Frontiers in Medicine (Apr 2022)
Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study
Abstract
ObjectivesAn accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information.DesignMulticenter retrospective observational cohort study.SettingFour health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles.PatientsCoronavirus Disease 2019-confirmed and hospitalized adult patients.Measurements and Main ResultsWe included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2 <93% into the predictive model. Besides age and SpO2, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79–0.88) and external validation at the other three health systems (range, 0.79–0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score.ConclusionsThe two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.
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