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TRV-2026-1139Version 1 · Certified

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TRUVACE RECORD VERSION
record: TRV-2026-1139
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-19T06:53:28.128938Z
status: published
lens: p_space
sector: health
headline: 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
dek: 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…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: (none)
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.
limitation: 
tag: Evidence-backed problem
key_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.
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.

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.
sources:
- peer_reviewed | Journal of the American Heart Association | https://doi.org/10.1161/jaha.126.050956 | 2026-09-18
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