There is currently limited clinical ability to identify COVID-19 patients at risk for severe outcomes. To unbiasedly identify metrics associated with severe outcomes in COVID-19 patients, we conducted a retrospective study of 835 COVID-19 positive patients at a single academic medical center between March 10, 2020 and October 13, 2020. As of December 1, 2020, 656 (79%) patients required hospitalization and 149 (18%) died. Unbiased comparisons of all clinical characteristics and mortality revealed that abnormal pH (OR 8.54, 95% CI 5.34-13.6), abnormal creatinine (OR 6.94, 95% CI 4.22-11.4), and abnormal PTT (OR 4.78, 95% CI 3.11-7.33) were most significantly associated with mortality. Correlation with ordinal severity scores confirmed these associations, in addition to associations between respiratory rate (Spearman's rho = -0.56), absolute neutrophil count (Spearman's rho = -0.5), and C-reactive protein (Spearman's rho = 0.59) with disease severity. Unsupervised principal component analysis and machine learning model classification of patient demographics, laboratory results, medications, comorbidities, signs and symptoms, and vitals are capable of separating patients on the basis of COVID-19 mortality (AUC 0.82). This retrospective analysis identifies laboratory and clinical metrics most relevant to predict COVID-19 severity.
Keywords: COVID-19; Laboratory results; Machine learning; Prediction.
© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.