Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach.

ABSTRACT:

Background

Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach.

Methods

The current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguous

SUBMITTER: Wang L 

PROVIDER: S-EPMC10466857 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature