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June 20, 2026BMC Cardiovascular DisordersOpen Access

Development and validation of an interpretable machine learning model for predicting 5-year major adverse cardiovascular events in patients with coronary artery disease

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Authors

ZJZ H E N G M JiangHZHaofeng ZhouYLYindu Liu

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Overview

Randomized trial develops and validates a machine learning model predicting five-year major adverse cardiovascular events in coronary artery disease patients, indicating clinical applicability.

Key Points

  • This study aims to develop and validate interpretable machine learning models for predicting major adverse cardiovascular events in patients with coronary artery disease.
  • Included a prospective cohort of 705 coronary artery disease patients, randomly divided into training (n=494) and validation (n=211) sets.
  • Key predictors identified using LASSO regression; four survival-based models developed.
  • Model performance assessed using discrimination, calibration, and decision curve analysis. SHAP analysis enhanced interpretability.
  • A total of 221 patients (31.3%) developed major adverse cardiovascular events during follow-up.
  • The random survival forest model demonstrated a C-index of 0.804 (95% CI: 0.770-0.837) in the training cohort and 0.710 (95% CI: 0.650-0.768) in the validation cohort.
  • SHAP analysis identified left ventricular ejection fraction, age, and number of diseased vessels as key predictors of events.

Cite This Study

Jiang et al. (2026) studied this question.

synapsesocial.com/papers/6a362e62db0793dc1a536211https://doi.org/10.1186/s12872-026-06103-1
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