Abstract
Background
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada.
Methods
Data from Ontario’s provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression–based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data.
Results
Of 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model–based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables.
Conclusions
We used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials.
External validation of the RISC, RISC-Malawi, and PERCH clinical prediction rules to identify risk of death in children hospitalized with pneumonia
