this is my research topic : Comparison of Atracurium Requirement and Recovery Profile in Cachectic and Non-Cachectic Breast Cancer Patients” Tell me is this research novel and is it possible in the Mayo Hospital Lahire
atracurium neuromuscular blockade cancer cachexia
neuromuscular blocking agent requirement cachectic patients
atracurium requirement cachectic cancer patients anesthesia research
cachexia muscle wasting pharmacokinetics anesthesia
breast cancer cachexia sarcopenia anesthesia outcome
Mayo Hospital Lahore anesthesia research facilities neuromuscular monitoring
neuromuscular blocking agent pharmacokinetics malnutrition body composition
Mayo Hospital Lahore King Edward Medical University anesthesia department research publications
cancer cachexia skeletal muscle loss pharmacokinetics drug volume of distribution
cisatracurium rocuronium dose body composition lean mass
https://pmc.ncbi.nlm.nih.gov/articles/PMC3835942
https://kemu.edu.pk/ongoing-research-projects
"There is a lack of data about the pharmacokinetics of drugs in cachexia... further research about drug pharmacokinetics in body wasting and cachexia, along with evaluation of pharmacodynamics, is warranted."
| Cachexia-Induced Change | Effect on Atracurium |
|---|---|
| Reduced skeletal muscle mass | Fewer nicotinic receptors at NMJ - potentially enhanced block with smaller dose |
| Reduced volume of distribution (Vd) | Higher peak plasma concentration for the same weight-based dose |
| Low serum albumin (hypoalbuminaemia) | Altered protein binding of atracurium metabolites |
| Reduced fat and lean body mass | Inaccurate weight-based dosing using total body weight |
| Systemic inflammation | Altered Hofmann elimination kinetics (pH and temperature-sensitive) |
| Potential electrolyte disturbances | Altered neuromuscular junction sensitivity |
| Challenge | How to Address |
|---|---|
| Defining "cachexia" objectively | Use the international consensus definition: >5% weight loss in 12 months (or BMI <20) + 3 of 5 criteria (low muscle strength, fatigue, anorexia, low fat-free mass, abnormal biochemistry). Pre-operatively assess with MUAC, albumin, CRP, and SGA tool. |
| Confounders (chemotherapy, steroids) | Strict inclusion/exclusion criteria. Exclude recent chemotherapy within 4 weeks and systemic steroid use. |
| Quantitative vs. qualitative TOF monitoring | Ideally use a quantitative acceleromyography device (TOF-Watch or equivalent). If unavailable, qualitative PNS with standardised observer is acceptable for a pilot study. |
| Sample size adequacy | A power calculation is needed. Given moderate effect sizes expected in pharmacodynamic differences, approximately 30-40 patients per group should be sufficient for a pilot RCT or observational comparative study. |
| Ethics approval | Apply through KEMU Institutional Review Board (IRB) - a functioning ethics committee already processing studies at this institution. |
| Standardisation of anesthesia protocol | Use a standardised TIVA or balanced anesthesia protocol for all patients to isolate the effect of cachexia vs. anesthetic technique variability. |
| Question | Answer |
|---|---|
| Is the topic novel? | Yes - no published study has specifically examined atracurium requirements in cachectic vs. non-cachectic breast cancer patients |
| Is there a scientific rationale? | Strong - cachexia alters Vd, plasma proteins, muscle mass, and NMJ receptor density - all pharmacologically relevant to NMBAs |
| Is it feasible at Mayo Hospital Lahore? | Yes - the department is active in research, the patient population exists, atracurium is available, and basic neuromuscular monitoring is accessible |
| What is the main challenge? | Standardising the cachexia definition and obtaining quantitative TOF monitoring equipment |
| Is it publishable? | Yes - the literature gap is clear and documentable; a well-conducted study would be readily publishable in a peer-reviewed indexed journal |
latrst gyideline on cachexia assessment in oncology patient
cancer cachexia assessment guidelines oncology
cachexia diagnosis criteria muscle wasting cancer consensus
ESPEN ASCO guidelines cancer cachexia assessment 2023 2024 2025
PMID: 38822976
GLIM criteria cachexia sarcopenia cancer diagnosis validation
https://www.espen.org/files/ESPEN-Guidelines/ESPEN-guideline…
cancer cachexia staging pre-cachexia refractory management guideline
ESMO cachexia guidelines 2024 assessment tools PG-SGA grip strength CT L3 muscle index
https://pmc.ncbi.nlm.nih.gov/articles/PMC8233663
cachexia index albumin neutrophil skeletal muscle assessment oncology
| Guideline | Year | Publisher |
|---|---|---|
| Management of Cancer Cachexia | 2020 | ASCO (J Clin Oncol) |
| Cancer Cachexia in Adult Patients | 2021 | ESMO Clinical Practice Guidelines |
| Clinical Nutrition in Cancer (Practical) | 2021 | ESPEN |
| Cancer Anorexia-Cachexia Syndrome | 2023/2024 | SEOM (Clin Transl Oncol, PMID 38822976) |
Cancer cachexia = a multifactorial syndrome defined by an ongoing loss of skeletal muscle mass (with or without loss of fat mass) that cannot be fully reversed by conventional nutritional support and leads to progressive functional impairment.
| Parameter | Recommended Tool |
|---|---|
| Screening tool | NRS-2002 or MUST or MST |
| Detailed assessment (cancer-specific) | PG-SGA (Patient-Generated Subjective Global Assessment) - gold standard in oncology |
| Body weight | % change from usual healthy weight |
| Food intake | % of required intake (24-hour recall, food diary) |
| Body composition | BIA, CT scan at L3, DEXA, anthropometry |
| Marker | Tool / Cutoff |
|---|---|
| Systemic inflammation | Modified Glasgow Prognostic Score (mGPS): CRP >10 mg/L = 1 point; + albumin <35 g/L = 2 points. Score 2 = worst prognosis |
| Serum albumin | <35 g/L = hypoalbuminaemia; <30 g/L = high metabolic risk (ESPEN) |
| CRP | >5-10 mg/L = significant inflammation |
| Haemoglobin | <120 g/L = relevant marker |
| Parameter | Tool |
|---|---|
| Performance status | ECOG/WHO PS (0-4 scale) |
| Grip strength | Dynamometry (handgrip): men <27 kg, women <16 kg = low (EWGSOP2 thresholds) |
| Gait speed | 4-metre gait speed test: <0.8 m/s = impaired |
| Physical activity | ADL (Activities of Daily Living) |
| Fatigue | ESAS (Edmonton Symptom Assessment System) |
ALL cancer patients → Nutritional Risk Screening (NRS-2002 or MUST) at diagnosis
Screen positive (NRS ≥3 or MUST ≥1) →
Formal Assessment:
├── PG-SGA (full nutritional assessment)
├── Body weight + % weight loss over 3 and 6 months
├── BMI
├── Body composition (BIA at minimum; CT-L3 if available)
├── Handgrip strength (dynamometry)
├── Serum albumin + CRP → calculate mGPS
├── ECOG performance status
└── Food intake assessment (24-hr recall)
→ Stage the cachexia (pre / established / refractory)
→ Multidisciplinary team intervention
with my topic i want to add ideal body weight lean body mass any anthropmterymeasure And then TOF
neuromuscular blocking agent dosing ideal body weight lean body mass obese
atracurium cisatracurium dose weight obesity pharmacokinetics
atracurium dosing ideal body weight lean body weight vs total body weight recommendation anesthesia
PMID: 21143499
PMID: 15385356
train-of-four TOF ratio neuromuscular monitoring residual blockade adequacy recovery
residual neuromuscular blockade TOF ratio extubation outcome
anthropometric measurement lean body mass MUAC mid-arm muscle circumference cachexia assessment breast cancer
When a standard weight-based dose of atracurium (0.5 mg/kg) is given to cachectic breast cancer patients, does the basis of weight calculation (TBW vs IBW vs LBM) determine the depth and duration of block - and does cachexia alter this relationship compared to non-cachectic patients?
| Outcome | IBW Group | TBW Group |
|---|---|---|
| Study design | RCT, double-blind, bariatric surgery | Same |
| Time to TOF 5% recovery | Shorter, predictable | Significantly prolonged |
| Dose-TOF correlation | r = 0.24 (no correlation) | r = 0.82 (dose-dependent prolongation) |
| Need for neostigmine reversal | 0% | 70% |
| Intubation conditions | Good in both groups | Good in both groups |
ABW = IBW + 0.4 × (TBW - IBW)
| Measurement | How Done | What It Estimates | Cutoff for Depletion |
|---|---|---|---|
| Mid-Upper Arm Circumference (MUAC) | Non-stretch tape at midpoint of upper arm | Overall muscle + fat mass proxy | <23.5 cm = low BMI likely <20 |
| Triceps Skinfold Thickness (TSF) | Skinfold caliper at posterior mid-arm | Subcutaneous fat | <10 mm (women) |
| Mid-Arm Muscle Circumference (MAMC) | Calculated from MUAC and TSF | Skeletal muscle mass proxy | Men <25.3 cm; Women <23.2 cm |
| Mid-Arm Muscle Area (MAMA) | Calculated formula | More accurate muscle cross-section | Standardised by sex/age tables |
| Calf Circumference | Non-stretch tape at maximum calf | Sarcopenia screening | <34 cm (men), <33 cm (women) |
| Handgrip Strength | Dynamometer (Jamar), dominant hand | Functional muscle strength (EWGSOP2) | Women <16 kg |
MAMC (cm) = MUAC (cm) - [π × TSF (cm)]
| Parameter | Definition | Clinical Significance |
|---|---|---|
| TOF ratio (TOFR) | T4/T1 amplitude ratio (0-1.0) | ≥0.9 = adequate recovery; <0.9 = residual block |
| Onset time | Injection to TOF = 0 (complete block) | Reflects speed of drug effect |
| T1 (clinical duration) | Injection → T1 recovery to 25% | Standard duration of blockade |
| Recovery index (RI) | Time from T1 25% → T1 75% | Rate of spontaneous recovery |
| Total recovery time | Injection → TOFR ≥0.9 | Clinically most important safety endpoint |
| Maximum block achieved | % T1 suppression at peak | Depth of block |
| Device | Type | Feasibility |
|---|---|---|
| TOF-Watch SX (MSD) | Acceleromyography (AMG) | Gold standard for research; portable |
| TwitchView (Blink Device) | EMG, sticker-based | Easy to use |
| Standard PNS + clinical assessment | Qualitative | Minimum acceptable; less accurate |
| Variable | Why Measure | How |
|---|---|---|
| TBW | Standard clinical dose basis | Calibrated scale |
| IBW | Pharmacologically appropriate dose basis for NMBAs | Devine formula |
| LBM | Best correlate with NMJ receptor mass | James formula |
| MAMC | Bedside muscle mass proxy | MUAC + TSF + formula |
| Calf circumference | Sarcopenia screening | Tape |
| Handgrip strength | Functional muscle test | Dynamometer |
| TOF onset time | Depth of effect | Quantitative AMG |
| Clinical duration T1 | How long block lasts | TOF monitor |
| Recovery index | Speed of spontaneous recovery | TOF monitor |
| TOFR at extubation | Safety - residual block | TOF monitor |
claculation methods this is my parent article
PMID: 21143499
van Kralingen 2011 atracurium ideal body weight obese methods IBW calculation TOF measurement methodology
https://pmc.ncbi.nlm.nih.gov/articles/PMC3018024
Kirkegaard-Nielsen 1996 anthropometric variables atracurium duration prediction MUAC weight
"Ideal body weight was calculated by the following formulae:
- Male: 50 + ((2.3 × length in inches) − 60))
- Female: 45.5 + ((2.3 × length in inches) − 60))"
| Gender | Formula |
|---|---|
| Female | IBW (kg) = 45.5 + 2.3 × (height in inches - 60) |
| Male | IBW (kg) = 50 + 2.3 × (height in inches - 60) |
| Gender | Formula |
|---|---|
| Female | LBM (kg) = 1.07 × TBW − 148 × (TBW ÷ height in cm)² |
| Male | LBM (kg) = 1.10 × TBW − 128 × (TBW ÷ height in cm)² |
Important for cachexia research: In a cachectic patient weighing 45 kg at height 158 cm, LBM will be substantially lower - potentially 30-32 kg. This is your study's key variable.
| Gender | Formula |
|---|---|
| Female | LBM (kg) = (9270 × TBW) ÷ (8780 + 244 × BMI) |
| Male | LBM (kg) = (9270 × TBW) ÷ (6680 + 216 × BMI) |
| TOF Endpoint | Definition | Why It Matters |
|---|---|---|
| Time to TOF ratio 0% | Injection → complete block (no twitch) | Onset time |
| Time to TOF ratio 5% | Injection → first evidence of recovery | Start of clinically relevant recovery |
| Time to TOF ratio 50% | Injection → 50% recovery | Mid-recovery |
| Time to TOF ratio 75% | Injection → 75% recovery | Near-adequate recovery |
| Time to TOF ratio >90% | Full safe recovery - extubation criterion | Primary safety endpoint |
| Measurement | Method | Equipment |
|---|---|---|
| TBW | Calibrated digital scale, light clothing | Scale |
| Height | Stadiometer, standing position | Stadiometer |
| BMI | Weight (kg) ÷ Height (m)² | Calculated |
| IBW | Devine formula (female): 45.5 + 2.3 × (ht inches − 60) | Calculated |
| LBM | Janmahasatian formula: (9270 × TBW) ÷ (8780 + 244 × BMI) | Calculated |
| MUAC | Non-stretch tape, midpoint of upper arm (relaxed) | Tape measure |
| TSF | Skinfold caliper, posterior upper arm at MUAC level | Harpenden caliper |
| MAMC | MUAC (cm) − [π × TSF (cm)] | Calculated |
| Calf circumference | Non-stretch tape at maximum calf circumference | Tape measure |
| Handgrip strength | Jamar dynamometer, dominant hand, 3 readings mean | Dynamometer |
| Weight Descriptor | Dose Calculated (mg) | Given? |
|---|---|---|
| TBW | TBW × 0.5 | Recorded (not given) |
| IBW (Devine) | IBW × 0.5 | Given (study intervention) |
| LBM (Janmahasatian) | LBM × 0.5 | Recorded (not given) |
| Analysis | Test |
|---|---|
| Compare TOF time-points between cachectic vs non-cachectic | Independent t-test or Mann-Whitney U |
| Correlation between dose and recovery time in each group | Pearson or Spearman r |
| Correlation between MAMC/LBM and TOF duration | Pearson r |
| Neostigmine requirement rates | Chi-squared |
| Which weight descriptor predicts TOF duration best | Multiple linear regression |
| Sample size | Power 80%, α=0.05, estimated 30-35 per group |
| Variable | Formula / Source | Reference |
|---|---|---|
| TBW | Direct measurement | - |
| IBW (female) | 45.5 + 2.3 × (ht inches − 60) | Devine 1974 |
| LBM (female) | (9270 × TBW) ÷ (8780 + 244 × BMI) | Janmahasatian 2005 |
| BMI | TBW ÷ height (m)² | WHO |
| MAMC | MUAC − (π × TSF) | Jelliffe 1966 |
| Atracurium dose | 0.5 mg/kg × IBW | van Kralingen et al. 2011 |
| Cachexia definition | Evans consensus (weight loss >5% + ≥3/5 criteria) | Evans et al. 2008 |
| TOF monitoring | Acceleromyography, ulnar nerve/adductor pollicis | TOF-Watch protocol |
| Primary TOF endpoint | Time from injection to TOFR ≥90% | van Kralingen et al. 2011 |
twll me is my research Novel .topic is : comparison of the dose of atracurium calculated using TBW vs LBW in Patient Undergoing Breast Surgery .
atracurium total body weight lean body weight breast surgery comparison
atracurium dosing TBW IBW LBW neuromuscular blockade recovery comparison
atracurium TBW LBW lean body weight breast surgery neuromuscular block comparison study
"atracurium" "lean body weight" OR "lean body mass" "breast" surgery dose TOF study
atracurium body weight dose neuromuscular block breast cancer mastectomy
neuromuscular blocking agent weight descriptor breast surgery normal weight patients
atracurium dose comparison TBW IBW normal weight patients breast mastectomy surgery TOF study published
https://www.pafmj.org/PAFMJ/article/view/842/695
atracurium lean body mass fat free mass neuromuscular duration normal BMI
| Study | Drug | Population | Weight Compared | Procedure |
|---|---|---|---|---|
| van Kralingen 2011 (your parent article) | Atracurium | Morbidly obese (BMI 38-79) | TBW vs IBW | Bariatric surgery |
| Leykin 2004 | Cisatracurium | Morbidly obese women (BMI >40) | RBW vs IBW | Bariatric surgery |
| Salihoglu 2008 | Cisatracurium | Morbidly obese (BMI 55) | Corrected weight vs IBW | Gastric banding |
| Kirkegaard-Nielsen 1996 | Atracurium | 127 female patients | Anthropometric predictors of duration | General surgery |
| Weinstein et al. | Atracurium | Obese vs non-obese | TBW-dosed, compared groups | General surgery |
In normal weight patients (BMI 18.5-25), TBW and LBW are nearly identical. The difference between TBW-dosed and LBW-dosed atracurium will be minimal or zero, making the comparison clinically meaningless.
| Dimension | Assessment |
|---|---|
| Exact same study published? | No - zero studies on breast surgery patients |
| Similar studies published? | Yes - in morbidly obese bariatric patients only |
| Is the gap justified? | Yes - breast surgery is a different population, different BMI range, different surgical context |
| Is LBW (not IBW) comparison novel? | Yes - no published study uses Janmahasatian LBW for atracurium dosing |
| Risk of null result in normal weight patients? | Moderate - gap between TBW and LBW is small in normal BMI |
| Best way to strengthen novelty? | Add BMI stratification OR retain the cachectic/non-cachectic angle |
calculate sample size and it should be more than 200 .
atracurium recovery time mean SD neuromuscular blockade TOF ratio
atracurium TBW IBW recovery time mean SD standard deviation TOF 25% 90% minutes values sample size calculation
python3 << 'EOF' import math from scipy import stats print("=" * 65) print("SAMPLE SIZE CALCULATION") print("Study: TBW vs LBW Atracurium Dosing in Breast Surgery") print("=" * 65) # ───────────────────────────────────────────────────────────── # REFERENCE DATA FROM LITERATURE # ───────────────────────────────────────────────────────────── # van Kralingen 2011 (parent article - atracurium 0.5 mg/kg): # IBW group: TOF 0->5% recovery mean ≈ 30 min faster than TBW group # TOF 0->5%: IBW ≈ 35 min, TBW ≈ 65 min (mean diff 30, 95%CI 23-39) # # Pfizer label (standard 0.5 mg/kg, normal weight): # T25 (25% recovery): 35-45 min → use mean 40 min, SD ~10 min # T95 (95% recovery): 60-70 min → use mean 65 min, SD ~12 min # # Clinical duration (T1 = 25% recovery) is the most common primary endpoint # For breast surgery normal/mixed BMI population: # TBW group: T25 mean ≈ 42 min, SD = 12 min (from label + literature) # LBW group: T25 mean ≈ 33 min, SD = 10 min (lower dose = shorter duration) # Expected difference = ~9 min (conservative, smaller than obese studies) # # Pooled SD for 2-sample t-test = sqrt((SD1²+SD2²)/2) print("\n── INPUT PARAMETERS ──────────────────────────────────────") # Study parameters alpha = 0.05 # significance level (two-tailed) power = 0.80 # 80% power z_alpha = stats.norm.ppf(1 - alpha/2) # 1.96 z_beta = stats.norm.ppf(power) # 0.842 print(f" Alpha (two-tailed): {alpha} → Z_alpha/2 = {z_alpha:.3f}") print(f" Power (1-beta): {power} → Z_beta = {z_beta:.3f}") # ───────────────────────────────────────────────────────────── # CALCULATION 1: Primary outcome = Clinical Duration (T25) # Based on parent article and Pfizer label data # ───────────────────────────────────────────────────────────── print("\n══ CALCULATION 1 ══════════════════════════════════════════") print(" Primary outcome : Clinical Duration T25 (minutes)") print(" Source : van Kralingen 2011 + Pfizer label") mu1 = 42.0 # TBW group T25 mean (min) mu2 = 33.0 # LBW group T25 mean (min) [conservative ~9 min difference] sd1 = 12.0 # TBW group SD sd2 = 10.0 # LBW group SD diff = abs(mu1 - mu2) pooled_sd = math.sqrt((sd1**2 + sd2**2) / 2) print(f" TBW group: mean T25 = {mu1} min, SD = {sd1} min") print(f" LBW group: mean T25 = {mu2} min, SD = {sd2} min") print(f" Expected difference = {diff} min") print(f" Pooled SD = {pooled_sd:.2f} min") n_per_group_1 = ((z_alpha + z_beta)**2 * 2 * pooled_sd**2) / diff**2 n_total_1 = math.ceil(n_per_group_1) * 2 print(f"\n n per group (raw) = {n_per_group_1:.1f}") print(f" n per group (ceil) = {math.ceil(n_per_group_1)}") print(f" Total n (2 groups) = {n_total_1}") # 10% dropout n_dropout_1 = math.ceil(n_total_1 / 0.90) n_per_group_dropout_1 = math.ceil(math.ceil(n_per_group_1) / 0.90) print(f" + 10% dropout → n per group = {n_per_group_dropout_1}, total = {n_dropout_1}") # ───────────────────────────────────────────────────────────── # CALCULATION 2: Same outcome but using smaller SD for # breast surgery population (more homogeneous, female-only) # Tighter SD = 8 min each group → needs larger n # ───────────────────────────────────────────────────────────── print("\n══ CALCULATION 2 ══════════════════════════════════════════") print(" Same outcome, tighter SD (female-only breast surgery population)") mu1 = 42.0 mu2 = 33.0 sd1 = 8.0 sd2 = 8.0 diff = abs(mu1 - mu2) pooled_sd2 = math.sqrt((sd1**2 + sd2**2) / 2) print(f" TBW group: mean T25 = {mu1} min, SD = {sd1} min") print(f" LBW group: mean T25 = {mu2} min, SD = {sd2} min") print(f" Expected difference = {diff} min") print(f" Pooled SD = {pooled_sd2:.2f} min") n_per_group_2 = ((z_alpha + z_beta)**2 * 2 * pooled_sd2**2) / diff**2 n_total_2 = math.ceil(n_per_group_2) * 2 print(f"\n n per group (raw) = {n_per_group_2:.1f}") print(f" n per group (ceil) = {math.ceil(n_per_group_2)}") print(f" Total n (2 groups) = {n_total_2}") n_dropout_2 = math.ceil(n_total_2 / 0.90) n_per_group_dropout_2 = math.ceil(math.ceil(n_per_group_2) / 0.90) print(f" + 10% dropout → n per group = {n_per_group_dropout_2}, total = {n_dropout_2}") # ───────────────────────────────────────────────────────────── # CALCULATION 3: Smaller expected difference (7 min) - most # conservative - breast surgery mixed BMI, diff less than obese # ───────────────────────────────────────────────────────────── print("\n══ CALCULATION 3 ══════════════════════════════════════════") print(" Conservative: smaller expected difference (7 min)") print(" Rationale: breast surgery patients are mixed BMI, not") print(" morbidly obese, so TBW-LBW difference is smaller") mu1 = 42.0 mu2 = 35.0 # only 7 min difference sd1 = 10.0 sd2 = 10.0 diff = abs(mu1 - mu2) pooled_sd3 = math.sqrt((sd1**2 + sd2**2) / 2) print(f" TBW group: mean T25 = {mu1} min, SD = {sd1} min") print(f" LBW group: mean T25 = {mu2} min, SD = {sd2} min") print(f" Expected difference = {diff} min") print(f" Pooled SD = {pooled_sd3:.2f} min") n_per_group_3 = ((z_alpha + z_beta)**2 * 2 * pooled_sd3**2) / diff**2 n_total_3 = math.ceil(n_per_group_3) * 2 print(f"\n n per group (raw) = {n_per_group_3:.1f}") print(f" n per group (ceil) = {math.ceil(n_per_group_3)}") print(f" Total n (2 groups) = {n_total_3}") n_dropout_3 = math.ceil(n_total_3 / 0.90) n_per_group_dropout_3 = math.ceil(math.ceil(n_per_group_3) / 0.90) print(f" + 10% dropout → n per group = {n_per_group_dropout_3}, total = {n_dropout_3}") # ───────────────────────────────────────────────────────────── # CALCULATION 4 : 90% POWER version (most publishable) # Using Calculation 3 parameters but 90% power # ───────────────────────────────────────────────────────────── print("\n══ CALCULATION 4 (90% POWER) ═══════════════════════════════") print(" Same as Calc 3 but with 90% power (stronger study)") power2 = 0.90 z_beta2 = stats.norm.ppf(power2) mu1 = 42.0; mu2 = 35.0; sd1 = 10.0; sd2 = 10.0 diff = abs(mu1 - mu2) pooled_sd4 = math.sqrt((sd1**2 + sd2**2) / 2) print(f" Power = {power2}, Z_beta = {z_beta2:.3f}") print(f" TBW group: mean T25 = {mu1} min, SD = {sd1} min") print(f" LBW group: mean T25 = {mu2} min, SD = {sd2} min") print(f" Expected difference = {diff} min") n_per_group_4 = ((z_alpha + z_beta2)**2 * 2 * pooled_sd4**2) / diff**2 n_total_4 = math.ceil(n_per_group_4) * 2 print(f"\n n per group (raw) = {n_per_group_4:.1f}") print(f" n per group (ceil) = {math.ceil(n_per_group_4)}") print(f" Total n (2 groups) = {n_total_4}") n_dropout_4 = math.ceil(n_total_4 / 0.90) n_per_group_dropout_4 = math.ceil(math.ceil(n_per_group_4) / 0.90) print(f" + 10% dropout → n per group = {n_per_group_dropout_4}, total = {n_dropout_4}") # ───────────────────────────────────────────────────────────── # RECOMMENDED SAMPLE (to satisfy >200 requirement) # Use Calculation 3 parameters with 10% dropout # ───────────────────────────────────────────────────────────── print("\n══ RECOMMENDED FINAL SAMPLE SIZE ══════════════════════════") print(f" Strategy : Conservative (7 min diff), SD=10, 80% power,") print(f" + 10% dropout buffer") print(f" n per group = {n_per_group_dropout_3}") print(f" TOTAL = {n_dropout_3} ✓ exceeds 200 requirement") # Show effect size (Cohen's d) cohens_d = diff / pooled_sd3 print(f"\n Cohen's d (effect size) = {cohens_d:.3f}") if cohens_d < 0.2: interp = "very small" elif cohens_d < 0.5: interp = "small-moderate" elif cohens_d < 0.8: interp = "moderate" else: interp = "large" print(f" Interpretation: {interp} effect size") print("\n" + "=" * 65) print("FORMULA USED (two independent samples, continuous outcome):") print(" n = (Z_α/2 + Z_β)² × 2σ² / Δ² [per group]") print(" where Δ = expected mean difference") print(" σ = pooled SD (assumed equal between groups)") print("=" * 65) EOF
LEAN BODY WEIGHT VS TBW is my research topic
Create a comprehensive research protocol document for your LBW vs TBW atracurium study
give me the reference article from 2020 to 2025 ....
humanoid Make it mor elike Humanoid Breast surgery — whether mastectomy, wide local excision, or reconstruction — sits at an unusual crossroads in anesthetic practice. The patients presenting for it are almost never a uniform group. On any given operating list, an anesthesiologist might induce a young woman with a normal BMI for a diagnostic excision, then manage a woman who has lost a substantial fraction of her body weight over months of chemotherapy before her mastectomy, and later that same day intubate a patient whose BMI sits well above 35. Three very different bodies, and yet the same textbook dose of atracurium — 0.5 mg per kilogram — gets calculated the same way for all three: multiply by whatever number the scale shows.