COVID-19 risk stratification algorithms based on sTREM-1 and IL-6 in emergency department

Abstract

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has led to surges of patients presenting to emergency departments (EDs) and potentially overwhelming health systems.ObjectiveWe sought to assess the predictive accuracy of host biomarkers at clinical presentation to the ED for adverse outcome.MethodsProspective observational study of PCR-confirmed COVID-19 patients in the ED of a Swiss hospital. Concentrations of inflammatory and endothelial dysfunction biomarkers were determined at clinical presentation. We evaluated the accuracy of clinical signs and these biomarkers in predicting 30-day intubation/mortality, and oxygen requirement by calculating the area under the receiver-operating characteristic curve and by classification and regression tree analysis.ResultsOf 76 included patients with COVID-19, 24 were outpatients or hospitalized without oxygen requirement, 35 hospitalized with oxygen requirement, and 17 intubated/died. We found that soluble triggering receptor expressed on myeloid cells had the best prognostic accuracy for 30-day intubation/mortality (area under the receiver-operating characteristic curve, 0.86; 95% CI, 0.77-0.95) and IL-6 measured at presentation to the ED had the best accuracy for 30-day oxygen requirement (area under the receiver-operating characteristic curve, 0.84; 95% CI, 0.74-0.94). An algorithm based on respiratory rate and sTREM-1 predicted 30-day intubation/mortality with 94% sensitivity and 0.1 negative likelihood ratio. An IL-6–based algorithm had 98% sensitivity and 0.04 negative likelihood ratio for 30-day oxygen requirement.ConclusionssTREM-1 and IL-6 concentrations in COVID-19 in the ED have good predictive accuracy for intubation/mortality and oxygen requirement. sTREM-1– and IL-6–based algorithms are highly sensitive to identify patients with adverse outcome and could serve as early triage tools.