Development and validation of a multivariable risk prediction model for COVID-19 mortality in the Southern United States

Gupta, Aashish; Kachur, Sergey M.; Tafur, Jose D.; Patel, Harsh K.; Timme, Divina O.; Shariati, Farnoosh; Rogers, Kristen D.; Morin, Daniel P.; Lavie, Carl J.

Objective To evaluate clinical characteristics of COVID-19 admitted patients in Southern United States and development as well as validation of a mortality risk prediction model. Patients and methods Southern Louisiana was an early hot-spot in the pandemic, which provided a large collection of clinical data on inpatients with COVID-19. We designed a risk stratification model to assess admitted COVID patients’ mortality risk. Data from 1673 consecutive patients diagnosed with COVID-19 infection and hospitalized between 03/01/2020 to 04/30/2020 was used to create an 11-factor mortality risk model based on baseline comorbidity, organ injury, and laboratory results. The risk model was validated using a subsequent cohort of 2067 consecutive hospitalized patients admitted between 06/01/2020 to 12/31/2020. Results The resultant model has an area under the curve of 0.783 (95% confidence interval 0.76-0.81), with an optimal sensitivity of 0.74 and specificity of 0.69 for predicting mortality. Validation of this model in a subsequent cohort of 2067 consecutively hospitalized patients yielded comparable prognostic performance. Conclusion We have developed an easy-to-use, robust model for systematically evaluating patients presenting to acute care settings with COVID infection.