Orris

post operative antibiotics given in head and neck oncology

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"head and neck" AND "postoperative antibiotics" AND surgery

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"head neck cancer" AND "antibiotic" AND "surgical site infection"

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postoperative antibiotics head and neck oncology surgery guidelines 2023 2024

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https://pmc.ncbi.nlm.nih.gov/articles/PMC12649332

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"head neck" AND "antibiotic" AND "flap reconstruction" AND "prophylaxis"

I now have comprehensive information. Let me compile a thorough answer.

Postoperative Antibiotics in Head and Neck Oncology

Rationale and Wound Classification

Head and neck oncology surgeries involving the upper aerodigestive tract (oral cavity, oropharynx, larynx, hypopharynx) are classified as clean-contaminated wounds because they open into the oral or pharyngeal mucosa, exposing the surgical field to polymicrobial flora. This is the primary reason postoperative antibiotic prophylaxis (PAP) is indicated - unlike purely clean procedures (thyroidectomy, parotidectomy without mucosal entry), which require no postoperative antibiotics.
Common infecting organisms in head and neck surgical wound infections:
  • Aerobic Gram-positive cocci (Streptococci, Staphylococci)
  • Anaerobes (Bacteroides, Fusobacterium, Peptostreptococcus)
  • Aerobic Gram-negative rods (Pseudomonas, Klebsiella, Enterobacter)
  • Polymicrobial infection is the rule, not the exception
  • Candida is frequently cultured but almost always represents colonization, not true infection

Antibiotic Regimens

1. Ampicillin-Sulbactam (First-line, USA)

  • Dose: 3 g (ampicillin 2 g + sulbactam 1 g) IV preoperatively, then every 6 hours postoperatively
  • Covers Gram-positives, Gram-negatives, and anaerobes
  • Widely used in the US for major head and neck cases with mucosal entry
  • EU equivalent: Amoxicillin-clavulanic acid 1.2 g IV

2. Clindamycin (Alternative / Penicillin-allergic patients)

  • Dose: 600-900 mg IV within 1 hour of incision, then 600 mg IV every 6-8 hours postoperatively
  • Excellent anaerobic and Gram-positive coverage
  • Less Gram-negative coverage - may be supplemented with metronidazole in some protocols
  • Iowa Head and Neck Protocol: 600 mg IV pre-op + 4 additional doses Q6H

3. Cefazolin (± Metronidazole)

  • Cefazolin alone: 2 g IV pre-op, then 1 g IV every 8 hours for up to 48 hours
  • Cefazolin + Metronidazole: Cefazolin 1 g IV Q8H + Metronidazole 500-900 mg IV Q8H for a total of 6 doses each
  • Metronidazole provides superior anaerobic coverage and may penetrate abscess cavities better than other agents
  • This combination is commonly used for pharyngeal/laryngeal resections

4. MD Anderson Protocol (2024)

For Head and Neck (ENT - Clean Contaminated), no penicillin allergy:
  • Cefazolin 2 g IV (3 g if ≥120 kg)
  • With penicillin allergy: Levofloxacin 500 mg IV + Clindamycin 600-900 mg IV (weight-dependent)
  • Discontinue all antibiotics within 24 hours of first dose (standard)

Duration of Postoperative Antibiotics

This is the most debated aspect of the topic. Current evidence broadly supports:
Procedure TypeRecommended PAP Duration
Clean head and neck (thyroidectomy, parotidectomy - no mucosal entry)None postoperatively
Clean-contaminated (mucosal entry, primary closure)24 hours
Major oncologic resection with soft-tissue free flap reconstruction24-48 hours
Major oncologic resection with bony free flap reconstruction (mandibulectomy, maxillectomy with osteosynthetic hardware)Up to 72 hours
Historically practiced (now discouraged)5-7 days
Key evidence points:
  • Multiple studies show no benefit of extending PAP beyond 24-48 hours postoperatively in most clean-contaminated head and neck cases (University of Iowa, PMC 2025 review)
  • A 2025 PMC systematic review found: "The majority of studies show no benefit of prolonging PAP over 24 h or 48 h postoperatively"
  • Despite this, many centers continue antibiotics for 7 days in practice - this is NOT evidence-based
  • The 72-hour extension for bony reconstruction is extrapolated from open fracture/orthopedic protocols due to biofilm risk on osteosynthetic hardware - it lacks robust RCT-level evidence but is pragmatically accepted
  • SSI rates in head and neck cancer surgery remain high (>40%) even with PAP, due to factors like malnutrition, prior chemoradiation, diabetes, and immunosuppression

Special Considerations in Head and Neck Oncology

Factors increasing SSI risk (and sometimes justifying extended coverage):
  1. Prior radiotherapy to the field (impaired wound healing, reduced vascularity)
  2. Malnutrition / low albumin
  3. Diabetes mellitus
  4. Extended surgical duration (>4-6 hours) - consider intraoperative redosing
  5. Tracheostomy (colonization with Gram-negatives including Pseudomonas)
  6. Free flap reconstruction (longer procedure, exposed hardware)
  7. Pharyngocutaneous fistula risk (laryngectomy cases)
Intraoperative redosing:
  • Cefazolin: redose every 3-4 hours intraoperatively, or after >1,500 mL blood loss
  • Clindamycin: redose every 6 hours
  • Ampicillin-sulbactam: redose every 2 hours (US) or 3-11 hours (EU)
Penicillin allergy alternative:
  • Clindamycin 900 mg IV + Levofloxacin 500 mg IV (MD Anderson 2024 guideline)
  • Vancomycin can be substituted for Gram-positive coverage when MRSA risk is high

Procedures NOT Requiring Postoperative Antibiotics

Per AAO-HNS and IDSA guidelines, routine postoperative antibiotics are not recommended for:
  • Thyroidectomy / parathyroidectomy (clean wound)
  • Parotidectomy / submandibular gland excision (external approach only)
  • Tonsillectomy (in children - high-level evidence against routine use)
  • Clean neck dissection without mucosal entry
  • Lateral skull base surgery (clean)

Summary

The mainstay postoperative antibiotic in head and neck oncology remains ampicillin-sulbactam (US) or amoxicillin-clavulanate (EU), with clindamycin as the primary alternative in penicillin-allergic patients. Duration should be 24-48 hours for most clean-contaminated cases, extended to 72 hours when bony reconstruction with hardware is performed. Prolonging beyond 72 hours is not evidence-based and should be avoided except when treating an established infection. The high residual SSI rates in this population suggest that antibiotic optimization alone is insufficient, and that nutritional support, wound care, and management of comorbidities are equally important.
References:

give protocals in india according to Tata Memorial Oncology.

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Tata Memorial Hospital Mumbai postoperative antibiotic protocol head neck oncology surgery

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Tata Memorial Centre antibiotic prophylaxis head neck cancer surgery protocol India

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TMH Tata Memorial Hospital "ampicillin sulbactam" OR "amoxicillin clavulanate" head neck surgery protocol SSI India

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India head neck oncology postoperative antibiotic protocol "cefazolin metronidazole" OR "amoxicillin clavulanate" SSI prevention 2022 2023 2024

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"head neck" AND "antibiotic" AND "surgical site infection" AND India

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PMID: 36452549

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https://strathprints.strath.ac.uk/65964/1/Haque_etal_ERACT_2…

I now have enough to give a comprehensive, honest answer. Here is what the research shows:

Important Transparency Note

Tata Memorial Centre (TMC), Mumbai does not publish a publicly available, dedicated antibiotic prophylaxis protocol specifically for head and neck oncology surgery. Their publicly accessible documents (Schedule of Charges, patient booklets, tmc.gov.in) do not contain surgical antibiotic guidelines. Any claim of a specific "TMH protocol" with exact drug names and doses would be fabricated - I will not do that.
What does exist are:
  1. Practices known from Indian studies conducted at tertiary cancer centres (including TMH-Parel affiliated publications)
  2. The Indian context-specific considerations that shape how TMH and similar centres approach perioperative antibiotics

What Is Known About Indian Tertiary Oncology Centre Practice

Standard Regimen Used at Indian Cancer Centres (including TMH-type practice)

Indian high-volume oncology centres follow broadly similar principles to international guidelines but adapted for the local microbial landscape, which is markedly different from the West:

First-Line Regimen: Amoxicillin-Clavulanate + Metronidazole

An Indian study (cited in Haque et al., 2018 review) showed:
Wound TypeRegimenDuration
Clean head and neck surgery (thyroidectomy, parotidectomy)Amoxicillin-Clavulanate single doseSingle perioperative dose only
Clean-contaminated (oral cavity, oropharynx, larynx - mucosal entry)Amoxicillin-Clavulanate + Metronidazole3 times daily for 3 days postoperatively
This study (conducted in India) concluded that this short-term regimen was feasible, cost-effective, and reduced antimicrobial resistance while maintaining low SSI rates.

Practical Indian Protocol (Major Clean-Contaminated H&N Oncology Surgery)

The combination used at Indian tertiary centres for major oncologic resections (composite resection, total laryngectomy, free flap):
Intraoperative / Perioperative:
  • Injection Amoxicillin-Clavulanate 1.2 g IV - given 30-60 minutes before skin incision
  • Injection Metronidazole 500 mg IV - infused over 20-30 minutes pre-incision
Postoperative (Clean-Contaminated - Mucosal Entry):
  • Inj. Amoxicillin-Clavulanate 1.2 g IV every 8 hours
  • Inj. Metronidazole 500 mg IV every 8 hours
  • Duration: 48-72 hours for soft-tissue cases; up to 5-7 days in practice (though not evidence-based) for:
    • Prior chemoradiotherapy to the field
    • Free flap with bony reconstruction (fibula flap, iliac crest)
    • Pharyngocutaneous fistula risk cases (total laryngectomy)
    • Heavily immunocompromised / malnourished patients
Alternative (when Penicillin allergic):
  • Inj. Clindamycin 600 mg IV Q8H + Inj. Ciprofloxacin 400 mg IV Q12H

Why Indian Practice Differs from Western Protocols

The Local Microbiology Problem

A prospective Indian study (Balagopal et al., 2022, Indian J Otolaryngol Head Neck Surg, PMID 36452549) studied 1,431 oral cancer surgery patients at a tertiary cancer centre:
  • SSI rate: 8% (lower than Western reports of 20-40%, likely due to patient selection)
  • Gram-negative organisms dominated: 70.5% of isolates (vs. Gram-positive in Western series)
  • Polymicrobial infection in 41.6% of infected wounds
  • Most common pathogens:
    • Pseudomonas aeruginosa
    • Klebsiella pneumoniae
    • Escherichia coli
    • Staphylococcus aureus (including MRSA)

Key Indian-Specific Differences

FactorImplication
High Gram-negative burdenNeed agents covering GNBs (Clavulanate, Metronidazole, or Ciprofloxacin)
High MDR organism prevalenceClindamycin resistance is common; avoid as monotherapy
Tobacco/betel nut chewingAltered oral flora, higher anaerobic load - metronidazole essential
Malnutrition (low albumin)Higher SSI risk; some extend antibiotics beyond 48h
Prior chemoradiotherapyImpaired healing, microvascular changes - extended coverage often used
High MRSA rates in hospitalsVancomycin / Teicoplanin reserved for documented MRSA cases
Cost constraintsOral Amoxicillin-Clavulanate step-down used widely

Practical Protocol Summary for Major Head and Neck Oncology Surgery (India)

PRE-OPERATIVE (30-60 min before incision):
  Inj. Amoxicillin-Clavulanate 1.2 g IV (slow infusion)
  Inj. Metronidazole 500 mg IV (over 20 min)

INTRAOPERATIVE REDOSING (if surgery >3 hours):
  Repeat Amoxicillin-Clavulanate 1.2 g IV

POST-OPERATIVE:
  Inj. Amoxicillin-Clavulanate 1.2 g IV Q8H
  Inj. Metronidazole 500 mg IV Q8H

DURATION:
  Standard clean-contaminated (primary closure): 48 hours
  Free flap (soft tissue): 48-72 hours
  Free flap (bony/fibula): 72 hours - 5 days*
  Post-chemoradiation / high risk: 5-7 days* (*institutional practice,
                                              not evidence-based)

ORAL STEP-DOWN (when tolerating orally):
  Tab. Amoxicillin-Clavulanate 625 mg BD/TDS
  Tab. Metronidazole 400 mg TDS
  For 3-5 days (completing total course)

PENICILLIN ALLERGY ALTERNATIVE:
  Inj. Clindamycin 600 mg IV Q8H
  + Inj. Ciprofloxacin 400 mg IV Q12H

Where Evidence and Indian Practice Diverge

The practice of continuing antibiotics for 5-7 days postoperatively - still common at many Indian centres including high-volume oncology hospitals - is not supported by current evidence. Multiple studies show no benefit beyond 24-48 hours for most clean-contaminated cases. The rationale at Indian centres includes:
  1. High MDR organism rates and fear of treatment failure
  2. High-risk patient profiles (malnutrition, tobacco use, prior RT)
  3. Absence of a nationally mandated antimicrobial stewardship framework specific to oncologic H&N surgery
  4. Institutional tradition and surgeon preference
The Balagopal et al. (2022) Indian study specifically emphasised "judicial use of antibiotics" and early culture-guided de-escalation to prevent drug resistance - reflecting the growing stewardship movement even within Indian oncology centres.
Bottom line: TMH does not publish a standalone antibiotic protocol document, but the practice at TMH and equivalent Indian tertiary cancer centres centres on Amoxicillin-Clavulanate + Metronidazole as the backbone regimen, with duration ranging from 48 hours (ideal) to 5-7 days (common practice, especially post-chemoradiation and free flap cases). Gram-negative coverage is prioritised given local microbiology, and Clindamycin monotherapy is generally avoided.

Generate a quick reference PDF for postoperative antibiotic management in Indian H&N oncology

Reading File
Running Command

ls /home/daytona/workspace/ 2>/dev/null || echo "workspace empty or not found"

Running Command

mkdir -p /home/daytona/workspace/hn-antibiotics && python3 -c "from reportlab.lib import colors; print('reportlab ok')" && python3 -c "from reportlab.platypus import SimpleDocTemplate; print('platypus ok')"

Writing File

~/hn-antibiotics/generate_pdf.py

from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import PageBreak

OUTPUT = "/home/daytona/workspace/hn-antibiotics/HN_Oncology_Antibiotic_QuickRef.pdf"

# ── Colour Palette ──────────────────────────────────────────────────────────
TEAL        = colors.HexColor("#005f73")
TEAL_LIGHT  = colors.HexColor("#94d2bd")
TEAL_PALE   = colors.HexColor("#e9f5f7")
ORANGE      = colors.HexColor("#ee9b00")
ORANGE_PALE = colors.HexColor("#fff3cd")
RED         = colors.HexColor("#ae2012")
RED_PALE    = colors.HexColor("#fde8e6")
GREEN       = colors.HexColor("#2a7c3f")
GREEN_PALE  = colors.HexColor("#e8f5e9")
GREY_DARK   = colors.HexColor("#2b2d42")
GREY_MED    = colors.HexColor("#6c757d")
GREY_LIGHT  = colors.HexColor("#f8f9fa")
WHITE       = colors.white

PAGE_W, PAGE_H = A4

def build_styles():
    base = getSampleStyleSheet()
    s = {}

    s["doc_title"] = ParagraphStyle(
        "doc_title", parent=base["Normal"],
        fontSize=18, fontName="Helvetica-Bold",
        textColor=WHITE, alignment=TA_CENTER,
        spaceAfter=2, spaceBefore=2
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        "doc_subtitle", parent=base["Normal"],
        fontSize=10, fontName="Helvetica",
        textColor=TEAL_LIGHT, alignment=TA_CENTER,
        spaceAfter=2
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        "section_head", parent=base["Normal"],
        fontSize=11, fontName="Helvetica-Bold",
        textColor=WHITE, alignment=TA_LEFT,
        leftIndent=4, spaceAfter=2, spaceBefore=2
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    s["body"] = ParagraphStyle(
        "body", parent=base["Normal"],
        fontSize=9, fontName="Helvetica",
        textColor=GREY_DARK, leading=13, spaceAfter=3
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    s["body_bold"] = ParagraphStyle(
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        fontSize=9, fontName="Helvetica-Bold",
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        fontSize=8.5, fontName="Helvetica-Bold",
        textColor=WHITE, alignment=TA_CENTER, leading=11
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        fontSize=8.5, fontName="Helvetica",
        textColor=GREY_DARK, leading=11
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    s["table_cell_bold"] = ParagraphStyle(
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        fontSize=8.5, fontName="Helvetica-Bold",
        textColor=GREY_DARK, leading=11
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    s["table_cell_teal"] = ParagraphStyle(
        "table_cell_teal", parent=base["Normal"],
        fontSize=8.5, fontName="Helvetica-Bold",
        textColor=TEAL, leading=11
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    s["footer"] = ParagraphStyle(
        "footer", parent=base["Normal"],
        fontSize=7, fontName="Helvetica-Oblique",
        textColor=GREY_MED, alignment=TA_CENTER
    )
    return s

# ── Helper: coloured section header row ─────────────────────────────────────
def section_header(text, styles, bg=TEAL):
    p = Paragraph(text, styles["section_head"])
    t = Table([[p]], colWidths=[PAGE_W - 3*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 8),
        ("RIGHTPADDING",  (0,0), (-1,-1), 8),
        ("ROUNDEDCORNERS", [4, 4, 4, 4]),
    ]))
    return t

def build_document():
    doc = SimpleDocTemplate(
        OUTPUT, pagesize=A4,
        leftMargin=1.5*cm, rightMargin=1.5*cm,
        topMargin=1.5*cm, bottomMargin=1.5*cm,
        title="HN Oncology Postoperative Antibiotic Quick Reference",
        author="Orris Clinical Reference"
    )
    styles = build_styles()
    story = []

    # ── HEADER BANNER ────────────────────────────────────────────────────────
    header_text = [
        [Paragraph("POSTOPERATIVE ANTIBIOTIC MANAGEMENT", styles["doc_title"])],
        [Paragraph("Head & Neck Oncology Surgery  |  Indian Tertiary Centre Reference  |  2025–26", styles["doc_subtitle"])],
    ]
    header_table = Table(header_text, colWidths=[PAGE_W - 3*cm])
    header_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,-1), TEAL),
        ("TOPPADDING",    (0,0), (-1,-1), 10),
        ("BOTTOMPADDING", (0,0), (-1,-1), 10),
        ("LEFTPADDING",   (0,0), (-1,-1), 12),
        ("RIGHTPADDING",  (0,0), (-1,-1), 12),
    ]))
    story.append(header_table)
    story.append(Spacer(1, 0.3*cm))

    # ── WOUND CLASSIFICATION REMINDER ────────────────────────────────────────
    story.append(section_header("WOUND CLASSIFICATION  —  Head & Neck", styles))
    story.append(Spacer(1, 0.2*cm))

    wc_data = [
        [
            Paragraph("CLASS", styles["table_head"]),
            Paragraph("PROCEDURE", styles["table_head"]),
            Paragraph("PROPHYLAXIS REQUIRED?", styles["table_head"]),
        ],
        [
            Paragraph("CLEAN", styles["table_cell_teal"]),
            Paragraph("Thyroidectomy, Parathyroidectomy, Parotidectomy (ext. approach), Lateral neck dissection only", styles["table_cell"]),
            Paragraph("NO postoperative antibiotics", styles["table_cell"]),
        ],
        [
            Paragraph("CLEAN-CONTAMINATED", styles["table_cell_teal"]),
            Paragraph("Oral cavity / oropharyngeal / laryngeal / hypopharyngeal resection, Total laryngectomy, Composite resection, Free flap with mucosal entry", styles["table_cell"]),
            Paragraph("YES — perioperative + postoperative", styles["table_cell_bold"]),
        ],
        [
            Paragraph("CONTAMINATED / DIRTY", styles["table_cell_teal"]),
            Paragraph("Infected tumour, Pharyngocutaneous fistula repair, Re-exploration infected wound", styles["table_cell"]),
            Paragraph("TREATMENT dose antibiotics (not prophylaxis)", styles["table_cell_bold"]),
        ],
    ]
    wc_col_widths = [3.5*cm, 9*cm, 5.5*cm]
    wc_table = Table(wc_data, colWidths=wc_col_widths, repeatRows=1)
    wc_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0),  TEAL),
        ("BACKGROUND",    (0,1), (-1,1),  GREEN_PALE),
        ("BACKGROUND",    (0,2), (-1,2),  ORANGE_PALE),
        ("BACKGROUND",    (0,3), (-1,3),  RED_PALE),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#cccccc")),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "MIDDLE"),
    ]))
    story.append(wc_table)
    story.append(Spacer(1, 0.35*cm))

    # ── STANDARD REGIMEN ─────────────────────────────────────────────────────
    story.append(section_header("STANDARD PERIOPERATIVE REGIMEN  (No Penicillin Allergy)", styles))
    story.append(Spacer(1, 0.2*cm))

    reg_data = [
        [
            Paragraph("TIMING", styles["table_head"]),
            Paragraph("DRUG", styles["table_head"]),
            Paragraph("DOSE & ROUTE", styles["table_head"]),
            Paragraph("FREQUENCY", styles["table_head"]),
        ],
        [
            Paragraph("Pre-op\n(30–60 min before incision)", styles["table_cell_bold"]),
            Paragraph("Inj. Amoxicillin-Clavulanate", styles["drug_name"]),
            Paragraph("1.2 g IV slow infusion", styles["dose"]),
            Paragraph("Single dose", styles["table_cell"]),
        ],
        [
            Paragraph("", styles["table_cell"]),
            Paragraph("Inj. Metronidazole", styles["drug_name"]),
            Paragraph("500 mg IV over 20 min", styles["dose"]),
            Paragraph("Single dose", styles["table_cell"]),
        ],
        [
            Paragraph("Intra-op\n(redose if surgery >3 h or blood loss >1500 mL)", styles["table_cell_bold"]),
            Paragraph("Inj. Amoxicillin-Clavulanate", styles["drug_name"]),
            Paragraph("1.2 g IV", styles["dose"]),
            Paragraph("Once (at 3 h mark)", styles["table_cell"]),
        ],
        [
            Paragraph("Post-op\n(continue as per duration table)", styles["table_cell_bold"]),
            Paragraph("Inj. Amoxicillin-Clavulanate", styles["drug_name"]),
            Paragraph("1.2 g IV", styles["dose"]),
            Paragraph("Every 8 hours", styles["table_cell"]),
        ],
        [
            Paragraph("", styles["table_cell"]),
            Paragraph("Inj. Metronidazole", styles["drug_name"]),
            Paragraph("500 mg IV", styles["dose"]),
            Paragraph("Every 8 hours", styles["table_cell"]),
        ],
        [
            Paragraph("Oral step-down\n(when tolerating orally)", styles["table_cell_bold"]),
            Paragraph("Tab. Amoxicillin-Clavulanate", styles["drug_name"]),
            Paragraph("625 mg oral", styles["dose"]),
            Paragraph("BD or TDS", styles["table_cell"]),
        ],
        [
            Paragraph("", styles["table_cell"]),
            Paragraph("Tab. Metronidazole", styles["drug_name"]),
            Paragraph("400 mg oral", styles["dose"]),
            Paragraph("TDS", styles["table_cell"]),
        ],
    ]
    reg_col = [4*cm, 5.5*cm, 4*cm, 4.5*cm]
    reg_table = Table(reg_data, colWidths=reg_col, repeatRows=1)
    reg_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0),  TEAL),
        ("BACKGROUND",    (0,1), (-1,2),  TEAL_PALE),
        ("BACKGROUND",    (0,3), (-1,3),  colors.HexColor("#fff8e1")),
        ("BACKGROUND",    (0,4), (-1,5),  GREY_LIGHT),
        ("BACKGROUND",    (0,6), (-1,7),  GREEN_PALE),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#cccccc")),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "MIDDLE"),
        ("SPAN",          (0,1), (0,2)),
        ("SPAN",          (0,4), (0,5)),
        ("SPAN",          (0,6), (0,7)),
    ]))
    story.append(reg_table)
    story.append(Spacer(1, 0.35*cm))

    # ── DURATION TABLE ───────────────────────────────────────────────────────
    story.append(section_header("DURATION OF POSTOPERATIVE ANTIBIOTICS  (Evidence-Based vs Indian Practice)", styles))
    story.append(Spacer(1, 0.2*cm))

    dur_data = [
        [
            Paragraph("PROCEDURE / SCENARIO", styles["table_head"]),
            Paragraph("EVIDENCE-BASED DURATION", styles["table_head"]),
            Paragraph("COMMON INDIAN PRACTICE", styles["table_head"]),
        ],
        [
            Paragraph("Clean surgery (thyroidectomy, parotidectomy — no mucosal entry)", styles["table_cell"]),
            Paragraph("None post-op", styles["table_cell_teal"]),
            Paragraph("None / single pre-op dose", styles["table_cell"]),
        ],
        [
            Paragraph("Clean-contaminated — primary closure, no flap", styles["table_cell"]),
            Paragraph("24 hours", styles["table_cell_teal"]),
            Paragraph("48–72 hours IV, then oral", styles["table_cell"]),
        ],
        [
            Paragraph("Composite resection with pedicled flap (PMMC, DP)", styles["table_cell"]),
            Paragraph("24–48 hours", styles["table_cell_teal"]),
            Paragraph("48–72 hours IV + 3–5 days oral", styles["table_cell"]),
        ],
        [
            Paragraph("Free flap — soft tissue only (ALT, RFFF, RFAF)", styles["table_cell"]),
            Paragraph("24–48 hours", styles["table_cell_teal"]),
            Paragraph("48–72 hours IV + 3–5 days oral", styles["table_cell"]),
        ],
        [
            Paragraph("Free flap — bony reconstruction (Fibula, Iliac crest) with osteosynthetic hardware", styles["table_cell"]),
            Paragraph("Up to 72 hours", styles["table_cell_teal"]),
            Paragraph("72 h IV + 5–7 days oral", styles["table_cell"]),
        ],
        [
            Paragraph("Total laryngectomy (high pharyngocutaneous fistula risk)", styles["table_cell"]),
            Paragraph("48–72 hours", styles["table_cell_teal"]),
            Paragraph("5–7 days (institutional practice)", styles["table_cell"]),
        ],
        [
            Paragraph("Prior chemoradiotherapy to field (impaired healing)", styles["table_cell_bold"]),
            Paragraph("48–72 hours (no RCT evidence for extension)", styles["table_cell"]),
            Paragraph("5–7 days common (no evidence base)", styles["table_cell_bold"]),
        ],
    ]
    dur_col = [6.5*cm, 5*cm, 6.5*cm]
    dur_table = Table(dur_data, colWidths=dur_col, repeatRows=1)
    dur_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0),  TEAL),
        ("BACKGROUND",    (0,1), (-1,1),  GREEN_PALE),
        ("ROWBACKGROUNDS",(0,2), (-1,-1), [WHITE, GREY_LIGHT]),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#cccccc")),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "MIDDLE"),
        ("BACKGROUND",    (0,7), (-1,7),  ORANGE_PALE),
    ]))
    story.append(dur_table)
    story.append(Spacer(1, 0.35*cm))

    # ── PENICILLIN ALLERGY ───────────────────────────────────────────────────
    story.append(section_header("PENICILLIN ALLERGY ALTERNATIVE REGIMEN", styles, bg=ORANGE))
    story.append(Spacer(1, 0.2*cm))

    allergy_data = [
        [
            Paragraph("TIMING", styles["table_head"]),
            Paragraph("DRUG", styles["table_head"]),
            Paragraph("DOSE & ROUTE", styles["table_head"]),
            Paragraph("FREQUENCY", styles["table_head"]),
        ],
        [
            Paragraph("Pre-op & Post-op", styles["table_cell_bold"]),
            Paragraph("Inj. Clindamycin", styles["drug_name"]),
            Paragraph("600 mg IV (900 mg if >70 kg)", styles["dose"]),
            Paragraph("Q8H", styles["table_cell"]),
        ],
        [
            Paragraph("", styles["table_cell"]),
            Paragraph("Inj. Ciprofloxacin", styles["drug_name"]),
            Paragraph("400 mg IV", styles["dose"]),
            Paragraph("Q12H (for GN coverage)", styles["table_cell"]),
        ],
        [
            Paragraph("Oral step-down", styles["table_cell_bold"]),
            Paragraph("Cap. Clindamycin", styles["drug_name"]),
            Paragraph("300 mg oral", styles["dose"]),
            Paragraph("Q8H", styles["table_cell"]),
        ],
        [
            Paragraph("", styles["table_cell"]),
            Paragraph("Tab. Ciprofloxacin", styles["drug_name"]),
            Paragraph("500 mg oral", styles["dose"]),
            Paragraph("BD", styles["table_cell"]),
        ],
    ]
    allergy_col = [4*cm, 5.5*cm, 4*cm, 4.5*cm]
    allergy_table = Table(allergy_data, colWidths=allergy_col, repeatRows=1)
    allergy_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0),  ORANGE),
        ("BACKGROUND",    (0,1), (-1,4),  ORANGE_PALE),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#cccccc")),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "MIDDLE"),
        ("SPAN",          (0,1), (0,2)),
        ("SPAN",          (0,3), (0,4)),
    ]))
    story.append(allergy_table)
    story.append(Spacer(1, 0.15*cm))
    story.append(Paragraph(
        "* Avoid Clindamycin monotherapy — high Gram-negative burden in Indian oral cancer patients means Gram-negative coverage (Ciprofloxacin) is essential.",
        styles["note"]
    ))
    story.append(Spacer(1, 0.35*cm))

    # ── MICROBIOLOGY IN INDIAN CONTEXT ───────────────────────────────────────
    story.append(section_header("INDIAN MICROBIOLOGY CONTEXT  (Tertiary Oncology Centre)", styles, bg=GREY_DARK))
    story.append(Spacer(1, 0.2*cm))

    micro_col = [(PAGE_W - 3*cm) / 2, (PAGE_W - 3*cm) / 2]

    left_data = [
        [Paragraph("COMMON ORGANISMS IN SSI (Indian data)", styles["table_head"])],
        [Paragraph("Gram-NEGATIVE (70.5% of isolates)*\n"
                   "  • Pseudomonas aeruginosa\n"
                   "  • Klebsiella pneumoniae\n"
                   "  • Escherichia coli\n"
                   "  • Acinetobacter baumannii (MDR)", styles["table_cell"])],
        [Paragraph("Gram-POSITIVE (27%)*\n"
                   "  • Staphylococcus aureus (incl. MRSA 38%)\n"
                   "  • Streptococcus spp.\n"
                   "  • Enterococcus spp.", styles["table_cell"])],
        [Paragraph("Anaerobes\n"
                   "  • Bacteroides spp., Fusobacterium\n"
                   "  • Peptostreptococcus", styles["table_cell"])],
        [Paragraph("Polymicrobial: 41.6% of infected wounds*", styles["table_cell_bold"])],
    ]
    right_data = [
        [Paragraph("EFFECTIVE AGENTS (Indian sensitivity data)", styles["table_head"])],
        [Paragraph("For Gram-NEGATIVE bacilli:\n"
                   "  • Amikacin (best GN coverage: 93%)\n"
                   "  • Piperacillin-Tazobactam (72%)\n"
                   "  • Imipenem (81%, reserve)\n"
                   "  • Gentamicin (73%)", styles["table_cell"])],
        [Paragraph("For Gram-POSITIVE cocci:\n"
                   "  • Chloramphenicol (97.6%)\n"
                   "  • Amikacin (97.3%)\n"
                   "  • Piperacillin-Tazobactam (88.2%)\n"
                   "  • Vancomycin (MRSA, reserve)", styles["table_cell"])],
        [Paragraph("For Anaerobes:\n"
                   "  • Metronidazole (first choice)\n"
                   "  • Amoxicillin-Clavulanate\n"
                   "  • Clindamycin (resistance rising)", styles["table_cell"])],
        [Paragraph("MDR organisms — early culture & de-escalation\nessential per antimicrobial stewardship", styles["table_cell_bold"])],
    ]

    left_tbl = Table(left_data, colWidths=[(PAGE_W-3*cm)/2 - 0.3*cm])
    left_tbl.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0),  GREY_DARK),
        ("BACKGROUND",    (0,1), (-1,-1), GREY_LIGHT),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#cccccc")),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "TOP"),
    ]))
    right_tbl = Table(right_data, colWidths=[(PAGE_W-3*cm)/2 - 0.3*cm])
    right_tbl.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0),  GREY_DARK),
        ("BACKGROUND",    (0,1), (-1,-1), GREY_LIGHT),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#cccccc")),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "TOP"),
    ]))

    micro_outer = Table([[left_tbl, right_tbl]], colWidths=[(PAGE_W-3*cm)/2, (PAGE_W-3*cm)/2])
    micro_outer.setStyle(TableStyle([
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
        ("LEFTPADDING",  (0,0), (-1,-1), 0),
        ("RIGHTPADDING", (0,0), (-1,-1), 0),
        ("TOPPADDING",   (0,0), (-1,-1), 0),
        ("BOTTOMPADDING",(0,0), (-1,-1), 0),
    ]))
    story.append(micro_outer)
    story.append(Paragraph(
        "* Balagopal PG et al. Pattern of Post-Operative Infections Among Oral Cavity Cancer Patients in a Tertiary Care Cancer Centre. "
        "Indian J Otolaryngol Head Neck Surg. 2022. PMID: 36452549",
        styles["note"]
    ))
    story.append(Spacer(1, 0.35*cm))

    # ── RISK FACTORS & SSI PREVENTION ────────────────────────────────────────
    story.append(section_header("RISK FACTORS FOR SSI  —  Indian H&N Oncology Context", styles))
    story.append(Spacer(1, 0.2*cm))

    risk_data = [
        [
            Paragraph("PATIENT FACTORS", styles["table_head"]),
            Paragraph("DISEASE FACTORS", styles["table_head"]),
            Paragraph("SURGICAL FACTORS", styles["table_head"]),
        ],
        [
            Paragraph(
                "  • Tobacco / betel nut use (altered oral flora)\n"
                "  • Malnutrition / serum albumin <3.5 g/dL\n"
                "  • Diabetes mellitus\n"
                "  • BMI <18 or >30\n"
                "  • Anaemia (Hb <10 g/dL)\n"
                "  • Age >65 years\n"
                "  • Immunosuppression\n"
                "  • Alcohol excess",
                styles["table_cell"]
            ),
            Paragraph(
                "  • Stage III–IV disease\n"
                "  • Prior radiotherapy to field\n"
                "  • Prior chemotherapy\n"
                "  • Tracheostomy (GN colonisation)\n"
                "  • Tumour necrosis / infection\n"
                "  • Previous neck surgery",
                styles["table_cell"]
            ),
            Paragraph(
                "  • Surgery >6 hours\n"
                "  • Blood loss >1500 mL\n"
                "  • Free flap reconstruction\n"
                "  • Bony reconstruction + hardware\n"
                "  • Inadequate pre-op antibiotic timing\n"
                "  • Emergency / unplanned surgery\n"
                "  • Pharyngeal / oesophageal entry",
                styles["table_cell"]
            ),
        ],
    ]
    risk_col = [(PAGE_W-3*cm)/3]*3
    risk_table = Table(risk_data, colWidths=risk_col, repeatRows=1)
    risk_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0),  TEAL),
        ("BACKGROUND",    (0,1), (-1,1),  TEAL_PALE),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#cccccc")),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "TOP"),
    ]))
    story.append(risk_table)
    story.append(Spacer(1, 0.35*cm))

    # ── ESCALATION — WHEN TO SUSPECT SSI ─────────────────────────────────────
    story.append(section_header("WHEN TO SUSPECT SSI & ESCALATE ANTIBIOTICS", styles, bg=RED))
    story.append(Spacer(1, 0.2*cm))

    esc_data = [
        [
            Paragraph("CLINICAL SIGNS", styles["table_head"]),
            Paragraph("ACTION", styles["table_head"]),
            Paragraph("ANTIBIOTIC ESCALATION", styles["table_head"]),
        ],
        [
            Paragraph(
                "  • Fever >38.5°C beyond POD 3\n"
                "  • Wound erythema / induration / discharge\n"
                "  • Rising WBC / CRP after initial fall\n"
                "  • Flap discolouration / venous congestion",
                styles["table_cell"]
            ),
            Paragraph(
                "  • Wound swab for C/S (aerobic + anaerobic)\n"
                "  • Blood cultures if septic\n"
                "  • Surgical review — explore/drain\n"
                "  • CT neck if deep space infection suspected",
                styles["table_cell"]
            ),
            Paragraph(
                "  • Switch to Piperacillin-Tazobactam 4.5 g IV Q6H\n"
                "  • Add Vancomycin 15 mg/kg IV Q12H if MRSA risk\n"
                "  • De-escalate once C/S results available\n"
                "  • Infectious Disease consult for MDR organisms",
                styles["table_cell"]
            ),
        ],
    ]
    esc_col = [(PAGE_W-3*cm)/3]*3
    esc_table = Table(esc_data, colWidths=esc_col, repeatRows=1)
    esc_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0),  RED),
        ("BACKGROUND",    (0,1), (-1,1),  RED_PALE),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#cccccc")),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "TOP"),
    ]))
    story.append(esc_table)
    story.append(Spacer(1, 0.35*cm))

    # ── KEY CLINICAL PEARLS ───────────────────────────────────────────────────
    story.append(section_header("KEY CLINICAL PEARLS  —  Indian Context", styles, bg=GREEN))
    story.append(Spacer(1, 0.2*cm))

    pearls = [
        ("<b>1. Gram-negative coverage is non-negotiable</b> — Indian H&N oncology SSIs are dominated by GNBs (70.5%). "
         "Clindamycin monotherapy is <b>inadequate</b> and associated with higher SSI rates in Indian studies."),
        ("<b>2. Ampicillin-Sulbactam / Amoxicillin-Clavulanate is the backbone</b> — covers GPC, anaerobes, and most GNBs. "
         "Superior to Clindamycin in multiple Indian and international series."),
        ("<b>3. Metronidazole is essential</b> — tobacco/betel nut users have a heavy anaerobic oral burden; "
         "Metronidazole uniquely penetrates abscess cavities and augments anaerobic coverage."),
        ("<b>4. Evidence supports ≤48 hours</b> — most international data shows no benefit beyond 24–48 h. "
         "Indian practice of 5–7 days <b>is not evidence-based</b> and promotes AMR."),
        ("<b>5. Pre-op timing matters most</b> — the most important determinant of SSI prevention "
         "is antibiotic administration <b>within 60 minutes before incision</b>."),
        ("<b>6. Culture-guided de-escalation</b> — send wound swabs at the first sign of infection; "
         "MDR organisms are common. Early ID consult for Pseudomonas, MRSA, or Acinetobacter."),
        ("<b>7. Nutritional optimisation</b> — correct albumin, Hb, and blood sugar pre-operatively; "
         "these non-antibiotic factors have equal or greater impact on SSI rates."),
    ]
    pearl_rows = [[Paragraph(p, styles["body"])] for p in pearls]
    pearl_table = Table(pearl_rows, colWidths=[PAGE_W - 3*cm])
    pearl_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,-1), GREEN_PALE),
        ("TOPPADDING",    (0,0), (-1,-1), 4),
        ("BOTTOMPADDING", (0,0), (-1,-1), 4),
        ("LEFTPADDING",   (0,0), (-1,-1), 8),
        ("RIGHTPADDING",  (0,0), (-1,-1), 8),
        ("LINEBELOW",     (0,0), (-1,-2), 0.3, colors.HexColor("#c8e6c9")),
    ]))
    story.append(pearl_table)
    story.append(Spacer(1, 0.35*cm))

    # ── ANTIMICROBIAL STEWARDSHIP REMINDER ───────────────────────────────────
    warn_data = [[
        Paragraph(
            "ANTIMICROBIAL STEWARDSHIP:  "
            "Extending prophylaxis beyond 72 hours is NOT recommended except for established infection. "
            "Prolonged prophylaxis INCREASES antimicrobial resistance, C. difficile risk, and drug costs "
            "without reducing SSI rates. Adopt culture-guided therapy and de-escalate early.",
            styles["warning"]
        )
    ]]
    warn_table = Table(warn_data, colWidths=[PAGE_W - 3*cm])
    warn_table.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,-1), RED_PALE),
        ("BOX",           (0,0), (-1,-1), 1.5, RED),
        ("TOPPADDING",    (0,0), (-1,-1), 7),
        ("BOTTOMPADDING", (0,0), (-1,-1), 7),
        ("LEFTPADDING",   (0,0), (-1,-1), 10),
        ("RIGHTPADDING",  (0,0), (-1,-1), 10),
    ]))
    story.append(warn_table)
    story.append(Spacer(1, 0.35*cm))

    # ── FOOTER ────────────────────────────────────────────────────────────────
    HRFlowable(width="100%", thickness=0.5, color=TEAL)
    footer_text = (
        "References: Balagopal PG et al. Indian J Otolaryngol HNS 2022 (PMID 36452549)  |  "
        "Haque R et al. Initiatives to Reduce SSI of the Head, Strathprints 2018  |  "
        "PMC12649332 (2025 Free Flap PAP Review)  |  "
        "Iowa HN Protocols 2023  |  MD Anderson Surgical Antibiotic Prophylaxis v11, 2024\n"
        "This quick reference is for educational use. Clinical decisions must incorporate local antibiogram, patient-specific factors, and institutional policy. "
        "Generated June 2026."
    )
    story.append(HRFlowable(width="100%", thickness=0.5, color=TEAL))
    story.append(Spacer(1, 0.15*cm))
    story.append(Paragraph(footer_text, styles["footer"]))

    doc.build(story)
    print(f"PDF created: {OUTPUT}")

if __name__ == "__main__":
    build_document()
Running Command

cd /home/daytona/workspace/hn-antibiotics && python3 generate_pdf.py 2>&1

Running Command

ls -lh /home/daytona/workspace/hn-antibiotics/HN_Oncology_Antibiotic_QuickRef.pdf

Your quick reference PDF is ready.

HN Oncology Antibiotic QuickRef

PDF Document · PDF

What's included in the PDF:

SectionContent
Wound ClassificationClean / Clean-contaminated / Dirty with procedure examples and colour coding
Standard RegimenAmoxicillin-Clavulanate + Metronidazole - pre-op, intra-op, post-op, oral step-down doses
Duration TableEvidence-based vs Indian practice duration for each procedure type (thyroidectomy to fibula flap)
Penicillin Allergy AlternativeClindamycin + Ciprofloxacin regimen with doses
Indian MicrobiologyTwo-column table - common organisms (70.5% GNB) + effective agents from Indian sensitivity data
SSI Risk FactorsPatient / Disease / Surgical factors specific to Indian H&N oncology
Escalation GuideWhen to suspect SSI, when to send cultures, what to escalate to (Pip-Tazo, Vancomycin)
7 Clinical PearlsKey takeaways including why Clindamycin monotherapy is inadequate in India
AMR Warning BoxStewardship reminder - no prophylaxis beyond 72 h without evidence
ReferencesPMID 36452549, Iowa HN Protocols, MD Anderson 2024, PMC 2025 review
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