Development of heart failure risk prediction models based on a multi-marker approach using random forest algorithms

Abstract

Background:

The early identification of heart failure (HF) risk may favorably affect outcomes, and the combination of multiple biomarkers may provide a more comprehensive and valuable means for improving the risk of stratification. This study was conducted to assess the importance of individual cardiac biomarkers creatine kinase MB isoenzyme (CK-MB), B-type natriuretic peptide (BNP), galectin-3 (Gal-3) and soluble suppression of tumorigenicity-2 (sST2) for HF diagnosis, and the predictive performance of the combination of these four biomarkers was analyzed using random forest algorithms.

Methods:

A total of 193 participants (80 patients with HF and 113 age- and gender-matched healthy controls) were included from June 2017 to December 2017. The correlation and regression analysis were conducted between cardiac biomarkers and echocardiographic parameters. The accuracy and importance of these predictor variables were assessed using random forest algorithms.

Results:

Patients with HF exhibited significantly higher levels of CK-MB, BNP, Gal-3, and sST2. BNP exhibited a good independent predictive capacity for HF (AUC 0.956). However, CK-MB, sST2, and Gal-3 exhibited a modest diagnostic performance for HF, with an AUC of 0.709, 0.711, and 0.777, respectively. BNP was the most important variable, with a remarkably higher mean decrease accuracy and Gini. Furthermore, there was a general increase in predictive performance using the multi-marker model, and the sensitivity, specificity was 91.5% and 96.7%, respectively.

Conclusion:

The random forest algorithm provides a robust method to assess the accuracy and importance of predictor variables. The combination of CK-MB, BNP, Gal-3, and sST2 achieves improvement in prediction accuracy for HF.