BMI & BSA Calculator

Overview

Clinical calculator for anthropometric measurements used in health assessment, obesity screening, and chemotherapy dosing calculations.

Key Capabilities:

• BMI Calculation: Standard and adjusted BMI formulas
• BSA Estimation: Multiple validated formulas (DuBois, Mosteller, Haycock)
• Weight Classification: WHO and CDC category assignment
• Dosing Support: Chemotherapy and medication dose calculations
• Pediatric Support: Age-appropriate norms and calculations
• Unit Flexibility: Metric and imperial input support

When to Use

✅ Use this skill when:

• Calculating chemotherapy doses requiring BSA (mg/m²)
• Screening for obesity or underweight in clinical practice
• Adjusting drug doses based on body composition
• Documenting baseline anthropometrics in patient charts
• Teaching medical students clinical calculations
• Quick assessment in resource-limited settings

❌ Do NOT use when:

• BMI alone for clinical diagnosis → Use comprehensive metabolic assessment
• Pregnancy weight assessment → Use gestational weight gain charts
• Pediatric growth evaluation → Use WHO/CDC growth charts with percentiles
• Body composition analysis → Use DEXA or bioimpedance
• Athletic/muscular patients → Consider body fat % instead of BMI

Integration:

• Upstream: ehr-semantic-compressor (patient data extraction), automated-soap-note-generator (vital signs)
• Downstream: drug-interaction-checker (dose calculation), medication-reconciliation (dosing verification)

Core Capabilities

1. BMI Calculation

Calculate Body Mass Index with classification:

from scripts.calculator import BMIBSACalculator

calc = BMIBSACalculator()

# Calculate BMI
result = calc.calculate_bmi(
    weight_kg=70,
    height_cm=175,
    age=45,
    sex="male"
)

print(f"BMI: {result.bmi:.1f} kg/m²")
print(f"Category: {result.category}")  # Normal weight
print(f"Ideal weight range: {result.ideal_weight_range}")

BMI Categories (WHO):

Adjusted BMI:

• BMI Prime: BMI / 25 (obesity severity index)
• Ponderal Index: BMI for tall/short individuals
• Age-adjusted: For elderly patients (>65)

2. BSA Calculation

Multiple formulas for different clinical scenarios:

# Calculate BSA using different formulas
bsa_results = calc.calculate_bsa(
    weight_kg=70,
    height_cm=175,
    formulas=["dubois", "mosteller", "haycock", "gehan_george"]
)

for formula, bsa in bsa_results.items():
    print(f"{formula}: {bsa:.2f} m²")

BSA Formulas:

3. Drug Dosing Calculations

Apply BSA to medication dosing:

# Calculate chemotherapy dose
dose = calc.calculate_dose(
    bsa=bsa_results["dubois"],
    drug="carboplatin",
    dose_per_m2=400,  # mg/m²
    max_dose=800  # mg cap
)

print(f"Calculated dose: {dose:.0f} mg")
print(f"BSA used: {bsa_results['dubois']:.2f} m²")

Common BSA-Based Doses:

• Carboplatin: AUC-based (Calvert formula)
• 5-FU: 400-600 mg/m²
• Doxorubicin: 60-75 mg/m² (lifetime max 450-550 mg/m²)
• Paclitaxel: 135-175 mg/m²

4. Pediatric Calculations

Age-appropriate calculations for children:

pediatric = calc.pediatric_mode(
    weight_kg=25,
    height_cm=120,
    age_years=8,
    sex="female"
)

print(f"BMI-for-age percentile: {pediatric.bmi_percentile}%")
print(f"Weight status: {pediatric.weight_status}")
print(f"BSA (Haycock): {pediatric.bsa:.2f} m²")

Pediatric Considerations:

• BMI percentiles (not absolute values)
• Growth chart integration
• Age-specific BSA formulas
• Body composition changes with development

Common Patterns

Pattern 1: Chemotherapy Dosing

Scenario: Calculate carboplatin dose for cancer patient.

# Calculate BSA and dose
python scripts/main.py \
  --weight 70 \
  --height 175 \
  --drug carboplatin \
  --target-auc 5 \
  --creatinine-clearance 80 \
  --output dose_calculation.txt

Output:

BSA (DuBois): 1.79 m²
Calvert Formula: Dose = Target AUC × (GFR + 25)
                 = 5 × (80 + 25)
                 = 525 mg
Maximum dose check: 525 mg ≤ 800 mg ✓
Recommended dose: 525 mg

Pattern 2: Obesity Screening

Scenario: BMI assessment for weight management clinic.

# BMI with full assessment
assessment = calc.assess_bmi(
    weight_kg=95,
    height_cm=165,
    age=52,
    sex="female",
    waist_cm=98
)

print(f"BMI: {assessment.bmi:.1f} (Obese Class II)")
print(f"Waist-to-height ratio: {assessment.whtr:.2f} (High risk)")
print(f"Comorbidity risk: {assessment.health_risk}")
print(f"Recommended: {assessment.recommendations}")

Pattern 3: Pediatric Growth Assessment

Scenario: Calculate child's BSA for medication dosing.

# Pediatric dosing
child = calc.pediatric_assessment(
    weight_kg=20,
    height_cm=110,
    age_years=6,
    sex="male"
)

print(f"BSA: {child.bsa:.2f} m² (Haycock formula)")
print(f"BMI percentile: {child.bmi_percentile}th")
print(f"Doxorubicin dose: {child.bsa * 60:.0f} mg")

Pattern 4: Rapid Clinical Assessment

Scenario: Quick BMI/BSA for admission vital signs.

# Quick calculation
python scripts/main.py --weight 80 --height 180 --quick

# Output:
# BMI: 24.7 kg/m² (Normal)
# BSA: 2.00 m² (DuBois)
# Ideal weight: 65-80 kg

Complete Workflow Example

Comprehensive patient assessment:

from scripts.calculator import BMIBSACalculator
from scripts.reports import ClinicalReport

# Initialize
calc = BMIBSACalculator()
report = ClinicalReport()

# Patient data
patient = {
    "weight_kg": 75,
    "height_cm": 170,
    "age": 55,
    "sex": "female",
    "waist_cm": 88
}

# Calculate all metrics
bmi = calc.calculate_bmi(**patient)
bsa = calc.calculate_bsa(**patient, formula="dubois")
assessment = calc.comprehensive_assessment(**patient)

# Generate report
report_data = {
    "bmi": bmi,
    "bsa": bsa,
    "assessment": assessment,
    "recommendations": assessment.recommendations
}

report.generate(report_data, output="patient_assessment.pdf")

Quality Checklist

Input Validation:

• Weight realistic (2-300 kg range)
• Height realistic (50-250 cm range)
• Units clearly specified (kg/lbs, cm/in)
• Age appropriate for formulas used

Calculation Accuracy:

• Formula selection appropriate for patient
• BSA formula matches clinical context
• Pediatric vs. adult norms correctly applied
• Rounding appropriate (1-2 decimal places)

Clinical Interpretation:

• CRITICAL: BMI is screening tool, not diagnostic
• Ethnicity-specific cutoffs considered
• Muscle mass considered (athletes)
• Age adjustments applied (elderly/children)

Documentation:

• Formula used documented (DuBois vs. Mosteller)
• Units clearly stated
• Date of calculation recorded
• Dose limits verified for chemotherapy

Common Pitfalls

Calculation Errors:

• ❌ Unit confusion → Pounds vs. kg, inches vs. cm

  • ✅ Always verify units; convert if necessary

• ❌ Wrong formula → Using adult BSA for infants

  • ✅ Use Haycock for children \x3C 12 years

• ❌ BMI over-interpretation → Diagnosing based on BMI alone

  • ✅ BMI is screening tool; clinical correlation required

Clinical Misuse:

• ❌ Athletes misclassified → Muscular patients marked obese

  • ✅ Consider waist circumference or body fat %

• ❌ Elderly inappropriate norms → Same cutoffs for all ages

  • ✅ Use age-adjusted BMI for >65 years

• ❌ Ignoring ethnicity → Universal cutoffs applied

  • ✅ Asian populations: lower obesity thresholds

Dosing Errors:

• ❌ BSA rounding → 1.79 m² rounded to 1.8 m²

  • ✅ Use precise values for chemotherapy

• ❌ Max dose ignored → Exceeding lifetime limits

  • ✅ Always check cumulative doses (doxorubicin)

References

Available in references/ directory:

• bsa_formulas_comparison.md - Formula accuracy by population
• pediatric_norms.md - Growth charts and percentiles
• chemotherapy_dosing.md - BSA-based drug calculations
• ethnic_adjustments.md - Population-specific cutoffs
• calculator_validation.md - Comparison with reference standards

Scripts

Located in scripts/ directory:

• main.py - CLI calculator interface
• calculator.py - Core BMI/BSA calculations
• formulas.py - Multiple BSA formula implementations
• pediatric.py - Child-specific calculations
• dosing.py - Medication dose calculations
• reports.py - Clinical report generation

Limitations

• BMI Limitations: Doesn't distinguish fat from muscle; varies by ethnicity
• BSA Estimation: All formulas are approximations; 10-15% variation normal
• Extreme Values: Very short/tall patients may have inaccurate estimates
• Not for Diagnosis: BMI/BSA are tools, not clinical diagnoses
• Amputees: Standard formulas inaccurate; adjustment needed
• Pregnancy: Special considerations not included

Parameters

Usage

Basic Usage

# Calculate BMI and BSA
python scripts/main.py --weight 70 --height 175

# Calculate with drug dosing
python scripts/main.py --weight 70 --height 175 --dose 100

# Output as JSON
python scripts/main.py --weight 70 --height 175 --format json --output results.json

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access
• Input validation for weight/height
• Output does not expose sensitive information
• Error messages sanitized
• Script execution in sandboxed environment

Prerequisites

# Python 3.7+
# No additional packages required (uses standard library)

Evaluation Criteria

Success Metrics

• Successfully calculates BMI using standard formula
• Successfully calculates BSA using DuBois formula
• Correctly categorizes BMI (Underweight, Normal, Overweight, Obese)
• Calculates drug doses based on BSA when provided

Test Cases

1. Normal Adult: 70kg, 175cm → BMI 22.9 (Normal), BSA ~1.85 m²
2. Drug Dosing: 70kg, 175cm, 100mg/m² → Dose 185mg
3. JSON Output: Valid JSON with all fields

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Add additional BSA formulas (Haycock, Mosteller)
  • Add pediatric BMI percentiles
  • Add unit conversion (lbs, ft/in)

⚕️ Clinical Note: BMI and BSA are screening and calculation tools, not substitutes for clinical judgment. Always correlate with physical examination, patient history, and other assessments. Double-check all chemotherapy calculations independently.