• Shuangchun Yan, Amy Tsurumi, Yok-Ai Que, Colleen M Ryan, Arunava Bandyopadhaya, Alexander A Morgan, Patrick J Flaherty, Ronald G Tompkins, and Laurence G Rahme.
• *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.
• Ann. Surg. 2015 Apr 1; 261 (4): 781-92.

ObjectiveTo develop predictive models for early triage of burn patients based on hypersusceptibility to repeated infections.BackgroundInfection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking.MethodsSecondary 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.ResultsThree 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.ConclusionsEarly 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.

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