Background
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 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; 1,796 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 (AUC for all rules > 0.92) and calibration (P > 0.50 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 four 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.