The chronic heart failure evolutions: Different fates and routes.

ABSTRACT:

Aims

Individual prognostic assessment and disease evolution pathways are undefined in chronic heart failure (HF). The application of unsupervised learning methodologies could help to identify patient phenotypes and the progression in each phenotype as well as to assess adverse event risk.

Methods and results

From a bulk of 7948 HF patients included in the MECKI registry, we selected patients with a minimum 2-year follow-up. We implemented a topological data analysis (TDA), based on 43 variables derived from clinical, biochemical, cardiac ultrasound, and exercise evaluations, to identify several patients' clusters. Thereafter, we used the trajectory analysis to describe the evolution of HF states, which is able to identify bifurcation points, characterized by different follow

SUBMITTER: Agostoni P 

PROVIDER: S-EPMC11769638 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature