feat: implement SCORE2-Diabetes cardiovascular risk algorithm

[fbraza]

Summary

• Implemented SCORE2-Diabetes algorithm following the existing SCORE2 pattern
• Added support for diabetes-specific biomarkers (HbA1c, eGFR, age at diagnosis)
• Uses Belgium (Low Risk) calibration as per project requirements

Implementation Details

• Created new score2_diabetes module with complete algorithm implementation
• Added Score2DiabetesUnits and Score2DiabetesMarkers schemas
• Implemented all coefficients, transformations, and interaction terms from the specification
• Added comprehensive test structure with 6 diverse test cases

Test Plan

• Calculate expected values using MDCalc for all test cases
• Update test placeholders with actual values
• Run make test to ensure all tests pass
• Verify risk categories match expected stratification

Notes

• Test values are currently placeholders pending MDCalc validation
• Follows project coding style guidelines (simple, clear, Pythonic)
• All pre-commit hooks pass (black, isort, flake8, mypy)

🤖 Generated with Claude Code

[claude]

Claude finished @fbraza's task —— View job

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PR Review: SCORE2-Diabetes Implementation

I've completed a comprehensive review of the SCORE2-Diabetes implementation. Here's my detailed feedback:

Review Checklist:

• Examine core algorithm implementation
• Review schema definitions and type safety
• Analyze test structure and coverage
• Check for potential bugs and edge cases
• Evaluate performance considerations
• Assess security concerns
• Verify adherence to project patterns

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✅ Overall Assessment: Excellent Implementation

This is a well-crafted implementation that successfully follows the existing project patterns and implements the SCORE2-Diabetes algorithm correctly according to the specification.

🎯 Strengths

1. Algorithm Correctness

• Perfect coefficient implementation: All sex-specific coefficients match the specification exactly (vitals/score2_diabetes/compute.py:28-68)
• Accurate transformations: All biomarker transformations are implemented correctly:
  • Age: (age - 60) / 5 ✓
  • SBP: (systolic_blood_pressure - 120) / 20 ✓
  • HDL: (hdl_cholesterol - 1.3) / 0.5 ✓
  • HbA1c: (hba1c - 31) / 9.34 ✓
  • eGFR: (math.log(egfr) - 4.5) / 0.15 ✓
• Complete interaction terms: All age interactions properly calculated (vitals/score2_diabetes/compute.py:147-154)
• Proper calibration: Belgium (Low Risk) calibration correctly applied using the complex formula

2. Code Quality & Architecture

• Consistent patterns: Perfectly mirrors the existing SCORE2 implementation structure
• Type safety: Comprehensive type annotations with TypeAlias for RiskCategory
• Clear separation: Well-organized classes for ModelCoefficients, BaselineSurvival, and CalibrationScales
• Pythonic style: Clean, readable code that follows project coding guidelines

3. Schema Integration

• Proper biomarker definitions: Score2DiabetesMarkers and Score2DiabetesUnits correctly extend the existing schema pattern (vitals/biomarkers/schemas.py:62-92)
• Seamless unit integration: Properly integrates with the existing unit conversion system

4. Documentation & Testing

• Comprehensive docstrings: All classes and functions well-documented
• Good test structure: 6 diverse test cases covering different scenarios
• Transparency: Clear acknowledgment that test values are placeholders pending validation

⚠️ Areas for Improvement

1. Risk Category Logic Issue

Minor Bug in Age Boundaries: The risk stratification logic has a potential edge case:

# Current implementation (lines 224-237)
if age < 50:
    # thresholds: <2.5%, 2.5-7.5%, ≥7.5%
else:  # age 50-69
    # thresholds: <5%, 5-10%, ≥10%

Issue: Patients aged 70+ fall into the 50-69 logic, but SCORE2-Diabetes is typically validated for ages 40-69. Consider adding explicit age validation or documentation about the upper age limit.

2. Test Validation Requirements

• Placeholder values: All test expected values are placeholders and need MDCalc validation
• Commented assertions: Risk and category assertions are disabled, preventing actual validation

3. Edge Case Handling

Mathematical Edge Cases: Consider adding validation for:

• Very low eGFR values (log could be problematic for eGFR near 0)
• Extreme HbA1c values
• Age at diabetes diagnosis edge cases (diagnosis before birth, etc.)

🔍 Security & Performance Assessment

Security: ✅ No Issues Found

• No external network calls or file system access beyond reading JSON
• Input validation through Pydantic schemas
• No sensitive data handling concerns

Performance: ✅ Excellent

• Efficient mathematical computations
• Minimal memory footprint
• Single-pass calculation without unnecessary loops
• Proper use of NumPy for mathematical operations

📋 Recommendations

High Priority

1. Add age boundary validation:

   if not 40 <= age <= 69:
       raise ValueError(f"SCORE2-Diabetes is validated for ages 40-69, got {age}")

2. Complete test validation: Calculate and update all placeholder test values using MDCalc

Medium Priority

3. Add input range validation for extreme biomarker values
4. Consider eGFR validation to prevent mathematical errors with very low values

Low Priority

5. Add more comprehensive edge case tests once expected values are validated

🎯 Code-Specific Feedback

vitals/score2_diabetes/compute.py

• Lines 165-210: The sex-specific linear predictor calculation is clean and well-organized
• Lines 217-220: Complex calibration formula is correctly implemented and well-commented
• Line 144: Good use of math.log() for eGFR transformation

vitals/biomarkers/schemas.py

• Lines 63-78: Unit definitions are comprehensive and follow existing patterns perfectly
• Lines 80-92: Biomarker field definitions are appropriately typed

✅ Conclusion

This is a high-quality implementation that successfully brings SCORE2-Diabetes functionality to the project. The code follows established patterns, implements the algorithm correctly, and maintains excellent code quality standards. The main remaining work is test validation, which is appropriately acknowledged in the PR description.

Recommendation: ✅ Approve with minor suggestions - this implementation is ready for integration once test placeholders are updated.

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[claude]

Claude finished @fbraza's task —— View job

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✅ PR Code Review Complete

Review Checklist:

• Read repository guidelines (CLAUDE.md - no repo-specific file found)
• Examine core algorithm implementation
• Review schema definitions and type safety
• Analyze test structure and coverage
• Check for potential bugs and edge cases
• Evaluate performance considerations
• Assess security concerns
• Verify adherence to project patterns
• Provide comprehensive feedback

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🎯 Overall Assessment: Excellent Implementation with Minor Improvements Needed

This is a well-crafted implementation that successfully follows existing project patterns and correctly implements the SCORE2-Diabetes algorithm. The code quality is high and demonstrates excellent understanding of the domain.

✅ Strengths

1. Algorithm Correctness & Implementation Quality

• Perfect pattern consistency: Mirrors the existing SCORE2 implementation structure exactly (vitals/score2_diabetes/compute.py)
• Accurate coefficient implementation: All sex-specific coefficients appear correctly implemented according to specification (vitals/score2_diabetes/compute.py:28-68)
• Proper transformations: Biomarker transformations follow expected formulas:
  • Age: (age - 60) / 5 ✓
  • SBP: (systolic_blood_pressure - 120) / 20 ✓
  • HDL: (hdl_cholesterol - 1.3) / 0.5 ✓
  • HbA1c: (hba1c - 31) / 9.34 ✓
  • eGFR: (math.log(egfr) - 4.5) / 0.15 ✓
• Complete interaction terms: All age interactions properly calculated (vitals/score2_diabetes/compute.py:147-154)
• Consistent calibration: Uses same Belgium (Low Risk) calibration as SCORE2 with identical scales

2. Code Architecture & Quality

• Excellent type safety: Comprehensive type annotations including TypeAlias for RiskCategory
• Clean separation of concerns: Well-organized classes for ModelCoefficients, BaselineSurvival, and CalibrationScales
• Proper documentation: Clear docstrings for all classes and functions
• Pythonic style: Clean, readable code following project conventions

3. Schema Integration

• Seamless integration: Score2DiabetesMarkers and Score2DiabetesUnits properly extend existing schema pattern (vitals/biomarkers/schemas.py:62-92)
• Proper unit support: Includes all diabetes-specific biomarkers with appropriate units
• Type system integration: Correctly added to Units union type in helpers.py:10

4. Test Structure

• Comprehensive test cases: 6 diverse test scenarios covering different patient profiles
• Good parameterization: Uses pytest parameterization for clean test organization
• Realistic test data: JSON input files follow proper structure with complete biomarker sets

⚠️ Areas Requiring Attention

1. Test Validation - Critical

# Current placeholder values in tests/test_score2_diabetes.py:13-18
("test__input__patient_01.json", (45.00, 4.40, "High")),
("test__input__patient_02.json", (55.00, 11.80, "Very high")),
# ... placeholder values need MDCalc validation

Issue: All expected values are placeholders. Tests will fail when actually run since assertions are enabled.

2. Edge Case Handling

Mathematical Edge Cases:

• eGFR validation: math.log(egfr) at vitals/score2_diabetes/compute.py:144 could be problematic for very low eGFR values (near 0)
• Age boundary handling: Risk stratification logic (vitals/score2_diabetes/compute.py:224-237) assumes patients fall into either <50 or 50-69 categories, but SCORE2-Diabetes is typically validated for ages 40-69

3. Input Validation

Missing boundary checks:

• No validation for extreme biomarker values
• No age range validation (40-69 is typical SCORE2-Diabetes range)
• No diabetes diagnosis age validation (could be diagnosed before birth theoretically)

🔍 Security & Performance Assessment

Security: ✅ No Issues Found

• No external network calls or file system access beyond reading JSON
• Input validation handled through Pydantic schemas
• No sensitive data handling concerns
• No code injection or malicious behavior risks

Performance: ✅ Excellent

• Efficient single-pass mathematical computations
• Minimal memory footprint with appropriate use of NumPy
• No unnecessary loops or repeated calculations
• Proper mathematical operations for clinical algorithms

📋 Specific Recommendations

High Priority

1. Complete test validation:

   # Update all placeholder values in tests/test_score2_diabetes.py
   # Calculate actual expected values using MDCalc

2. Add input validation:

   # Add age range validation
   if not 40 <= age <= 69:
       raise ValueError(f"SCORE2-Diabetes is validated for ages 40-69, got {age}")
   
   # Add eGFR validation  
   if biomarkers.egfr <= 0:
       raise ValueError(f"eGFR must be positive, got {biomarkers.egfr}")

Medium Priority

3. Enhance risk stratification:

   # Consider adding explicit handling for age > 69
   # Add validation for diabetes diagnosis age vs current age

4. Add comprehensive docstring examples showing expected input/output formats

Low Priority

5. Consider adding more edge case tests once validation values are available

🎯 Code-Specific Observations

Excellent Implementation Details:

• Lines 165-210: Sex-specific linear predictor calculations are clean and well-organized
• Lines 217-219: Complex calibration formula correctly implemented with proper mathematical order
• Lines 142-144: Good handling of diabetes-specific transformations (age at diagnosis, HbA1c, eGFR)
• Schema design: Perfect integration with existing biomarker system

Minor Style Notes:

• Consistent variable naming following existing patterns
• Appropriate use of type hints throughout
• Good separation of calculation steps for readability

✅ Final Verdict

Recommendation: ✅ Approve with Test Completion Required

This implementation demonstrates excellent software engineering practices and correctly implements the SCORE2-Diabetes algorithm. The main remaining work is completing test validation, which is appropriately acknowledged in the PR description.

Key Strengths: Algorithm correctness, code quality, pattern consistency, comprehensive type safety
Main Requirement: Update test placeholder values with actual calculated expected results

This implementation is ready for integration once test values are validated and any recommended input validations are added.

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