Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART-2 study.

ABSTRACT:

Aims

Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co-morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data-driven approaches with machine learning in a hospital-based registry.

Methods and results

A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART-2 (Chronic Heart Failure Analysis and Registry in the Tohoku District-2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non-cardiovascular death, all-cause death, and free from hospitalizati

SUBMITTER: Nakano K 

PROVIDER: S-EPMC10192279 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature