Clustering out-of-hospital cardiac arrest patients with non-shockable rhythm by machine learning latent class analysis.

ABSTRACT:

Aim

We aimed to identify subphenotypes among patients with out-of-hospital cardiac arrest (OHCA) with initial non-shockable rhythm by applying machine learning latent class analysis and examining the associations between subphenotypes and neurological outcomes.

Methods

This study was a retrospective analysis within a multi-institutional prospective observational cohort study of OHCA patients in Osaka, Japan (the CRITICAL study). The data of adult OHCA patients with medical causes and initial non-shockable rhythm presenting with OHCA between 2012 and 2016 were included in machine learning latent class analysis models, which identified subphenotypes, and patients who presented in 2017 were included in a dataset validating the subphenotypes. We investigated associations between

SUBMITTER: Okada Y 

PROVIDER: S-EPMC9136939 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature