LAUSR

Background and importance mRNA-based host response signatures have been reported to improve sepsis diagnostics. Meanwhile, prognostic markers for the rapid and accurate prediction of severity in patients with suspected acute infections and sepsis remain an unmet need. IMX-SEV-2 is a 29-host-mRNA classifier designed to predict disease severity in patients with acute infection or sepsis. Objective Validation of the host-mRNA infection severity classifier IMX-SEV-2. Design, settings and participants Prospective, observational, convenience cohort of emergency department (ED) patients with suspected acute infections. Outcome measures and analysis Whole blood RNA tubes were analyzed using independently trained and validated composite target genes (IMX-SEV-2). IMX-SEV-2-generated risk scores for severity were compared to the patient outcomes in-hospital mortality and 72-h multiorgan failure. Main results Of the 312 eligible patients, 22 (7.1%) died in hospital and 58 (18.6%) experienced multiorgan failure within 72 h of presentation. For predicting in-hospital mortality, IMX-SEV-2 had a significantly higher area under the receiver operating characteristic (AUROC) of 0.84 [95% confidence intervals (CI), 0.76–0.93] compared to 0.76 (0.64–0.87) for lactate, 0.68 (0.57–0.79) for quick Sequential Organ Failure Assessment (qSOFA) and 0.75 (0.65–0.85) for National Early Warning Score 2 (NEWS2), (P = 0.015, 0.001 and 0.013, respectively). For identifying and predicting 72-h multiorgan failure, the AUROC of IMX-SEV-2 was 0.76 (0.68–0.83), not significantly different from lactate (0.73, 0.65–0.81), qSOFA (0.77, 0.70–0.83) or NEWS2 (0.81, 0.75–0.86). Conclusion The IMX-SEV-2 classifier showed a superior prediction of in-hospital mortality compared to biomarkers and clinical scores among ED patients with suspected infections. No improvement for predicting multiorgan failure was found compared to established scores or biomarkers. Identifying patients with a high risk of mortality or multiorgan failure may improve patient outcomes, resource utilization and guide therapy decision-making.