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Article

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

Intensive care medicine - 1 Mar 2025

Antcliffe David B, Harte Elsa, Hussain Humma, Jiménez Beatriz, Browning Charlotte, Gordon Anthony C

Abstract excerpt

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...

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Identifiers

PMID: 40163132
DOI: 10.1007/s00134-025-07859-4
PMCID: PMC12018528

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