Prediction of Multiple Infections After Severe Burn Trauma: ... : Annals of Surgery (original) (raw)

Original Articles

A Prospective Cohort Study

Yan, Shuangchun PhD*,†,‡; Tsurumi, Amy PhD*,†,‡; Que, Yok-Ai MD, PhD§; Ryan, Colleen M. MD*,‡; Bandyopadhaya, Arunava PhD*,†,‡; Morgan, Alexander A. PhD¶; Flaherty, Patrick J. PhD‖,**; Tompkins, Ronald G. MD, ScD*; Rahme, Laurence G. MS, PhD*,†,‡

*Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA

†Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA

‡Shriners Hospitals for Children Boston, Boston, MA

§Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland

¶Department of Biochemistry and Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, CA

‖Department of Biomedical Engineering, Worcester Polytechnic Institute, Worchester, MA

**Program in Bioinformatics and Computational Biology, Worcester Polytechnic Institute, Worchester, MA.

Reprints: Laurence G. Rahme, MS, PhD, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114. E-mail: [email protected].

Disclosure: The Inflammation and the Host Response to Injury “Glue Grant” program is supported by the National Institute of General Medical Sciences. This manuscript was prepared using a data set obtained from the Glue Grant program and does not necessarily reflect the opinions or views of the Inflammation and the Host Response to Injury Investigators or the NIGMS. The sources of support were the US Army Medical Research Acquisition Act of US Department of Defense and National Institute of General Medical Sciences. The authors declare no conflicts of interest [Trial Registration: clinicaltrials.gov Identifier NCT00257244].

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Objective:

To develop predictive models for early triage of burn patients based on hypersusceptibility to repeated infections.

Background:

Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking.

Methods:

Secondary analysis of 459 burn patients (≥16 years old) with 20% or more total body surface area burns recruited from 6 US burn centers. We compared blood transcriptomes with a 180-hour cutoff on the injury-to-transcriptome interval of 47 patients (≤1 infection episode) to those of 66 hypersusceptible patients [multiple (≥2) infection episodes (MIE)]. We used LASSO regression to select biomarkers and multivariate logistic regression to built models, accuracy of which were assessed by area under receiver operating characteristic curve (AUROC) and cross-validation.

Results:

Three predictive models were developed using covariates of (1) clinical characteristics; (2) expression profiles of 14 genomic probes; (3) combining (1) and (2). The genomic and clinical models were highly predictive of MIE status [AUROCGenomic = 0.946 (95% CI: 0.906–0.986); AUROCClinical = 0.864 (CI: 0.794–0.933); AUROCGenomic/AUROCClinical_P_ = 0.044]. Combined model has an increased AUROCCombined of 0.967 (CI: 0.940–0.993) compared with the individual models (AUROCCombined_/AUROCClinical_P = 0.0069). Hypersusceptible patients show early alterations in immune-related signaling pathways, epigenetic modulation, and chromatin remodeling.

Conclusions:

Early triage of burn patients more susceptible to infections can be made using clinical characteristics and/or genomic signatures. Genomic signature suggests new insights into the pathophysiology of hypersusceptibility to infection may lead to novel potential therapeutic or prophylactic targets.