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Published on: August 8, 2022

Machine Learning-based Model for Major Adverse Cardiac Event Prediction in Patients with Hypertrophic Cardiomyopathy

Thomas Geyer1,2,3, Christopher McIntosh1,2,4, Vishesh Sood1,2

  • 1Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto General Hospital, University Health Network (UHN), Toronto, Canada.

Radiology. Cardiothoracic Imaging
|June 4, 2026
PubMed

Insights

A new machine learning model effectively predicts major adverse cardiac events (MACEs) in hypertrophic cardiomyopathy (HCM) patients. Key cardiac MRI findings significantly improve risk stratification for heart conditions.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Machine Learning

Background:

  • Hypertrophic cardiomyopathy (HCM) is a significant cause of cardiac morbidity and mortality.
  • Accurate risk stratification for major adverse cardiac events (MACEs) is crucial for managing HCM patients.
  • Existing risk models may not fully incorporate advanced imaging and machine learning capabilities.

Purpose of the Study:

  • To develop and validate a machine learning-based model for predicting MACEs in patients with HCM.
  • To identify key predictors of MACEs, particularly those derived from cardiac magnetic resonance imaging (CMR).
  • To compare the performance of the machine learning model against established risk stratification tools.

Main Methods:

  • Retrospective cohort study of 604 patients undergoing CMR for HCM evaluation (2015-2022).
  • Cardiac MRI sequences included cine, T1/T2 mapping, and late gadolinium enhancement (LGE).
  • A penalized Cox proportional hazards model (CoxNet) was trained using 33 variables, including clinical, genetic, echocardiographic, and CMR data, with cross-validation.

Main Results:

  • The CoxNet model achieved a C-index of 0.75 for MACE prediction, demonstrating favorable performance.
  • Model performance was comparable to the 2014 European Society of Cardiology sudden cardiac death risk model (C-index 0.67, P=.07).
  • Significant predictors included apical aneurysm, LV end-systolic volume index, extensive LGE, native T1 z-score, and male sex.

Conclusions:

  • A machine learning model utilizing routinely available clinical and CMR variables accurately predicts MACEs in HCM.
  • Cardiac MRI features are critical determinants for improving risk stratification in hypertrophic cardiomyopathy.
  • This model offers a promising tool for enhanced MACE prediction and personalized patient management.

Keywords:
CardiacHeartHyperplasiaHypertrophyMRIMachine LearningModel Training

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