aceMachine learning predicts major adverse kidney and cardiovascular events in heart failure with reduced ejection fraction
Background Heart failure with reduced ejection fraction (HFrEF) links to adverse kidney and cardiovascular events. However, current machine learning (ML)-based algorithms do not include comprehensive echocardiographic features or predict adverse kidney outcomes. Methods We built ML models to predict 3-year all-cause mortality, major adverse cardiovascular events (MACE), and major adverse kidney events (MAKE) in retrospective HFrEF cohorts. Models were developed from 1,486 patients at Taipei Veterans General Hosp…
Ensemble models using detailed clinical and echocardiographic features predicted 3-year mortality and cardiorenal events in HFrEF better than conventional scores, supporting earlier detection of high-risk patients.
The models were built and validated using retrospective cohorts, and the external validation group was small at 246 patients, so prospective performance is not shown.
Evidence
- Peer-reviewedKidney Research and Clinical Practice2026-10-06
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Truvace Impact Record TRV-2026-1828, v1: “Machine learning predicts major adverse kidney and cardiovascular events in heart failure with reduced ejection fraction.” Truvace, 2026-10-18. /record/TRV-2026-1828 (accessed at citation time). sha256 7f4bfe8a330cd0e0…
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