Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome.

ABSTRACT:

Purpose

Machine learning has shown promise to detect useful subgroups of patients with sepsis from gene expression and protein data. This approach has rarely been deployed in metabolomic datasets. Metabolomic data are of interest as they capture effects from the genome, proteome, and environmental. We aimed to discover metabolic sub-phenotypes of septic shock, examine their temporal stability and association with clinical outcome.

Methods

Analysis was performed in two double-blind randomized trials in septic shock (LeoPARDS (1402 samples from 470 patients) and VANISH (493 samples from 173 patients)). Patients were included soon after the onset of shock and had serum collected at up to four time points. Metabolic clusters were identified from 474 metabolites using k-means clu

SUBMITTER: Antcliffe DB 

PROVIDER: S-EPMC12018528 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature