cvd-risk 0.1.3

Production-grade Python package for cardiovascular disease risk calculation

PyCVDRisk 🫀

Cardiovascular Disease Risk Calculator

A comprehensive Python library for CVD risk assessment

📖 Documentation • 🚀 Quick Start • 💬 Discussions

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✨ What is PyCVDRisk?

PyCVDRisk is a production-ready Python package that implements major cardiovascular disease risk prediction models. Whether you're a researcher analyzing biobank data or a clinician integrating risk assessment into your workflow, PyCVDRisk provides accurate, validated, and easy-to-use CVD risk calculations.

🎯 Key Features

• 7 Production-Ready Models: SCORE2, ASCVD, Framingham, QRISK3, SMART2, WHO CVD, Globorisk
• Batch Processing: Handle thousands of patients efficiently with vectorized operations
• Type-Safe: Full type hints and validation with Pydantic
• Well-Tested: Comprehensive test suite with high code coverage
• Research-Grade: Based on peer-reviewed algorithms and clinical guidelines

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🚀 Installation

pip install cvd-risk

That's it! PyCVDRisk is now ready to use.

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📖 Quick Start

Calculate Risk for One Patient

from cvd_risk import SCORE2

# Create a patient profile
patient = {
    'age': 55,
    'sex': 'male',
    'systolic_bp': 140,
    'total_cholesterol': 6.0,
    'hdl_cholesterol': 1.2,
    'smoking': True,
    'region': 'moderate'
}

# Calculate 10-year CVD risk
result = SCORE2(**patient)

print(f"Risk Score: {result.risk_score:.1f}%")
print(f"Risk Category: {result.risk_category}")

Output:

Risk Score: 10.6%
Risk Category: Moderate risk

Batch Processing for Multiple Patients

import pandas as pd
from cvd_risk import SCORE2

# Load your patient data
df = pd.DataFrame({
    'age': [45, 60, 55, 70],
    'sex': ['female', 'male', 'male', 'female'],
    'systolic_bp': [120, 150, 135, 145],
    'total_cholesterol': [5.5, 7.2, 6.1, 4.8],
    'hdl_cholesterol': [1.8, 1.0, 1.3, 1.5],
    'smoking': [False, True, False, True],
    'region': ['low', 'high', 'moderate', 'moderate']
})

# Calculate risks for all patients
results = SCORE2.batch_calculate(**df)

# Add results to your dataframe
df['cvd_risk_10y'] = results['risk_score']
df['risk_category'] = results['risk_category']

print(df)

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🏥 Available Models

Model	Population	Primary Use	Status
SCORE2	European adults (40-69)	Primary prevention	✅ Production Ready
ASCVD	US/International adults	Primary prevention	✅ Production Ready
Framingham	US adults	Primary prevention	✅ Production Ready
QRISK3	UK primary care	Primary prevention	✅ Production Ready
SMART2	Secondary prevention	Recurrent CVD risk	✅ Production Ready
WHO CVD	Global populations	Primary prevention	✅ Production Ready
Globorisk	182 countries	Primary prevention	✅ Production Ready

More models coming soon: SCORE2-OP, PREVENT, LifeCVD2, and others

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🔬 Usage Examples

Clinical Integration

from cvd_risk import SCORE2, ASCVD

# Compare multiple models for the same patient
patient = {
    'age': 65, 'sex': 'male', 'systolic_bp': 150,
    'total_cholesterol': 6.5, 'hdl_cholesterol': 1.1,
    'smoking': False, 'region': 'high'
}

score2_risk = SCORE2(**patient).risk_score
ascvd_risk = ASCVD(**patient).risk_score

print(f"SCORE2: {score2_risk:.1f}%")
print(f"ASCVD: {ascvd_risk:.1f}%")

Epidemiological Research

import pandas as pd
from cvd_risk import SCORE2

# Process large cohorts efficiently
biobank_data = pd.read_csv('large_cohort.csv')  # 100K+ patients

# Calculate risks (takes ~1 second for large datasets)
risks = SCORE2.batch_calculate(**biobank_data)

# Statistical analysis
high_risk = (risks['risk_score'] > 10).sum()
total_patients = len(biobank_data)
high_risk_percentage = (high_risk / total_patients) * 100

print(f"High-risk patients: {high_risk:,} ({high_risk_percentage:.1f}%)")

Model Comparison

from cvd_risk import SCORE2, Framingham, QRISK3
import numpy as np

# Compare model predictions across a population
ages = np.random.normal(55, 10, 1000)
# ... generate synthetic population data ...

results = {
    'SCORE2': SCORE2.batch_calculate(age=ages, ...),
    'Framingham': Framingham.batch_calculate(age=ages, ...),
    'QRISK3': QRISK3.batch_calculate(age=ages, ...)
}

# Analyze agreement between models
# Statistical analysis code here...

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📚 Documentation

• Full Documentation - Complete API reference and guides
• Model Details - Clinical background for each algorithm
• Validation - How we ensure accuracy
• Contributing Guide - Add new models or improve the package

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🤝 Contributing

We welcome contributions! Here's how to get involved:

Ways to Contribute

• 🐛 Report bugs via GitHub Issues
• 💡 Suggest features in Discussions
• 📝 Improve documentation by editing files
• 🧪 Add test cases or validation data
• 🔧 Implement new risk models

Development Setup

# Clone the repository
git clone https://github.com/m-aljasem/PyCVDRisk.git
cd PyCVDRisk

# Install in development mode with all dependencies
pip install -e .[dev]

# Run tests
python build_script.py test

# Run all checks (linting + tests)
python build_script.py check

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📄 License

MIT License - Free for academic, clinical, and commercial use.

Just cite us in your research! 📚

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🙏 Acknowledgments

• European Society of Cardiology - SCORE2 algorithm
• Scientific Python Community - NumPy, Pandas, SciPy
• Open Source Contributors - Making research software accessible

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Built with ❤️ for cardiovascular research and clinical practice

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