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TRV-2026-1139Certified recordPeer-reviewed

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. 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…

Health · P Space — documented harm · certified 2026-09-19 · v1 · article view · machine-readable

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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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Truvace Impact Record TRV-2026-1139, v1: “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.” Truvace, 2026-09-19. /record/TRV-2026-1139 (accessed at citation time). sha256 f50c2b77a02d8408…

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