Predicting severe COVID-19 outcomes for triage and resource allocation

Background While numerous studies have identified factors associated with severe COVID-19 outcomes, they have yet to quantify these characteristics. Therefore, our study’s purpose is to stratify these risk factors and use them to predict outcomes. Study Design This is a retrospective review of the CDC COVID-19 Surveillance Data. Logistic regression models calculated risk estimates for independent variables, and random forest models predicted the chance of severe outcomes. Results Our sample of 3,798,261 patients with COVID-19 consisted mainly of females (51.9%), 10-to 69-year-olds, and White/Non-Hispanics (34.9%). Most were not healthcare workers (90.6%) and did not have preexisting medical conditions (47.1%). Age had an increased risk of severe outcomes that grew every decade of life. White patients had a decreased occurrence of severe outcomes than Non-Whites, except for Pacific Islanders with comparable mortality. The variable selection algorithm detected that three outcomes were more accurate without healthcare worker classification: mechanical ventilation/intubation, pneumonia, and ARDS Acute respiratory distress. However, providers had a decreased risk of severe outcomes overall. Also, patients with preexisting conditions demonstrated an increased risk in all outcomes. Compared to the logistic regressions, the predictive models had a higher performance (AUC>0.8). The death model had the best metrics, followed by hospitalization and ventilation. We amassed these predictive models into the Severe COVID-19 Calculator web application that estimates the probability of severe outcomes. Conclusions Several patient social and medical demographics recorded by the CDC significantly affect severe COVID-19 outcomes suggesting a multifactorial influence. To account for these variables, a generated Severe Covid-19 Calculator can accurately predict the chance of severe outcomes in citizens that may contract or have COVID-19.