BACKGROUND Necrotizing soft-tissue infections (NSTIs) are rapidly progressing infections frequently complicated by septic shock and associated with high mortality. Early diagnosis is critical for patient outcome, but challenging due to vague initial symptoms. Here, we identified predictive biomarkers for NSTI clinical phenotypes and outcomes using a prospective multicenter NSTI patient cohort.METHODS Luminex multiplex assays were used to assess 36 soluble factors in plasma from NSTI patients with positive microbiological cultures (n = 251 and n = 60 in the discovery and validation cohorts, respectively). Control groups for comparative analyses included surgical controls (n = 20), non-NSTI controls (i.e., suspected NSTI with no necrosis detected upon exploratory surgery, n = 20), and sepsis patients (n = 24).RESULTS Thrombomodulin was identified as a unique biomarker for detection of NSTI (AUC, 0.95). A distinct profile discriminating mono- (type II) versus polymicrobial (type I) NSTI types was identified based on differential expression of IL-2, IL-10, IL-22, CXCL10, Fas-ligand, and MMP9 (AUC >0.7). While each NSTI type displayed a distinct array of biomarkers predicting septic shock, granulocyte CSF (G-CSF), S100A8, and IL-6 were shared by both types (AUC >0.78). Finally, differential connectivity analysis revealed distinctive networks associated with specific clinical phenotypes.CONCLUSIONS This study identifies predictive biomarkers for NSTI clinical phenotypes of potential value for diagnostic, prognostic, and therapeutic approaches in NSTIs.TRIAL REGISTRATION ClinicalTrials.gov NCT01790698.FUNDING Center for Innovative Medicine (CIMED); Region Stockholm; Swedish Research Council; European Union; Vinnova; Innovation Fund Denmark; Research Council of Norway; Netherlands Organisation for Health Research and Development; DLR Federal Ministry of Education and Research; and Swedish Children’s Cancer Foundation.
Laura M. Palma Medina, Eivind Rath, Sanjeevan Jahagirdar, Trond Bruun, Martin B. Madsen, Kristoffer Strålin, Christian Unge, Marco Bo Hansen, Per Arnell, Michael Nekludov, Ole Hyldegaard, Magda Lourda, Vitor A.P. Martins dos Santos, Edoardo Saccenti, Steinar Skrede, Mattias Svensson, Anna Norrby-Teglund
Flow chart of the study pipeline.