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COVID-19 risk stratification algorithms based on sTREM-1 and IL-6 in emergency department
Mathias Van Singer1, Thomas Brahier1, Michelle Ngai2
1Infectious Diseases Service, University Hospital of Lausanne, Lausanne, Switzerland.
Insights
Biomarkers soluble triggering receptor expressed on myeloid cells (sTREM-1) and Interleukin-6 (IL-6) accurately predict severe COVID-19 outcomes. These markers can identify patients needing intensive care, improving emergency department triage during pandemics.
Area of Science:
- Infectious Diseases
- Biomarker Discovery
- Clinical Diagnostics
Background:
- The COVID-19 pandemic overwhelmed healthcare systems with emergency department (ED) patient surges.
- Accurate prediction of adverse outcomes in COVID-19 patients presenting to the ED is crucial for resource allocation.
- Host biomarkers at presentation may offer predictive value for patient trajectories.
Purpose of the Study:
- To evaluate the predictive accuracy of host biomarkers for adverse outcomes in COVID-19 patients.
- To assess the utility of biomarkers in predicting 30-day intubation, mortality, and oxygen requirement.
- To compare biomarker accuracy with clinical signs for patient stratification.
Main Methods:
- Prospective observational study of PCR-confirmed COVID-19 patients in a Swiss ED.
- Measurement of inflammatory and endothelial dysfunction biomarkers at clinical presentation.
- Analysis of predictive accuracy using ROC curves and classification/regression trees.
Main Results:
- Soluble triggering receptor expressed on myeloid cells (sTREM-1) showed high accuracy for predicting 30-day intubation/mortality (AUC 0.86).
- Interleukin-6 (IL-6) demonstrated strong accuracy for predicting 30-day oxygen requirement (AUC 0.84).
- Algorithms combining clinical signs (respiratory rate) and biomarkers achieved high sensitivity for adverse outcomes.
Conclusions:
- sTREM-1 and IL-6 concentrations in ED patients are accurate predictors of COVID-19 severity.
- Biomarker-based algorithms can effectively identify patients at high risk for adverse outcomes.
- These biomarkers and algorithms can serve as valuable early triage tools in emergency settings.
Background:
The coronavirus disease 2019 (COVID-19) pandemic has led to surges of patients presenting to emergency departments (EDs) and potentially overwhelming health systems.
Objective:
We sought to assess the predictive accuracy of host biomarkers at clinical presentation to the ED for adverse outcome.
Methods:
Prospective 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.
Results:
Of 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.
Conclusions:
sTREM-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.
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