Person:

Ryan, Colleen

Severe burn injury renders patients susceptible to multiple infection episodes; however, identifying specific patient groups at high risk remains challenging. Burn-induced inflammatory response dramatically modifies the levels of various cytokines. Whether these changes could predict susceptibility to infections remains unknown. The aim of this study was to determine the early changes in the pro- to anti-inflammatory cytokine ratio and investigate its ability to predict susceptibility to repeated infections after severe burn trauma. The patient population consisted of 34 adult patients having early (≤48 h since injury) blood draws following severe (≥20% total burn surface area (TBSA)) burn injury and suffering from a first infection episode at least 1 day after blood collection. Plasma TNF-α and IL-10 levels were measured to explore the association between the TNF-α/IL-10 ratio, hypersusceptibility to infections, burn size (TBSA), and common severity scores (Acute Physiology and Chronic Health Evaluation II (APACHEII), Baux, modified Baux (R-Baux), Ryan Score, and Abbreviated Burn Severity Index (ABSI)). TNF-α/IL10 plasma ratio measured shortly after burn trauma was inversely correlated with burn size and the injury severity scores investigated, and was predictive of repeated infections (≥3 infection episodes) outcome (AUROC [95%CI] of 0.80 [0.63–0.93]). Early measures of circulating TNF-α/IL10 ratio may be a previously unidentified biomarker associated with burn injury severity and predictive of the risk of hypersusceptibility to repeated infections.

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.