Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

ABSTRACT:

Aims

Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU.

Methods

We screened 6008 Mayo Clinic CICU patients with an admission diagnosis of HF from 2007 to 2018 and included those without missing values for common laboratory tests. Consensus k-means clustering was performed based on 10 common admission laboratory values (potassium, chloride, anion gap, blood urea nitrogen, haemoglobin, red blood

SUBMITTER: Jentzer JC 

PROVIDER: S-EPMC11631334 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature