated immediately if the participant develops chest pain, ' 'severe or worsening dyspnea, dizziness, pallor, SpO\u2082 below 85%, or expresses ' 'a desire to stop. A clinician competent in basic life support will be present or ' 'immediately accessible during all test administrations. Any adverse event will be ' 'documented and reported to the Principal Investigator and, if a serious adverse event, ' 'to the SLMC IERC within the required timeframe.', sa=6) p('5.8.2. Anticipated Risks and Discomforts to Subjects', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('Risk to participants is minimal. The 6MWT involves sustained walking at a self-selected ' 'pace — an activity equivalent to or less demanding than walking in a hospital corridor ' 'for a routine preoperative clinic visit. Transient breathlessness and leg fatigue are ' 'expected and resolve within minutes of stopping. Pre-test safety screening will exclude ' 'patients for whom any exercise is contraindicated.', sa=6) p('5.8.3. Expected Benefits to the Subject and to Others', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('Direct benefit to participants is not guaranteed, as the 6MWT result will not ' 'automatically alter surgical planning. However, the study findings may inform future ' 'practice: if validated, the 6MWT would provide clinicians with a bedside tool to ' 'identify high-risk patients who could benefit from preoperative physiotherapy, ' 'prehabilitation, or enhanced monitoring. Society-level benefit includes locally ' 'relevant evidence that may reshape perioperative risk assessment protocols in ' 'Philippine surgical centers.', sa=6) p('5.8.4. Protection of Confidentiality', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=3) p('All study procedures will be conducted in accordance with the Declaration of Helsinki ' '(2013) and ICH-GCP guidelines. Prior to enrollment of any participant, the protocol ' 'will be reviewed and approved by the SLMC Institutional Ethics Review Committee. ' 'Each enrolled participant will be assigned a unique alphanumeric study code; no ' 'identifying information (name, date of birth, hospital number) will appear in any ' 'data file or report. Study data will be stored in a password-protected, ' 'AES-encrypted electronic file accessible only to the principal investigator and ' 'co-investigators. Source documents will be retained for a minimum of five years ' 'following study completion, after which they will be destroyed in compliance with ' 'institutional records management policy. Findings will be disseminated exclusively ' 'in aggregate form, with no individual patient identifiable from any publication ' 'or presentation.', sa=12) # ════════════════════════════════════════════════════════════════════════════ # 7. REFERENCES # ════════════════════════════════════════════════════════════════════════════ sec('7', 'References') all_refs = [ ('[1]', 'Smetana GW, Lawrence VA, Cornell JE; American College of Physicians. ' 'Preoperative pulmonary risk stratification for noncardiothoracic surgery: ' 'systematic review for the American College of Physicians. ' 'Ann Intern Med. 2006 Apr 18;144(8):581-95. ' 'doi: 10.7326/0003-4819-144-8-200604180-00009.'), ('[2]', 'Garg S, Govindaraj V, Dwivedi DP, Raja K, Theerthar EP. ' 'Postoperative pulmonary complications in patients undergoing upper abdominal ' 'surgery: risk factors and predictive models. ' 'Monaldi Arch Chest Dis. 2025 Mar 31. ' 'doi: 10.4081/monaldi.2024.2915. PMID: 38526466.'), ('[3]', 'Rose GA, Davies RG, Appadurai IR, Williams IM, Bashir M, Berg RMG. ' '\'Fit for surgery\': the relationship between cardiorespiratory fitness and ' 'postoperative outcomes. ' 'Exp Physiol. 2022 Aug;107(8):780-95. ' 'doi: 10.1113/EP090156. PMID: 35579479.'), ('[4]', 'Soares SMTP, Nucci LB. ' 'Association between early pulmonary complications after abdominal surgery ' 'and preoperative physical capacity. ' 'Physiother Theory Pract. 2021 Jul;37(7):852-9. ' 'doi: 10.1080/09593985.2019.1650404. PMID: 31402737.'), ('[5]', 'Magalhaes CBA, Nogueira IC, Marinho LS, Daher EF, Garcia JHP, Viana CFG. ' 'Exercise capacity impairment can predict postoperative pulmonary complications ' 'after liver transplantation. ' 'Respiration. 2017;94(6):538-44. ' 'doi: 10.1159/000479008. PMID: 28738386.'), ('[6]', 'Inoue T, Ito S, Kanda M, Niwa Y, Nagaya M, Nishida Y. ' 'Preoperative six-minute walk distance as a predictor of postoperative ' 'complication in patients with esophageal cancer. ' 'Dis Esophagus. 2020 Mar 5;33(3):doz050. ' 'doi: 10.1093/dote/doz050. PMID: 31111872.'), ('[7]', 'Hattori K, Matsuda T, Takagi Y, Nagaya M, Inoue T, Nishida Y. ' 'Preoperative six-minute walk distance is associated with pneumonia after ' 'lung resection. ' 'Interact Cardiovasc Thorac Surg. 2018 Feb 1;26(2):208-13. ' 'doi: 10.1093/icvts/ivx310. PMID: 29049742.'), ('[8]', 'Makker PGS, Koh CE, Solomon MJ, Steffens D. ' 'Preoperative functional capacity and postoperative outcomes following ' 'abdominal and pelvic cancer surgery: a systematic review and meta-analysis. ' 'ANZ J Surg. 2022 Jul;92(7-8):1732-40. ' 'doi: 10.1111/ans.17577. PMID: 35253333.'), ('[9]', 'Argillander TE, Heil TC, Melis RJF, van Duijvendijk P, Klaase JM, ' 'van Munster BC. Preoperative physical performance as predictor of ' 'postoperative outcomes in patients aged 65 and older scheduled for major ' 'abdominal cancer surgery: a systematic review. ' 'Eur J Surg Oncol. 2022 Mar;48(3):575-84. ' 'doi: 10.1016/j.ejso.2021.09.019. PMID: 34629224.'), ('[10]', 'STARSurg Collaborative and TASMAN Collaborative. ' 'Evaluation of prognostic risk models for postoperative pulmonary complications ' 'in adult patients undergoing major abdominal surgery: a systematic review ' 'and international external validation cohort study. ' 'Lancet Digit Health. 2022 Jul;4(7):e498-e507. ' 'doi: 10.1016/S2589-7500(22)00069-3. PMID: 35750401.'), ('[11]', 'Boden I, Reeve J, Jernas A, Denehy L, Fagevik Olsen M. ' 'Preoperative physiotherapy prevents postoperative pulmonary complications ' 'after major abdominal surgery: a meta-analysis of individual patient data. ' 'J Physiother. 2024 Jul;70(3):195-202. ' 'doi: 10.1016/j.jphys.2024.02.012. PMID: 38472053.'), ('[12]', 'Dankert A, Dohrmann T, Loser B, Zapf A, Zollner C, Petzoldt M. ' 'Pulmonary function tests for the prediction of postoperative pulmonary ' 'complications. ' 'Dtsch Arztebl Int. 2022 Feb 18;119(7):99-106. ' 'doi: 10.3238/arztebl.m2022.0074. PMID: 34939921.'), ('[13]', 'Otadi K, Malmir K. ' 'Normative reference values for the six-minute walk test in older adults: ' 'a systematic review and meta-analysis. ' 'Arch Gerontol Geriatr. 2026 Jan;128:106026. ' 'doi: 10.1016/j.archger.2025.106026. PMID: 41027267.'), ('[14]', 'Yeung MT, Chan MY, Huang KS, Chen TJ, Chia CP, Fong MM. ' 'Normative reference values and regression equations to predict the ' '6-minute walk distance in the Asian adult population aged 21-80 years. ' 'Hong Kong Physiother J. 2022 Dec;42(2):69-78. ' 'doi: 10.1142/S1013702522500111. PMID: 37560171.'), ] for num, txt in all_refs: ref_e(num, txt) doc.add_paragraph() fp = doc.add_paragraph() fp.alignment = WD_ALIGN_PARAGRAPH.RIGHT fp.paragraph_format.space_before = Pt(24) r = fp.add_run('Protocol Version 01 dated ________________') r.font.name='Times New Roman'; r.font.size=Pt(10) out = '/tmp/workspace/6mwt-proposal/Study1_6MWT_PPCs_FreshVersion.docx' doc.save(out) print(f'Saved: {out}') print(f'Paragraphs: {len(doc.paragraphs)}, Tables: {len(doc.tables)}') PYEOF python3 /tmp/workspace/6mwt-proposal/build_study1_fresh.py

python3 -c " from docx import Document doc = Document('/tmp/workspace/6mwt-proposal/Study1_6MWT_PPCs_FreshVersion.docx') for i,p in enumerate(doc.paragraphs): if p.text.strip(): print(f'{i:3d} [{p.style.name}] {p.text[:120]}') print('---TABLES---') for t in doc.tables: for r in t.rows[:3]: print([c.text[:60] for c in r.cells]) "

cat << 'PYEOF' > /tmp/workspace/6mwt-proposal/build_study1_medical.py from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.oxml import OxmlElement from docx.oxml.ns import qn doc = Document() # ── Page layout ───────────────────────────────────────────────────────────── for section in doc.sections: section.top_margin = Inches(1.0) section.bottom_margin = Inches(1.0) section.left_margin = Inches(1.25) section.right_margin = Inches(1.0) section.page_width = Inches(8.5) section.page_height = Inches(11.0) # ── Base font ──────────────────────────────────────────────────────────────── for sname in ['Normal','Body Text','Default Paragraph Font']: try: s = doc.styles[sname] s.font.name = 'Times New Roman' s.font.size = Pt(12) except: pass # ── Helpers ────────────────────────────────────────────────────────────────── TNR = 'Times New Roman' def add_run(para, text, bold=False, italic=False, sz=12, underline=False): r = para.add_run(text) r.font.name = TNR; r.font.size = Pt(sz) r.bold = bold; r.italic = italic; r.underline = underline return r def body(text='', bold=False, italic=False, align=WD_ALIGN_PARAGRAPH.JUSTIFY, sb=0, sa=8, li=0.0, fi=0.0, sz=12): para = doc.add_paragraph() para.alignment = align para.paragraph_format.space_before = Pt(sb) para.paragraph_format.space_after = Pt(sa) if li: para.paragraph_format.left_indent = Inches(li) if fi: para.paragraph_format.first_line_indent = Inches(fi) if text: r = para.add_run(text) r.font.name=TNR; r.font.size=Pt(sz) r.bold=bold; r.italic=italic return para def heading1(num, title): """Numbered primary section heading — bold, left, 12pt, rule-style spacing.""" para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.LEFT para.paragraph_format.space_before = Pt(14) para.paragraph_format.space_after = Pt(4) add_run(para, f'{num}. {title.upper()}', bold=True, sz=12) return para def heading2(num, title): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.LEFT para.paragraph_format.space_before = Pt(10) para.paragraph_format.space_after = Pt(3) add_run(para, f'{num} {title}', bold=True, sz=12) return para def heading3(num, title): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.LEFT para.paragraph_format.space_before = Pt(8) para.paragraph_format.space_after = Pt(2) add_run(para, f'{num} {title}', bold=True, italic=True, sz=12) return para def numbered_item(num_text, body_text, li=0.35, hanging=0.35): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY para.paragraph_format.space_after = Pt(4) para.paragraph_format.left_indent = Inches(li) para.paragraph_format.first_line_indent = Inches(-hanging) r1 = para.add_run(num_text + '\t') r1.font.name=TNR; r1.font.size=Pt(12) r2 = para.add_run(body_text) r2.font.name=TNR; r2.font.size=Pt(12) return para def bullet_item(label, text, li=0.4, bold_label=True): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY para.paragraph_format.space_after = Pt(4) para.paragraph_format.left_indent = Inches(li) para.paragraph_format.first_line_indent = Inches(-0.2) r0 = para.add_run('\u2022 ') r0.font.name=TNR; r0.font.size=Pt(12) r1 = para.add_run(label) r1.font.name=TNR; r1.font.size=Pt(12); r1.bold=bold_label r2 = para.add_run(text) r2.font.name=TNR; r2.font.size=Pt(12) return para def ref_item(num, text): para = doc.add_paragraph() para.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY para.paragraph_format.space_after = Pt(3) para.paragraph_format.left_indent = Inches(0.4) para.paragraph_format.first_line_indent = Inches(-0.4) r1 = para.add_run(num + '\t'); r1.font.name=TNR; r1.font.size=Pt(11) r2 = para.add_run(text); r2.font.name=TNR; r2.font.size=Pt(11) return para def horiz_line(): para = doc.add_paragraph() para.paragraph_format.space_before = Pt(2) para.paragraph_format.space_after = Pt(2) pPr = para._p.get_or_add_pPr() pb = OxmlElement('w:pBdr') btm = OxmlElement('w:bottom') btm.set(qn('w:val'), 'single') btm.set(qn('w:sz'), '6') btm.set(qn('w:space'), '1') btm.set(qn('w:color'), '000000') pb.append(btm); pPr.append(pb) return para # ════════════════════════════════════════════════════════════════════════════ # COVER / HEADER # ════════════════════════════════════════════════════════════════════════════ p = body('', sa=2) add_run(p,'DATABANK INFO NEEDED:',bold=True) add_run(p,' \u2610 No \u2610 Yes') body('1. R&B Form No. 1A', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=4) body('ST. LUKE\'S MEDICAL CENTER', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sa=2, sz=13) body('RESEARCH AND BIOTECHNOLOGY DIVISION', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sa=2, sz=12) horiz_line() body('1.1. CLINICAL RESEARCH PROPOSAL', bold=True, align=WD_ALIGN_PARAGRAPH.CENTER, sb=6, sa=12, sz=12) # Title block p_title_label = body('RESEARCH TITLE:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=2) body( 'Preoperative Six-Minute Walk Distance as an Independent Predictor of ' 'Postoperative Pulmonary Complications in Adults Undergoing Elective Major ' 'Abdominal Surgery: A Prospective Observational Cohort Study', align=WD_ALIGN_PARAGRAPH.LEFT, sb=0, sa=10, sz=12 ) # Investigators table body('INVESTIGATORS:', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=4) inv_tbl = doc.add_table(rows=4, cols=3) inv_tbl.style = 'Table Grid' inv_tbl.columns[0].width = Inches(0.9) inv_tbl.columns[1].width = Inches(3.0) inv_tbl.columns[2].width = Inches(2.6) for i, h in enumerate(['Role', 'Name / Signature', 'Designation / Department']): c = inv_tbl.cell(0,i); c.text = h for r in c.paragraphs[0].runs: r.bold=True; r.font.size=Pt(11); r.font.name=TNR rows_d = [ ('Principal Investigator', '_________________________________', 'Consultant\nDept. of Surgery / Anesthesiology'), ('Co-Investigator', '_________________________________', 'Fellow-in-Training\n(Pulmonology / Anesthesiology)'), ('Research Coordinator', '_________________________________', ''), ] for i,(role,name,desig) in enumerate(rows_d): for j,val in enumerate([role,name,desig]): inv_tbl.cell(i+1,j).text = val for rr in inv_tbl.cell(i+1,j).paragraphs[0].runs: rr.font.size=Pt(11); rr.font.name=TNR body('', sa=4) p_dept = body('', align=WD_ALIGN_PARAGRAPH.LEFT, sa=4) add_run(p_dept,'Department / Center: ', bold=True) add_run(p_dept,'Department of Surgery and Anesthesiology, St. Luke\'s Medical Center') p_date = body('', align=WD_ALIGN_PARAGRAPH.LEFT, sa=8) add_run(p_date,'Date of Submission: ', bold=True) add_run(p_date,'_______________________________') horiz_line() # ════════════════════════════════════════════════════════════════════════════ # 2. ABSTRACT / BRIEF DESCRIPTION # ════════════════════════════════════════════════════════════════════════════ heading1('2', 'Abstract / Brief Description') body( 'Background: Postoperative pulmonary complications (PPCs) are a leading source of ' 'perioperative morbidity and mortality following major abdominal surgery, with reported ' 'incidence rates of 9% to 40% depending on case mix and outcome definitions employed. ' 'Despite this clinical burden, no universally adopted, objective, low-cost screening ' 'tool exists for identifying at-risk patients in resource-limited settings.', sb=4, sa=4 ) body( 'Rationale: The six-minute walk test (6MWT) — a submaximal field exercise test yielding ' 'the six-minute walk distance (6MWD) in meters — is a validated, reproducible measure ' 'of integrated cardiorespiratory and functional reserve. Preliminary prospective evidence ' 'suggests that impaired preoperative 6MWD independently predicts PPC occurrence; however, ' 'no study has prospectively evaluated this association in a general major abdominal surgery ' 'cohort using standardized outcome definitions.', sa=4 ) body( 'Objective: To determine the independent association between preoperative 6MWD and the ' 'occurrence of PPCs within 30 days of elective major abdominal surgery, and to derive a ' 'locally validated ROC-based 6MWD threshold for clinical risk stratification.', sa=4 ) body( 'Methods: Prospective observational cohort study. Adult patients scheduled for elective ' 'major abdominal surgery will undergo a standardized 6MWT two to seven days preoperatively, ' 'in accordance with the American Thoracic Society 2002 guidelines. The primary outcome ' 'is any PPC within 30 postoperative days, defined per the Standardised Endpoints in ' 'Perioperative Medicine consensus (StEP-COMPAC). Multivariable binary logistic regression ' 'will assess the independent association between 6MWD and PPCs, adjusting for operative ' 'duration and procedure type. ROC curve analysis with Youden\'s index will identify the ' 'optimal 6MWD cut-off. Estimated sample size: n\u00a0=\u00a076.', sa=4 ) body( 'Expected Outcome: Locally validated evidence on the predictive accuracy of preoperative ' '6MWD for PPCs in Filipino surgical patients, with a clinically actionable cut-off to ' 'guide preoperative risk stratification and targeted prehabilitation referrals.', sa=8 ) # ════════════════════════════════════════════════════════════════════════════ # 3. INTRODUCTION # ════════════════════════════════════════════════════════════════════════════ heading1('3', 'Introduction') # 3.1 heading2('3.1.', 'Significance and Burden of the Problem') body( 'Major abdominal surgery is associated with a high incidence of postoperative pulmonary ' 'complications (PPCs), encompassing pneumonia, respiratory failure, clinically significant ' 'atelectasis, pleural effusion requiring drainage, and bronchospasm. Across prospective ' 'registries and multicenter cohorts, the PPC incidence after intraperitoneal procedures ' 'ranges from 9% to 40%, with wide variation attributable to differences in case mix, ' 'anesthetic technique, and — most prominently — the operational criteria used to define ' 'a PPC.(1,2) This outcome heterogeneity has historically confounded risk stratification ' 'research and obscured the true epidemiologic burden.', sb=4, sa=6 ) body( 'The clinical consequences of PPCs extend substantially beyond the pulmonary system. ' 'Affected patients experience 2- to 3-fold longer hospital admissions, significantly ' 'elevated rates of unplanned intensive care unit (ICU) transfer, and in-hospital ' 'mortality rates that may exceed 25% in severe presentations.(2) From an institutional ' 'standpoint, each PPC event is associated with substantial incremental resource ' 'utilization — including nursing hours, pharmacy expenditure, and ventilator use — ' 'making PPC prevention a priority for both patient safety and healthcare efficiency.', sa=6 ) body( 'The logical corollary of this burden is the need for effective preoperative risk ' 'identification. Prospective studies consistently show that high-risk patients who ' 'receive targeted preoperative interventions — including inspiratory muscle training, ' 'physiotherapy, and nutritional optimization — experience significantly fewer ' 'postoperative pulmonary events. A meta-analysis by Boden et al. (2024) demonstrated ' 'that preoperative physiotherapy reduced PPC odds by 47% in elective abdominal surgery ' 'patients (OR 0.53; 95% CI 0.34\u20130.85).(11) This evidence base exists; the limiting ' 'factor in translating it to practice is the absence of a valid, accessible, ' 'low-cost tool for identifying who should receive these interventions.', sa=8 ) # 3.2 heading2('3.2.', 'Rationale and Research Gap') body( 'Preoperative risk assessment for PPCs in Philippine tertiary surgical practice currently ' 'relies on subjective estimation of functional capacity — typically expressed as metabolic ' 'equivalent tasks (METs) estimated from patient history — combined with clinical ' 'examination and anesthesiologist judgment. This approach is subject to systematic bias: ' 'studies consistently demonstrate that clinicians overestimate functional capacity when ' 'relying on self-reported activity, and that subjective MET estimation correlates poorly ' 'with objectively measured cardiorespiratory fitness.(3) The gold standard for objective ' 'preoperative fitness quantification — cardiopulmonary exercise testing (CPET) — ' 'generates a peak oxygen uptake (VO\u2082max) that robustly predicts perioperative ' 'outcomes, but its requirement for specialized equipment, trained personnel, and ' 'controlled laboratory conditions renders it inaccessible in the majority of Philippine ' 'surgical centers.(3)', sb=4, sa=6 ) body( 'The six-minute walk test (6MWT) offers a validated, field-based alternative. ' 'The test requires only a flat 30-meter corridor, a calibrated pulse oximeter, a ' 'stopwatch, and a trained administrator — infrastructure available at any level of ' 'health facility. The resulting six-minute walk distance (6MWD) correlates strongly ' 'with peak VO\u2082 on formal CPET (r\u00a0=\u00a00.50\u20130.73 across chronic disease ' 'populations) and captures the integrated functional reserve of the cardiovascular, ' 'respiratory, neuromuscular, and metabolic systems simultaneously.(3,13) Critically, ' 'it measures what patients actually do under physiologic load — not what they report ' 'doing in clinic.', sa=6 ) body( 'Published prospective data in surgical populations support the plausibility of ' '6MWD as a PPC predictor. Soares and Nucci (2021) enrolled 50 adults undergoing ' 'elective abdominal surgery and found that patients who developed PPCs within ' 'seven postoperative days had significantly lower preoperative 6MWDs than those ' 'who did not (444.8\u00a0m vs. 498.3\u00a0m; p\u00a0=\u00a00.013); after multivariable ' 'adjustment, each additional meter of preoperative 6MWD was independently protective ' '(OR\u00a0=\u00a00.978; 95%\u00a0CI 0.961\u20130.995; p\u00a0=\u00a00.010).(4) ' 'Magalhaes et al. (2017) replicated this finding in a prospective cohort of 100 liver ' 'transplant recipients, where every 50-meter gain in preoperative 6MWD was associated ' 'with a 41% reduction in the odds of postoperative respiratory complications ' '(OR\u00a0=\u00a00.589; 95%\u00a0CI 0.357\u20130.971; p\u00a0=\u00a00.03).(5) ' 'A 2022 systematic review and meta-analysis by Makker et al. confirmed that ' '6MWT\u00a0\u2265400\u00a0m was associated with a 62% reduction in the odds of ' 'grade 2\u20134 complications following abdominal and pelvic cancer surgery ' '(OR\u00a0=\u00a00.38; 95%\u00a0CI 0.15\u20130.95; p\u00a0=\u00a00.04).(8)', sa=6 ) body( 'Despite this evidence base, a critical methodologic gap persists. No prospective cohort ' 'study has specifically enrolled a heterogeneous major abdominal surgery population — ' 'inclusive of non-oncologic procedures — and applied both a standardized 6MWT protocol ' '(ATS 2002) and internationally consensus-defined PPC outcomes (StEP-COMPAC). Published ' 'studies are heterogeneous in surgical case mix, PPC definitions, and follow-up duration, ' 'limiting their generalizability. Furthermore, no cut-off data exist for Southeast Asian ' 'or Filipino surgical populations, in whom 6MWT normative values differ from Western ' 'reference equations.(14) This study is designed to address these specific gaps.', sa=8 ) # 3.3 heading2('3.3.', 'Background and Review of Related Literature') body( '3.3.1. Postoperative Pulmonary Complications Following Major Abdominal Surgery', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sb=6, sa=3 ) body( 'Abdominal surgery — particularly procedures involving the upper abdominal cavity — ' 'constitutes one of the highest-risk surgical categories for pulmonary complications. ' 'The pathophysiology is multifactorial. General anesthesia reduces functional residual ' 'capacity (FRC) and attenuates hypoxic pulmonary vasoconstriction, promoting dependent ' 'alveolar collapse that may persist for 24\u201348 hours postoperatively.(1) Abdominal ' 'incisions — whether midline, subcostal, or via laparotomy — directly impair ' 'diaphragmatic excursion and inspiratory effort, reducing tidal volume and cough ' 'effectiveness. Superimposed on these mechanical insults is the systemic inflammatory ' 'response of surgical injury, which alters pulmonary vascular permeability and promotes ' 'secretion retention in already-compromised airways.(1,2)', sa=6 ) body( 'Epidemiologic data confirm the resulting clinical burden. Garg et al. (2025) ' 'prospectively enrolled patients undergoing upper abdominal surgery and reported a ' 'PPC rate of 20.3%, with pneumonia and respiratory failure as the predominant events.(2) ' 'The international STARSurg/TASMAN collaborative, which validated six PPC prediction ' 'models across 11,591 patients undergoing major abdominal surgery, reported a ' 'StEP-COMPAC-defined PPC rate of 7.8%; the higher rates in procedure-specific cohorts ' 'reflect the variation introduced by case mix and surgical complexity.(10) This wide ' 'epidemiologic range underscores the necessity of standardized outcome definitions — ' 'a methodologic priority that the present study addresses through adoption of the ' 'StEP-COMPAC framework.', sa=8 ) body( '3.3.2. Limitations of Existing Preoperative Risk Stratification Tools', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sb=6, sa=3 ) body( 'The most widely adopted clinical scoring system for preoperative PPC risk ' 'stratification is the ARISCAT score, a seven-variable prediction model derived ' 'from a Spanish multicenter cohort of 2,464 patients. Despite its widespread use, ' 'the ARISCAT score achieved an AUROC of only 0.700 in the STARSurg/TASMAN ' 'international validation — the lower boundary of clinically adequate discrimination ' '— and all other validated models performed similarly or worse.(10) Spirometry-based ' 'risk stratification has also shown limited utility in non-thoracic surgical settings: ' 'a 2022 systematic review by Dankert et al. found only tentative evidence supporting ' 'the use of pulmonary function tests for PPC prediction after upper abdominal surgery, ' 'with insufficient evidence to support routine use in other abdominal procedures.(12) ' 'A fundamental shortcoming common to these tools is their failure to incorporate any ' 'objective measure of the patient\'s functional reserve — the very determinant of ' 'whether the postoperative physiologic stress can be tolerated.', sa=8 ) body( '3.3.3. The Six-Minute Walk Test: Physiologic Basis and Normative Data', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sb=6, sa=3 ) body( 'The 6MWT was standardized by the American Thoracic Society (ATS) in 2002 and is ' 'currently one of the most widely administered functional capacity tests globally. ' 'Unlike CPET, which requires a treadmill or cycle ergometer and continuous gas ' 'exchange monitoring, the 6MWT asks the patient to walk at a self-selected pace ' 'on a flat, 30-meter course for six minutes, with the primary output being total ' 'distance covered (6MWD) in meters.(3) The test engages the cardiovascular, ' 'respiratory, peripheral musculoskeletal, and motivational domains simultaneously, ' 'making 6MWD a global surrogate for functional aerobic capacity. Correlation with ' 'formal CPET-derived VO\u2082max is moderate to strong across chronic disease ' 'populations (r\u00a0=\u00a00.50\u20130.73), supporting its use as a field-based ' 'fitness proxy.(3)', sa=6 ) body( 'Normative reference data have been established across multiple populations. ' 'A 2026 meta-analysis by Otadi and Malmir, pooling 28 studies, reported mean 6MWDs ' 'of 473\u00a0m in older men and 428\u00a0m in older women, with a decline of ' 'approximately 10\u00a0m per decade of increasing age.(13) Asian-specific normative ' 'values from Yeung et al. (2022) — derived in a Singaporean cohort of 362 healthy ' 'adults aged 21\u201380 years — reported a population mean of 578\u00a0m ' '(\u00b175\u00a0m), declining from 601\u00a0m in the youngest age group to ' '519\u00a0m in adults aged 60\u201380 years.(14) These values are substantially ' 'higher than Western reference equations for equivalent age groups, reflecting ' 'anthropometric and demographic differences relevant to a Filipino surgical ' 'cohort.(14)', sa=8 ) body( '3.3.4. Evidence for 6MWT as a Perioperative Risk Predictor', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sb=6, sa=3 ) body( 'The mechanistic link between reduced preoperative 6MWD and postoperative pulmonary ' 'risk is well established. Rose et al. (2022) synthesized evidence across multiple ' 'surgical populations and concluded that impaired cardiorespiratory fitness — of which ' '6MWD is a validated proxy — is an independent risk factor for postoperative ' 'complications and in-hospital mortality, and is the single most modifiable ' 'perioperative risk variable amenable to prehabilitation.(3)', sa=6 ) body( 'In abdominal surgery populations specifically, Soares and Nucci (2021) conducted a ' 'prospective cohort study in 50 patients undergoing elective abdominal surgery, ' 'reporting a 50% overall PPC incidence. Patients who developed PPCs walked a mean ' 'of 444.8\u00a0m preoperatively versus 498.3\u00a0m in those who did not ' '(p\u00a0=\u00a00.013); multivariable analysis confirmed 6MWD as an independent ' 'predictor after adjusting for BMI, surgical time, and comorbidities ' '(OR\u00a0=\u00a00.978 per meter; p\u00a0=\u00a00.010).(4) In a parallel cohort ' 'design, Magalhaes et al. (2017) prospectively studied 100 liver transplant recipients ' 'and found that each 50-meter improvement in preoperative 6MWD was independently ' 'associated with a 41% reduction in postoperative respiratory complication risk ' '(OR\u00a0=\u00a00.589; 95%\u00a0CI 0.357\u20130.971; p\u00a0=\u00a00.03).(5)', sa=6 ) body( 'In esophageal cancer surgery, Inoue et al. (2020) prospectively evaluated 100 patients ' 'and identified a preoperative 6MWD threshold of \u2264454\u00a0m as predictive of ' 'major postoperative complications, with a sensitivity of 71% and specificity of 55%.(6) ' 'Hattori et al. (2018) reported a cut-off of \u2264450\u00a0m for postoperative ' 'pneumonia following pulmonary resection (sensitivity 69.2%, specificity 71.1%).(7) ' 'Synthesizing these data, Makker et al. (2022) conducted a systematic review and ' 'meta-analysis of five prospective studies in gastrointestinal and pelvic cancer surgery ' 'cohorts, concluding that a preoperative 6MWT\u00a0\u2265400\u00a0m was associated ' 'with substantially lower complication risk (OR\u00a0=\u00a00.38; 95%\u00a0CI ' '0.15\u20130.95; p\u00a0=\u00a00.04).(8) A systematic review by Argillander et al. ' '(2022) in older patients undergoing abdominal cancer surgery concluded that ' 'preoperative physical performance tests — including the 6MWT — are feasible ' 'CPET surrogates and predictors of postoperative outcomes, while calling for ' 'prospective studies using standardized protocols in non-oncologic abdominal ' 'surgery populations.(9)', sa=6 ) body( 'Collectively, the evidence establishes biological plausibility and statistical ' 'signal for 6MWT-based PPC risk prediction. The outstanding limitations are: ' 'the absence of prospective data in a heterogeneous (non-oncologic) general ' 'abdominal surgery cohort; non-standardized PPC definitions across existing studies; ' 'and the lack of locally validated cut-off values for Southeast Asian or Filipino ' 'surgical populations. The present study is specifically designed to address each ' 'of these deficiencies.', sa=8 ) # ════════════════════════════════════════════════════════════════════════════ # 4. OBJECTIVES # ════════════════════════════════════════════════════════════════════════════ heading1('4', 'Objectives') heading2('4.1.', 'General Objective') body( 'To determine the independent association between preoperative six-minute walk ' 'distance (6MWD) and the occurrence of postoperative pulmonary complications (PPCs) ' 'within 30 days of elective major abdominal surgery among adult patients admitted ' 'to a tertiary referral hospital.', sb=4, sa=8 ) heading2('4.2.', 'Specific Objectives') body('The study will specifically aim to:', sb=4, sa=4) spec = [ ('1.', 'Describe the baseline sociodemographic, clinical, and functional ' 'characteristics of the study cohort, including preoperative 6MWD, body mass index ' '(BMI), smoking status, comorbidities, ASA Physical Status classification, ' 'and ARISCAT score.'), ('2.', 'Determine the independent association between preoperative 6MWD as a ' 'continuous variable and PPC occurrence within 30 days of surgery, using ' 'multivariable binary logistic regression adjusted for operative duration and ' 'procedure type, expressed as adjusted odds ratios (aOR) with 95% confidence ' 'intervals (CI).'), ('3.', 'Evaluate the association between preoperative 6MWD and secondary clinical ' 'outcomes: unplanned ICU admission, requirement for invasive or non-invasive ' 'mechanical ventilation, length of hospital stay, and in-hospital mortality.'), ('4.', 'Identify an optimal preoperative 6MWD threshold for PPC prediction using ' 'receiver operating characteristic (ROC) curve analysis and the Youden index, and ' 'report the sensitivity, specificity, positive predictive value (PPV), and negative ' 'predictive value (NPV) at that threshold.'), ] for n, t in spec: numbered_item(n, t) body('', sa=4) # ════════════════════════════════════════════════════════════════════════════ # 5. METHODS # ════════════════════════════════════════════════════════════════════════════ heading1('5', 'Methods / Methodology') # 5.1 heading2('5.1.', 'Study Design, Setting, and Time Period') body( 'This is a prospective, single-center, observational cohort study. The study will ' 'be conducted at St. Luke\'s Medical Center, a tertiary-level accredited referral ' 'hospital. Eligible participants will be enrolled consecutively from the ' 'preoperative anesthesia assessment clinic over a 12-month recruitment period ' 'following Institutional Ethics Review Committee (IERC) approval. Each enrolled ' 'participant will be followed from the date of surgery through postoperative ' 'day\u00a030 or hospital discharge, whichever occurs later, to ensure complete ' 'ascertainment of all primary and secondary outcome events.', sb=4, sa=8 ) # 5.2 heading2('5.2.', 'Study Population and Eligibility Criteria') heading3('5.2.1.', 'Inclusion Criteria') body('Patients will be eligible for enrollment if all of the following criteria are met:', sb=2, sa=4) incl = [ ('1.','Adult patient aged 18 years or older at the time of the planned operative procedure.'), ('2.','Scheduled for elective major abdominal surgery, defined as an intraperitoneal ' 'operative procedure expected to last 60 minutes or longer under general or ' 'neuraxial anesthesia, including but not limited to colectomy, gastrectomy, ' 'hepatectomy, pancreatectomy (Whipple\'s or distal), and small-bowel resection.'), ('3.','Independently ambulatory at baseline and physically capable of performing a ' 'six-minute walk test without a mobility aid that would preclude standardized ' 'protocol administration.'), ('4.','Anticipated to remain hospitalized postoperatively with documentation of ' 'clinical outcome data accessible through hospital discharge or death.'), ('5.','Able and willing to provide written informed consent prior to study enrollment.'), ] for n,t in incl: numbered_item(n,t) body('',sa=4) heading3('5.2.2.', 'Exclusion Criteria') body('Patients will be excluded if any of the following conditions are present:', sb=2, sa=4) excl = [ ('1.','Emergency or unscheduled abdominal surgery, in which the preoperative time ' 'window required for 6MWT administration cannot be reliably established.'), ('2.','Pre-existing musculoskeletal or neurological conditions that independently ' 'preclude safe ambulation or invalidate 6MWT results: active lower-extremity ' 'ischemia, weight-bearing restrictions secondary to severe osteoarthritis or ' 'recent fracture, or hemiplegia.'), ('3.','Resting peripheral oxygen saturation (SpO\u2082) below 88% on room air, or ' 'hemodynamic instability at the time of planned testing — both constituting ' 'absolute contraindications to exercise testing per ATS guidelines.'), ('4.','Active unstable angina, acute decompensated heart failure (NYHA Class IV), ' 'or acute exacerbation of chronic obstructive pulmonary disease within four weeks ' 'of the scheduled procedure.'), ('5.','Planned re-operation or staged procedure during the same index hospitalization.'), ('6.','Patients in whom retrieval of essential baseline or postoperative outcome ' 'variables is not feasible.'), ] for n,t in excl: numbered_item(n,t) body('',sa=6) # 5.3 heading2('5.3.', 'Operational Definitions') body('Table 1 presents the operational definitions of key study variables and outcomes.', sb=4, sa=4) from docx.shared import Pt as _Pt op_tbl = doc.add_table(rows=1, cols=2) op_tbl.style = 'Table Grid' op_tbl.columns[0].width = Inches(1.9) op_tbl.columns[1].width = Inches(4.1) for i,h in enumerate(['Variable / Term','Operational Definition']): c = op_tbl.cell(0,i); c.text = h for r in c.paragraphs[0].runs: r.bold=True; r.font.size=_Pt(11); r.font.name=TNR op_defs = [ ('Major Abdominal Surgery', 'Any elective intraperitoneal operative procedure anticipated to last \u226560\u00a0minutes ' 'under general or neuraxial anesthesia, including colorectal resection, gastrectomy, ' 'hepatobiliary and pancreatic surgery, and small-bowel resection.'), ('Six-Minute Walk Test (6MWT)', 'A standardized submaximal exercise field test administered per the American Thoracic ' 'Society 2002 guidelines on a flat, marked 30-meter indoor corridor. The patient is ' 'instructed to walk as far and as fast as safely possible for exactly six minutes. ' 'Standardized verbal encouragement is provided at each completed minute.'), ('Six-Minute Walk Distance (6MWD)', 'The total distance covered (in meters, to the nearest meter) during the 6MWT. ' 'Serves as the primary continuous predictor variable. Also evaluated as a ' 'dichotomized variable at the ROC-derived study threshold and at the pre-specified ' 'literature benchmark of 400\u00a0m.'), ('Postoperative Pulmonary\nComplication (PPC)', 'The primary outcome. Any of the following events occurring within 30 days of the ' 'index surgical procedure, adjudicated per StEP-COMPAC consensus criteria:\n' '(a) Pneumonia: new pulmonary infiltrate on chest radiograph accompanied by at ' 'least two of: fever (T >38\u00b0C), leukocytosis (WBC >12\u00d710\u2079/L), or ' 'purulent tracheobronchial secretions;\n' '(b) Respiratory failure: SpO\u2082 <90% on room air, or requirement for ' 'supplemental oxygen beyond 24\u00a0h, or mechanical ventilation >24\u00a0h;\n' '(c) Atelectasis requiring active intervention: bronchoscopy or chest physiotherapy ' 'beyond routine postoperative care;\n' '(d) Pleural effusion requiring invasive drainage;\n' '(e) Bronchospasm requiring inhaled bronchodilator therapy.'), ('Unplanned ICU Admission', 'Transfer to the intensive care unit at any time after return from the operating ' 'theater, not prespecified in the postoperative management plan prior to surgery.'), ('Invasive Mechanical Ventilation', 'Ventilatory support delivered via endotracheal tube or tracheostomy initiated or ' 'continued beyond 24\u00a0hours after the conclusion of the operative procedure.'), ('Non-Invasive Ventilation (NIV)', 'Application of CPAP, BiPAP, or high-flow nasal cannula (HFNC) oxygen therapy ' 'initiated beyond the immediate post-anesthetic recovery period for acute ' 'respiratory compromise.'), ('Length of Hospital Stay', 'Total number of calendar days from the day of surgery (day\u00a0=\u00a00) to the ' 'day of hospital discharge. Reported as a continuous secondary outcome variable.'), ('In-Hospital Mortality', 'Death from any cause occurring during the index hospitalization prior to ' 'hospital discharge.'), ('Reduced Functional Capacity', 'Preoperative 6MWD below the ROC-derived study-specific threshold; also assessed ' 'against the pre-specified literature benchmark of <400\u00a0m.'), ] for var, defn in op_defs: row = op_tbl.add_row() row.cells[0].text = var; row.cells[1].text = defn for ci in [0,1]: for rr in row.cells[ci].paragraphs[0].runs: rr.font.size=_Pt(11); rr.font.name=TNR body( 'Table 1. Operational Definitions of Key Study Variables and Outcomes.', italic=True, sz=11, align=WD_ALIGN_PARAGRAPH.CENTER, sb=4, sa=10 ) # 5.4 heading2('5.4.', 'Study Procedures') heading3('5.4.1.', 'Participant Identification and Recruitment') body( 'Eligible patients will be identified prospectively from the elective operating ' 'theater schedule during the routine preoperative anesthesia assessment clinic visit, ' 'which occurs two to seven days prior to the scheduled procedure. The study team ' 'will screen all adult patients booked for qualifying abdominal operations. Those ' 'meeting eligibility criteria and passing the pre-exercise safety screen will ' 'be approached for enrollment. Written informed consent will be obtained before ' 'any study-specific procedure is performed.', sb=4, sa=8 ) heading3('5.4.2.', 'Data Collection') body('The following variables will be collected using a standardized, pre-piloted ' 'Case Report Form (CRF):', sb=4, sa=4) data_vars = [ ('Sociodemographics:', ' Age (years), biological sex, height (cm), weight (kg), ' 'body mass index (kg/m\u00b2).'), ('Clinical Comorbidities:', ' Hypertension, type 2 diabetes mellitus, ischemic ' 'heart disease, chronic heart failure (NYHA class), chronic obstructive pulmonary ' 'disease, obstructive sleep apnea, chronic kidney disease (non-dialysis-dependent), ' 'active tobacco use, and cumulative pack-year history.'), ('Preoperative Risk Stratification:', ' ASA Physical Status classification (I\u2013V), ' 'assigned by the attending anesthesiologist; ARISCAT score, computed from seven ' 'preoperative variables at time of enrollment.'), ('6MWT Parameters:', ' Total 6MWD (m); resting and post-test SpO\u2082 and heart rate ' 'by pulse oximetry; Borg dyspnea scale score (0\u201310) before and immediately ' 'after the test; number of laps completed; reason for early termination (if applicable).'), ('Preoperative Spirometry (if available):', ' FEV\u2081, FVC, and FEV\u2081/FVC ratio, ' 'extracted from the medical record if obtained as part of routine preoperative care.'), ('Surgical and Anesthetic Variables:', ' Operative procedure type, operative approach ' '(open / laparoscopic / robotic / hand-assisted), anesthetic technique, total ' 'operative duration (minutes), estimated intraoperative blood loss (mL).'), ('Primary Outcome:', ' Occurrence of any StEP-COMPAC-defined PPC (Yes/No) within ' '30\u00a0postoperative days; event type and calendar date of occurrence.'), ('Secondary Outcomes:', ' Unplanned ICU admission (Yes/No); invasive mechanical ' 'ventilation >24\u00a0h (Yes/No); NIV or HFNC initiated beyond recovery (Yes/No); ' 'length of hospital stay (days); in-hospital mortality (Yes/No).'), ] for lbl,txt in data_vars: bullet_item(lbl, txt) body('',sa=4) heading3('5.4.3.', 'Six-Minute Walk Test Protocol') body( 'A single standardized 6MWT will be administered to each participant by the principal ' 'investigator or a designated, protocol-trained research associate, strictly adhering ' 'to the ATS 2002 guidelines. Testing will be performed on a flat, clearly marked, ' 'climate-controlled indoor corridor of exactly 30 meters. Prior to the test, resting ' 'SpO\u2082 and heart rate will be recorded by pulse oximetry, and the Borg dyspnea ' 'scale administered. The participant will receive a standardized verbal briefing: ' '"The goal is to walk as far as possible in six minutes. You may slow down or stop ' 'if necessary, but please resume walking as soon as you are able." Standardized ' 'encouragement phrases will be delivered at each completed minute. At the conclusion ' 'of six minutes, the total distance walked will be recorded to the nearest meter, ' 'and post-test SpO\u2082, heart rate, and Borg score obtained.', sb=4, sa=6 ) body( 'The test will be suspended before six minutes if any of the following stopping ' 'criteria are met: (a) chest pain or chest tightness; (b) severe dyspnea; ' '(c) pallor, diaphoresis, or pre-syncopal symptoms; (d) SpO\u2082 below 85%; ' '(e) a Borg dyspnea score of 9 or 10; or (f) participant\'s voluntary request ' 'to stop. Early termination, the time elapsed, and the reason will be documented.', sa=6 ) body( 'All test administrators will complete a structured training session prior to study ' 'commencement, covering protocol standardization, verbal briefing scripts, ' 'encouragement timing, and stopping criteria. Intra-rater and inter-rater ' 'reliability of 6MWD measurement will be assessed through a pilot phase on ' 'five volunteer patients prior to enrollment of study participants.', sa=8 ) heading3('5.4.4.', 'Outcome Ascertainment and Adjudication') body( 'Postoperative outcome data will be collected through daily prospective review ' 'of the electronic medical record and nursing documentation by trained research ' 'staff from the day of surgery through hospital discharge. PPC occurrence will be ' 'adjudicated independently by two study investigators blinded to the participant\'s ' 'preoperative 6MWD, using the StEP-COMPAC criteria as the reference standard. ' 'Disagreements between adjudicators will be resolved by consensus review with a ' 'third senior investigator. Inter-rater agreement will be quantified using ' 'Cohen\'s kappa statistic prior to final analysis.', sb=4, sa=6 ) body( 'For participants discharged before postoperative day\u00a030, outcome data will ' 'be obtained through a structured telephone interview or scheduled outpatient ' 'follow-up consultation at day\u00a030, using a standardized questionnaire ' 'covering the occurrence of any PPC, readmission events, and vital status.', sa=8 ) # 5.5 heading2('5.5.', 'Outcome Measures') body( 'The primary outcome measure is the composite occurrence of any postoperative ' 'pulmonary complication (PPC) within 30 days of the index surgery, defined per ' 'StEP-COMPAC consensus criteria. This outcome was selected on mechanistic and ' 'epidemiologic grounds: the physiologic reserve measured by the 6MWT directly ' 'determines the patient\'s capacity to sustain the augmented ventilatory and ' 'immune demands imposed by the perioperative period.(3,4) The 30-day ascertainment ' 'window is consistent with standard perioperative outcome reporting and captures ' 'the full spectrum of PPC events, including those presenting after early ' 'discharge.(10)', sb=4, sa=6 ) body( 'Secondary outcome measures include: (a) length of hospital stay, in calendar ' 'days from surgery to discharge (continuous); (b) unplanned ICU admission ' '(dichotomous, Yes/No); (c) invasive mechanical ventilation beyond 24\u00a0hours ' 'postoperatively (dichotomous); (d) initiation of non-invasive ventilation or ' 'high-flow nasal cannula oxygen therapy beyond the immediate recovery period ' '(dichotomous); and (e) in-hospital mortality (dichotomous). The aggregate of ' 'secondary outcomes constitutes the clinical consequence spectrum of postoperative ' 'pulmonary morbidity and is consistent with outcome hierarchies used in comparable ' 'prospective surgical cohort studies.(3,4,5,8)', sa=8 ) # 5.6 heading2('5.6.', 'Sample Size Estimation') body( 'Sample size was estimated for the primary outcome using a two-proportion comparison ' 'formula, based on prospective incidence data from Soares and Nucci (2021) — the ' 'most methodologically comparable published study.(4) That cohort reported a PPC ' 'incidence of 65% among patients with preoperative 6MWD <400\u00a0m and 30% among ' 'those with 6MWD\u00a0\u2265400\u00a0m. Assuming equal allocation between groups ' '(1:1 ratio), a two-sided significance level (\u03b1) of 0.05, and a statistical ' 'power of 80% (\u03b2 = 0.20), the base estimated sample size is 31 participants ' 'per group (62 total).', sb=4, sa=6 ) body( 'To account for multivariable analysis with one primary covariate (operative ' 'duration), a minimum of 10 events per variable (EPV) convention requires an ' 'addition of 10 participants, yielding an adjusted total of 72. Applying a ' '5% allowance for prospective loss to follow-up (protocol non-completion, ' 'withdrawal of consent, incomplete outcome documentation) produces a final ' 'target sample size of approximately 76 participants. Sample size calculations ' 'were performed using standard formulas for comparison of two independent ' 'proportions.', sa=8 ) # 5.7 heading2('5.7.', 'Statistical Analysis') body( 'All statistical analyses will be performed using IBM SPSS Statistics Version 29.0 ' '(or R version 4.3+). A two-sided p\u00a0<\u00a00.05 will be adopted as the ' 'threshold for statistical significance throughout. No interim analysis is planned.', sb=4, sa=6 ) body('Descriptive Statistics', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=2) body( 'Baseline sociodemographic and clinical characteristics will be summarized ' 'using standard descriptive statistics. Normally distributed continuous variables ' 'will be expressed as mean\u00a0\u00b1\u00a0standard deviation (SD); ' 'non-normally distributed variables as median with interquartile range (IQR). ' 'Categorical variables will be reported as frequencies (n) and percentages (%). ' 'Distribution normality will be assessed using the Shapiro-Wilk test. ' 'Between-group comparisons (PPC vs. no PPC) will use the independent-samples ' 't-test or Mann-Whitney\u00a0U test for continuous variables, and the chi-squared ' 'or Fisher\'s exact test for categorical variables, as appropriate.', sa=6 ) body('Primary Analysis: Multivariable Logistic Regression', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=2) body( 'The primary hypothesis — that preoperative 6MWD is independently associated with ' 'PPC occurrence — will be tested using multivariable binary logistic regression. ' 'PPC occurrence (Yes/No) will be entered as the dependent variable. Preoperative ' '6MWD will be entered as a continuous independent variable (per 10-meter decrement), ' 'with operative duration and procedure type included as pre-specified confounders. ' 'Additional covariates associated with the outcome at p\u00a0<\u00a00.20 on ' 'univariable analysis will be considered for inclusion, subject to the 10 EPV ' 'constraint. Results will be expressed as adjusted odds ratios (aOR) with 95% ' 'confidence intervals (95%\u00a0CI).', sa=6 ) body('ROC Curve Analysis and Cut-off Derivation', bold=True, align=WD_ALIGN_PARAGRAPH.LEFT, sa=2) body( 'Receiver operating characteristic (ROC) curve analysis will be performed to ' 'evaluate the overall discriminative ability of preoperative 6MWD for PPC ' 'prediction. The area under the ROC curve (AUROC) with 95%\u00a0CI will be ' 'reported as the primary measure of model discrimination. The optimal 6MWD ' 'cut-off point will be identified using the Youden index ' '(J\u00a0=\u00a0sensitivity + specificity \u2212 1). At this threshold, ' 'sensitivity, specificity, positive predictive value (PPV), negative ' 'predictive value (NPV), and diagnostic likelihood ratios (positive and negative) ' 'will be reported. The a priori literature-derived cut-off of 400\u00a0m will be ' 'evaluated as a secondary threshold for comparative reference.', sa=8 ) # 5.8 heading2('5.8.', 'Ethical Considerations') body( 'This study will be conducted in full compliance with the principles of the ' 'Declaration of Helsinki (World Medical Association, 2013 revision), the ' 'International Conference on Harmonisation Good Clinical Practice (ICH-GCP) ' 'guidelines (E6 R2), and the applicable provisions of the Philippine National ' 'Health Research System (PNHRS) Ethical Standards. Protocol submission for ' 'Institutional Ethics Review Committee (IERC) approval will be completed prior ' 'to enrollment of any participant.', sb=4, sa=6 ) heading3('5.8.1.', 'Risk-Benefit Assessment and Adverse Event Management') body( 'The study imposes minimal incremental risk beyond the routine preoperative ' 'clinical assessment. The 6MWT is a widely performed, non-invasive field test ' 'with an established safety profile in medically supervised settings, including ' 'in patients with significant cardiorespiratory comorbidity. No study-specific ' 'invasive procedure will be performed. Participation will in no way alter the ' 'planned surgical, anesthetic, or postoperative management of any enrolled ' 'participant.', sb=4, sa=6 ) body( 'In the event of an adverse event during 6MWT administration, the test will be ' 'immediately terminated per the stopping criteria enumerated in Section 5.4.3. ' 'A clinician certified in basic life support (BLS) will be present or immediately ' 'accessible throughout all test administrations. Any adverse event will be ' 'documented on the Adverse Event Reporting Form and notified to the Principal ' 'Investigator within 24\u00a0hours. Serious adverse events will be reported to ' 'the SLMC IERC within the timeframe required by institutional policy.', sa=8 ) heading3('5.8.2.', 'Anticipated Risks and Discomforts') body( 'Risk to participants is minimal. The 6MWT entails self-paced sustained ambulation ' 'over six minutes — a physical demand equivalent to or below that of independent ' 'ambulation in a hospital corridor. Transient exertional dyspnea and lower-extremity ' 'fatigue are expected and universally resolve within two to five minutes of test ' 'conclusion. Pre-test safety screening (eligibility criteria, resting SpO\u2082, ' 'hemodynamic status) will exclude all patients in whom any form of exercise is ' 'clinically contraindicated.', sb=4, sa=8 ) heading3('5.8.3.', 'Anticipated Benefits') body( 'No direct therapeutic benefit to individual participants is guaranteed by study ' 'participation, as 6MWT results will not automatically modify perioperative ' 'management within the study period. However, participants will receive an ' 'objective, documented assessment of their functional capacity that their ' 'attending physician may use at their discretion. At the population level, ' 'successful validation of the 6MWT as a PPC predictor will provide Filipino ' 'surgeons and anesthesiologists with a free, zero-infrastructure preoperative ' 'risk stratification tool, with direct applicability to prehabilitation ' 'referral pathways in Philippine perioperative care settings.', sb=4, sa=8 ) heading3('5.8.4.', 'Confidentiality and Data Governance') body( 'All enrolled participants will be assigned a unique, non-identifiable alphanumeric ' 'study code at the time of consent. No personal identifiers — including name, date ' 'of birth, address, or hospital number — will appear in any study database, analysis ' 'file, or publication. Study data will be stored in a password-protected, ' 'AES-256-encrypted digital database accessible exclusively to the principal ' 'investigator and named co-investigators.', sb=4, sa=6 ) body( 'Paper source documents (signed consent forms, CRFs) will be stored in a locked ' 'filing cabinet with restricted key access within the study office. All study ' 'records will be retained for a minimum of five years following the date of final ' 'publication, after which they will be disposed of in accordance with the ' 'St. Luke\'s Medical Center institutional records management policy. Results will ' 'be disseminated exclusively in aggregate form through peer-reviewed publication ' 'and scientific conference presentation; no individual participant will be ' 'identifiable from any report.', sa=10 ) # ════════════════════════════════════════════════════════════════════════════ # 7. REFERENCES # ════════════════════════════════════════════════════════════════════════════ heading1('7', 'References') body('References are cited in the text using Vancouver (numeric) style in order of first appearance.', sb=4, sa=6, italic=True) refs = [ ('1.','Smetana GW, Lawrence VA, Cornell JE; American College of Physicians. ' 'Preoperative pulmonary risk stratification for noncardiothoracic surgery: ' 'systematic review for the American College of Physicians. ' 'Ann Intern Med. 2006 Apr 18;144(8):581\u201395. ' 'doi:\u00a010.7326/0003-4819-144-8-200604180-00009.'), ('2.','Garg S, Govindaraj V, Dwivedi DP, Raja K, Theerthar EP. ' 'Postoperative pulmonary complications in patients undergoing upper abdominal ' 'surgery: risk factors and predictive models. ' 'Monaldi Arch Chest Dis. 2025 Mar 31. ' 'doi:\u00a010.4081/monaldi.2024.2915. PMID:\u00a038526466.'), ('3.','Rose GA, Davies RG, Appadurai IR, Williams IM, Bashir M, Berg RMG. ' '\'Fit for surgery\': the relationship between cardiorespiratory fitness and ' 'postoperative outcomes. ' 'Exp Physiol. 2022 Aug;107(8):780\u201395. ' 'doi:\u00a010.1113/EP090156. PMID:\u00a035579479.'), ('4.','Soares SMTP, Nucci LB. ' 'Association between early pulmonary complications after abdominal surgery and ' 'preoperative physical capacity: a prospective observational study. ' 'Physiother Theory Pract. 2021 Jul;37(7):852\u20139. ' 'doi:\u00a010.1080/09593985.2019.1650404. PMID:\u00a031402737.'), ('5.','Magalhaes CBA, Nogueira IC, Marinho LS, Daher EF, Garcia JHP, Viana CFG. ' 'Exercise capacity impairment can predict postoperative pulmonary complications ' 'after liver transplantation. ' 'Respiration. 2017;94(6):538\u201344. ' 'doi:\u00a010.1159/000479008. PMID:\u00a028738386.'), ('6.','Inoue T, Ito S, Kanda M, Niwa Y, Nagaya M, Nishida Y. ' 'Preoperative six-minute walk distance as a predictor of postoperative ' 'complication in patients with esophageal cancer. ' 'Dis Esophagus. 2020 Mar 5;33(3):doz050. ' 'doi:\u00a010.1093/dote/doz050. PMID:\u00a031111872.'), ('7.','Hattori K, Matsuda T, Takagi Y, Nagaya M, Inoue T, Nishida Y. ' 'Preoperative six-minute walk distance is associated with pneumonia after ' 'lung resection. ' 'Interact Cardiovasc Thorac Surg. 2018 Feb 1;26(2):208\u201313. ' 'doi:\u00a010.1093/icvts/ivx310. PMID:\u00a029049742.'), ('8.','Makker PGS, Koh CE, Solomon MJ, Steffens D. ' 'Preoperative functional capacity and postoperative outcomes following abdominal ' 'and pelvic cancer surgery: a systematic review and meta-analysis. ' 'ANZ J Surg. 2022 Jul;92(7\u20138):1732\u201340. ' 'doi:\u00a010.1111/ans.17577. PMID:\u00a035253333.'), ('9.','Argillander TE, Heil TC, Melis RJF, van Duijvendijk P, Klaase JM, van Munster BC. ' 'Preoperative physical performance as predictor of postoperative outcomes in patients ' 'aged 65 years and older scheduled for major abdominal cancer surgery: a systematic ' 'review. ' 'Eur J Surg Oncol. 2022 Mar;48(3):575\u201384. ' 'doi:\u00a010.1016/j.ejso.2021.09.019. PMID:\u00a034629224.'), ('10.','STARSurg Collaborative; TASMAN Collaborative. ' 'Evaluation of prognostic risk models for postoperative pulmonary complications ' 'in adult patients undergoing major abdominal surgery: a systematic review and ' 'international external validation cohort study. ' 'Lancet Digit Health. 2022 Jul;4(7):e498\u2013e507. ' 'doi:\u00a010.1016/S2589-7500(22)00069-3. PMID:\u00a035750401.'), ('11.','Boden I, Reeve J, Jernas A, Denehy L, Fagevik Olsen M. ' 'Preoperative physiotherapy prevents postoperative pulmonary complications ' 'after major abdominal surgery: a meta-analysis of individual patient data. ' 'J Physiother. 2024 Jul;70(3):195\u2013202. ' 'doi:\u00a010.1016/j.jphys.2024.02.012. PMID:\u00a038472053.'), ('12.','Dankert A, Dohrmann T, Loser B, Zapf A, Zollner C, Petzoldt M. ' 'Pulmonary function tests for the prediction of postoperative pulmonary ' 'complications. ' 'Dtsch Arztebl Int. 2022 Feb 18;119(7):99\u2013106. ' 'doi:\u00a010.3238/arztebl.m2022.0074. PMID:\u00a034939921.'), ('13.','Otadi K, Malmir K. ' 'Normative reference values for the six-minute walk test in older adults: ' 'a systematic review and meta-analysis. ' 'Arch Gerontol Geriatr. 2026 Jan;128:106026. ' 'doi:\u00a010.1016/j.archger.2025.106026. PMID:\u00a041027267.'), ('14.','Yeung MT, Chan MY, Huang KS, Chen TJ, Chia CP, Fong MM. ' 'Normative reference values and regression equations to predict the ' '6-minute walk distance in the Asian adult population aged 21\u201380 years. ' 'Hong Kong Physiother J. 2022 Dec;42(2):69\u201378. ' 'doi:\u00a010.1142/S1013702522500111. PMID:\u00a037560171.'), ] for num, txt in refs: ref_item(num, txt) # Footer horiz_line() p_footer = body('', align=WD_ALIGN_PARAGRAPH.RIGHT, sb=4, sa=0) add_run(p_footer, 'Protocol Version 01 | Date of Preparation: _________________ | SLMC IERC Ref. No.: _________________', sz=10) OUT = '/tmp/workspace/6mwt-proposal/Study1_6MWT_PPCs_MedicalVersion.docx' doc.save(OUT) print(f'Saved: {OUT}') print(f'Paragraphs: {len(doc.paragraphs)} | Tables: {len(doc.tables)}') PYEOF python3 /tmp/workspace/6mwt-proposal/build_study1_medical.py