Development and Validation of a Multivariable Risk Prediction Model for COVID-19 Mortality in the Southern United States

Objective: To evaluate clinical characteristics of patients admitted to the hospital with coronavirus disease 2019 (COVID-19) in Southern United States and development as well as validation of a mortality risk prediction model. Patients and Methods: Southern Louisiana was an early hotspot during the pandemic, which provided a large collection of clinical data on inpatients with COVID-19. We designed a risk stratification model to assess the mortality risk for patients admitted to the hospital with COVID-19. Data from 1673 consecutive patients diagnosed with COVID-19 infection and hospitalized between March 1, 2020, and April 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 June 1, 2020, and December 31, 2020. Results: The resultant model has an area under the curve of 0.783 (95% CI, 0.76 to 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-19 infection. (c) 2021 Mayo Foundation for Medical Education and Research center dot Mayo Clin Proc. 2021;96(12):3030-3041

Automated summary

This study evaluated the clinical characteristics of COVID-19 patients admitted to hospitals in the Southern United States and developed a mortality risk prediction model. The 11-factor risk model showed good performance in predicting mortality risk, with an area under the curve of 0.783. Validation in a subsequent cohort of hospitalized patients confirmed the reliability and accuracy of the model in predicting mortality risk.