• Anping Cai, Rui Chen, Chengcheng Pang, Hui Liu, Yingling Zhou, Jiyan Chen, and Liwen Li.
• Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
• Postgrad Med. 2022 Nov 1; 134 (8): 810-819.

ObjectiveMachine learning (ML) model has not been developed specifically for ischemic heart failure (HF) patients. Whether the performance of ML model is better than the MAGGIC risk score and NT-proBNP is unknown. The current study was to apply ML algorithm to build risk model for predicting 1-year and 3-year all-cause mortality in ischemic HF patient and to compare the performance of ML model with the MAGGIC risk score and NT-proBNP.MethodThree ML algorithms without and with feature selection were used for model exploration, and the performance was determined based on the area under the curve (AUC) in five-fold cross-validation. The best performing ML model was selected and compared with the MAGGIC risk score and NT-proBNP. The calibration of ML model was assessed by the Brier score.ResultsRandom forest with feature selection had the highest AUC (0.742 and 95% CI: 0.697-0.787) for predicting 1-year all-cause mortality, and support vector machine without feature selection had the highest AUC (0.732 and 95% CI: 0.694-0.707) for predicting 3-year all-cause mortality. When compared to the MAGGIC risk score and NT-proBNP, ML model had a comparable AUC for predicting 1-year (0.742 vs 0.714 vs 0.694) and 3-year all-cause mortality (0.732 vs 0.712 vs 0.682). Brier scores for predicting 1-year and 3-year all-cause mortality were 0.068 and 0.174, respectively.ConclusionML models predicted prognosis in ischemic HF with good discrimination and well calibration. These models may be used by clinicians as a decision-making tool to estimate the prognosis of ischemic HF patients.

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