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.
Highlights
And effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes
As of April 24, 2020, a total of 46,945 patients had an encounter at a Mount Sinai facility who have either been tested for COVID-19 or who are under investigation for COVID-19
real time-polymerase chain reaction (RT-PCR) confirmation for SARS-CoV-2 was available for 8770 of these patients which comprise the final sample for our analyses
Summary
And effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. 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. Identifying susceptibility to COVID-19 related mortality based on measures immediately available at the first clinical evaluation may assist medical staff in providing timely and effective care for each patient. We explicitly test how demographic, clinical, and co-morbid disease factors relate to COVID-19 mortality in 8770 patients with laboratory-confirmed SARS-CoV-2 infection. To provide timely information that would support fast clinical decision-making, we focused on factors that can be assessed immediately at the first clinical evaluation and did not require laboratory processing or extensive medical chart review
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