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Researchers build AI model to predict heart attack risk in heart failure patients with sleep apnea

Study title: Multivariate Machine Learning Model for Long-Term Risk Prediction of Acute Coronary Syndrome in Patients With Heart Failure With Preserved Ejection Fraction and Obstructive Sleep Apnea

Background Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructive sleep apnea further increases the risk of acute coronary syndrome. However, effective tools for long-term acute coronary syndrome risk stratification in this population remain limited.

Read the study abstract

Background Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructive sleep apnea further increases the risk of acute coronary syndrome. However, effective tools for long-term acute coronary syndrome risk stratification in this population remain limited. This study aimed to develop and externally validate a machine learning-based prognostic model for predicting acute coronary syndrome risk at multiple time points in patients with heart failure with preserved ejection fra…

By the Truvace desk1 min read1 sourceEvidence: Moderate · 70/100 — Controlled or validated comparison · n = 2,272 · 1 independent sourcePeer-reviewedTRV-2026-1139· cite & verify
Multivariate Machine Learning Model for Long-Term Risk Prediction of Acute Coronary Syndrome in Patients With Heart Failure With Preserved Ejection Fraction and Obstructive Sleep Apnea
Long-term consistency of personality traits of cattle by Neave, Heather W.; Costa, Joao H. C.; Weary, Daniel M.; von Keyserlingk, Marina A. G.. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0

In brief

This study aimed to develop and externally validate a machine learning-based prognostic model for predicting acute coronary syndrome risk at multiple time points in patients with heart failure with preserved ejection fraction and obstructive sleep apnea. Methods We retrospectively enrolled 2272 patients with heart failure with preserved ejection fraction and obstructive sleep apnea from 2 tertiary hospitals.

Main points

  1. Background Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructive sleep apnea further increases the risk of acute coronary syndrome.
  2. However, effective tools for long-term acute coronary syndrome risk stratification in this population remain limited.
  3. Methods We retrospectively enrolled 2272 patients with heart failure with preserved ejection fraction and obstructive sleep apnea from 2 tertiary hospitals.

The gain

Researchers developed and externally validated a machine learning model to predict acute coronary syndrome risk in patients with HFpEF and sleep apnea, filling a gap in risk tools.

The rundown

Background Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructive sleep apnea further increases the risk of acute coronary syndrome. However, effective tools for long-term acute coronary syndrome risk stratification in this population remain limited.

Sources

  1. Peer-reviewedJournal of the American Heart Association2026-09-18

The debate