Machine learning model for predicting 1-year and 3-year all-cause mortality in ischemic heart failure patients. Issue 8 (17th November 2022)

• Record Type: Journal Article
• Authors: Cai, Anping; Chen, Rui; Pang, Chengcheng; Liu, Hui; Zhou, Yingling; Chen, Jiyan; Li, Liwen
• Is Part Of: Postgraduate medicine. Volume 134:Issue 8(2022)
• Page Start: 810
• Page End: 819
• Publication Date: 2022-11-17
• Subjects: Heart failure -- prognosis -- machine learning -- risk model -- discrimination -- calibration; Medicine -- Periodicals; Medicine -- Periodicals; Medicine; Periodicals; 610.5
• DOI: 10.1080/00325481.2022.2115735
• Languages: English
• ISSNs: 0032-5481

Abstract

ABSTRACT: Objective: Machine 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. Method: Three 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. Results: Random 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. Conclusion: ML 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.