Abstract
Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95%CI, 1.66-1.92]), male sex (OR, 1.57 [95%CI, 1.30-1.90]), higher BMI (OR, 1.03 [95%CI, 1.102-1.05]), higher heart rate (OR, 1.01 [95%CI, 1.00-1.01]), higher respiratory rate (OR, 1.05 [95%CI, 1.03-1.07]), lower oxygen saturation (OR, 0.94 [95%CI, 0.93-0.96]), and chronic kidney disease (OR, 1.53 [95%CI, 1.20-1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC=0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.
