Prediction of In-hospital Mortality among Adults with COVID-19 Infection

IMPORTANCE: Accurate and rapid prediction of the probability of dying from COVID-19 infection might help triage patients for hospitalization, intensive care, or limited treatment. OBJECTIVE: To develop a simple tool to estimate the probability of dying from acute COVID-19 illness. DESIGN, SETTING AND PARTICIPANTS: This cohort study included 13,190 adult patients admitted to one of the 11 hospitals in the New York City Health + Hospitals system for COVID-19 infection between March 1 and June 30, 2020. EXPOSURES: Demographic characteristics, vital signs, and laboratory tests readily available at the time of hospital admission. MAIN OUTCOME: Death from any cause during hospitalization. RESULTS: Patients had a mean age (interquartile range) of 58 (45-72) years; 5421 (41%) were women, 5258 Latinx (40%), 3805 Black (29%), 1168 White (9%), and 2959 Other (22%). During hospitalization, 2,875 (22%) died. Using separate test and validation samples, machine learning (Gradient Boosted Decision Trees) identified eight variables, oxygen saturation, respiratory rate, systolic and diastolic blood pressures, pulse rate, blood urea nitrogen level, age and creatinine, that predicted mortality, with an area under the ROC curve (AUC) of 94%. A score based on these variables classified 5,677 (46%) as low risk (a score of 0) who had 0.8% (95% confidence interval, 0.5, 1.0%) risk of dying, and 674 (5.4%) as high-risk (score ≥ 12 points) who had a 97.6% (96.5, 98.8%) risk of dying; the remainder had intermediate risks. A risk calculator is available online at https://danielevanslab.shinyapps.io/Covid_mortality/). CONCLUSIONS AND RELEVANCE: In a diverse population of hospitalized patients with COVID-19 infection, a clinical prediction model using a few readily available assessments may precisely estimate in-hospital mortality and can rapidly assist decisions to prioritize admissions and intensive care.