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Health·P Space·Evidence-backed problem·Published 2026-09-19

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

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…

TRV-2026-1139Peer-reviewedPermanent record — 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

The quick read

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 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
  • 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.
  • Methods We retrospectively enrolled 2272 patients with heart failure with preserved ejection fraction and obstructive sleep apnea from 2 tertiary hospitals.
Problem

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.

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