Logo image
An Integrated Clinico-Metabolomic Model Improves Prediction of Death in Sepsis
Journal article   Open access   Peer reviewed

An Integrated Clinico-Metabolomic Model Improves Prediction of Death in Sepsis

Raymond J. g Langley, Ephraim L. Tsalik, Jennifer C. van Velkinburgh, Seth W. Glickman, Brandon J. Rice, Chunping Wang, Bo Chen, Lawrence Carin, Arturo Suarez, Robert P. Mohney, …
Science translational medicine, v 5(195)
24 Jul 2013
PMID: 23884467
url
https://europepmc.org/articles/pmc3924586View
Accepted (AM)Open Access (License Unspecified) Open

Abstract

Life Sciences & Biomedicine Medicine, Research & Experimental Research & Experimental Medicine Science & Technology Cell Biology
Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and beta-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.

Metrics

16 Record Views
397 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Industry collaboration
Domestic collaboration
Web of Science research areas
Cell Biology
Medicine, Research & Experimental
Logo image