Predicting mortality and re-hospitalization for heart failure: a machine-learning and cluster analysis on frailty and comorbidity.

ABSTRACT:

Background

Machine-learning techniques have been recently utilized to predict the probability of unfavorable outcomes among elderly patients suffering from heart failure (HF); yet none has integrated an assessment for frailty and comorbidity. This research seeks to determine which machine-learning-based phenogroups that incorporate frailty and comorbidity are most strongly correlated with death or readmission at hospital for HF within six months following discharge from hospital.

Methods

In this single-center, prospective study of a tertiary care center, we included all patients aged 65 and older discharged for acute decompensated heart failure. Random forest analysis and a Cox multivariable regression were performed to determine the predictors of the composite endpoint. By

SUBMITTER: Okoye C 

PROVIDER: S-EPMC10721693 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature