Validation of diagnostic gene sets to identify critically ill patients with sepsis

David M Maslove; Tal Shapira; Kathrin Tyryshkin; Richard A Veldhoen; John C Marshall; John Muscedere

doi:10.1016/j.jcrc.2018.10.028

Validation of diagnostic gene sets to identify critically ill patients with sepsis

J Crit Care. 2019 Feb:49:92-98. doi: 10.1016/j.jcrc.2018.10.028. Epub 2018 Nov 1.

Authors

David M Maslove¹, Tal Shapira², Kathrin Tyryshkin³, Richard A Veldhoen⁴, John C Marshall⁵, John Muscedere⁶

Affiliations

¹ Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada; Department of Medicine, Queen's University, Kingston, ON, Canada; Kingston Health Sciences Center, Queen's University, Kingston, ON, Canada. Electronic address: david.maslove@queensu.ca.
² School of Computing, Queen's University, Kingston, ON, Canada.
³ Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.
⁴ Department of Medicine, Queen's University, Kingston, ON, Canada.
⁵ Critical Illness and Injury Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada.
⁶ Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada; Kingston Health Sciences Center, Queen's University, Kingston, ON, Canada.

PMID: 30408726
DOI: 10.1016/j.jcrc.2018.10.028

Abstract

Purpose: Gene expression diagnostics have been proposed to identify critically ill patients with sepsis. Three expression-based scores have been developed, but have not been compared in a prospective validation. We sought to validate these scores using an independent dataset and analysis.

Methods: We generated gene expression profiles from 61 critically ill patients. We validated the performance of 3 expression-based sepsis scores including 1) the Sepsis MetaScore (SMS); 2) the SeptiCyte™ Lab; and 3) the FAIM3:PLAC8 ratio. Sepsis was identified as the presence of definite, probable, or possible infection in the setting of organ dysfunction (SOFA score ≥ 2).

Results: For all 3 models, scores were significantly different between patients with and without sepsis. Discrimination was highest for the SMS (area under the receiver operating characteristics curve [AUROC 0.80 [95% CI 0.67-0.92]), with greater confidence in the presence of infection resulting in better model performance (max AUROC 0.93 [0.87-1.0]).

Conclusions: All three scores distinguished septic from non-septic ICU patients, with the SMS showing the best performance overall in our cohort. Our results suggest that models developed from the co-analysis of multiple cohorts are more generalizable. Further work is needed to identify expression-based biomarkers of response to specific therapies.

Keywords: Critical care; Gene expression; Precision medicine; Sepsis; Validation studies.

Publication types

Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Aged
Biomarkers / metabolism
Cohort Studies
Critical Illness*
Diagnosis, Differential
Female
Gene Expression Profiling / methods*
Humans
Male
Middle Aged
Organ Dysfunction Scores
Prospective Studies
ROC Curve
Sepsis / diagnosis*
Sepsis / genetics
Sepsis / metabolism
Systemic Inflammatory Response Syndrome / metabolism*

Substances

Biomarkers