Machine learning model for predicting 1-year and 3-year all-cause mortality in ischemic heart failure patients

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.

KEYWORDS:

• Heart failure
• prognosis
• machine learning
• risk model
• discrimination
• calibration

Acknowledgments

We appreciate very much for the patients and their family and the nurses to support the current study.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

The current study was supported from the National Key Research and Development Program of China (2016YFC1301202), Guangdong Provincial People’s Hospital Clinical Research Fund (Y012018085), Guangdong Provincial Key Laboratory of Coronary Artery Disease Prevention Fund (Z02207016) and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application（No. 2022B1212010011).