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Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study

Shitao Feng1, Baochao Fan2, Juncai Bai1

  • 1Department of Cardiac Rehabilitation, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.

Annals of Medicine
|June 13, 2026
PubMed

Insights

Machine learning models can predict major adverse cardiovascular and cerebrovascular events (MACCE) in heart failure (HF) patients. Lower heart rate recovery (HRR1) and higher carbon dioxide ventilation equivalent slope (VE/VCO2 slope) are key predictors of adverse outcomes.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Biostatistics

Background:

  • Heart failure (HF) is a critical clinical syndrome resulting from myocardial damage, signifying the advanced stage of various cardiac conditions.
  • Early identification of high-risk individuals is essential for effective management and improved patient outcomes.
  • Predicting major adverse cardiovascular and cerebrovascular events (MACCE) in HF patients remains a significant clinical challenge.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting MACCE in patients with HF.
  • To identify key predictive features contributing to adverse cardiovascular and cerebrovascular events.
  • To enhance risk stratification and early intervention strategies for HF patients.

Main Methods:

  • A retrospective cohort study involving 271 HF patients, divided into training and testing sets.
  • Utilized cardiopulmonary exercise testing (CPET) parameters and laboratory data for model development.
  • Compared seven ML models and employed SHAP analysis for feature interpretability.

Main Results:

  • The Categorical Boosting (CatBoost) model exhibited superior performance in predicting MACCE.
  • Boruta feature selection identified four key predictive features.
  • Lower heart rate recovery at 1 minute (HRR1) and higher VE/VCO2 slope were significant predictors of MACCE.

Conclusions:

  • HRR1 and VE/VCO2 slope are independent predictors for MACCE incidence in chronic heart failure (CHF) patients.
  • ML models, particularly CatBoost, show promise for risk stratification in HF.
  • These findings can aid in early identification and management of high-risk HF patients.
Abstract

Keywords:
Chronic HFcardiopulmonary exercise testheart rate recoverymachine learning

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