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This study developed an interpretable machine learning model (MLM-PREVENT) to improve cardiovascular risk prediction. The model enhanced the accuracy of the AHA-PREVENT equations in a local population while maintaining clinical interpretability.

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Area of Science:

  • Cardiovascular Medicine
  • Machine Learning in Healthcare
  • Health Informatics

Background:

  • Cardiovascular risk estimation is crucial for patient care.
  • Guideline-recommended risk models may have limitations when applied to local populations.
  • Local recalibration can address these limitations.

Purpose of the Study:

  • To develop a machine learning (ML) approach to enhance the American Heart Association's Predicting Risk of Cardiovascular Disease Events (AHA-PREVENT) equations for a local population.
  • To maintain clinical interpretability while augmenting model performance.
  • To provide an implementable tool for electronic health record systems.

Main Methods:

  • A cohort study utilized a New England-based electronic health record cohort (95,326 patients) from 2007-2016.
  • The AHA-PREVENT model was adapted using an Extreme Gradient Boosting (XGBoost) ML model (MLM-PREVENT).
  • The MLM was monotonically constrained to preserve known risk factor associations; discrimination, calibration, and reclassification were assessed.

Main Results:

  • MLM-PREVENT demonstrated improved calibration compared to the AHA-PREVENT model across various risk categories and sex subgroups.
  • Calibration was maintained or improved in Asian, Black, and White individuals.
  • Discrimination was comparable between MLM-PREVENT and AHA-PREVENT; MLM-PREVENT reclassified 11.5% of patients at a 7.5% risk threshold.

Conclusions:

  • An interpretable ML approach enhanced AHA-PREVENT's accuracy in a local population while preserving original risk associations.
  • This method can recalibrate other risk tools and is suitable for EHR integration.
  • The findings support improved cardiovascular risk assessment through localized, interpretable ML models.