Performance of point‐of‐care severity scores to predict prognosis in patients admitted through the emergency department with COVID‐19

Abstract

AbstractBackgroundIdentifying COVID‐19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID‐19 patients but have not been evaluated in a large cohort.ObjectiveTo determine whether well‐known risk scores can predict poor outcomes among hospitalized COVID‐19 patients.Designs, Settings, and ParticipantsA retrospective cohort study of adults presenting with COVID‐19 to 156 Hospital Corporation of America (HCA) Healthcare EDs, March 2, 2020, to February 11, 2021.InterventionQuick Sequential Organ Failure Assessment (qSOFA), Shock Index, National Early Warning System‐2 (NEWS2), and quick COVID‐19 Severity Index (qCSI) at presentation.Main Outcome and MeasuresThe primary outcome was in‐hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and vasopressors receipt. Patients scored positive with qSOFA ≥ 2, Shock Index > 0.7, NEWS2 ≥ 5, and qCSI ≥ 4. Test characteristics and area under the receiver operating characteristics curves (AUROCs) were calculated.ResultsWe identified 90,376 patients with community‐acquired COVID‐19 (mean age 64.3 years, 46.8% female). 17.2% of patients died in‐hospital, 28.6% went to the ICU, 13.7% received mechanical ventilation, and 13.6% received vasopressors. There were 3.8% qSOFA‐positive, 45.1% Shock Index‐positive, 49.8% NEWS2‐positive, and 37.6% qCSI‐positive at ED‐triage. NEWS2 exhibited the highest AUROC for in‐hospital mortality (0.593, confidence interval [CI]: 0.588–0.597), ICU admission (0.602, CI: 0.599–0.606), mechanical ventilation (0.614, CI: 0.610–0.619), and vasopressor receipt (0.600, CI: 0.595–0.604).ConclusionsSepsis severity scores at presentation have low discriminative power to predict outcomes in COVID‐19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID‐19 patients to develop more effective risk‐based triage.