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Development and Validation of a Multivariable Risk Prediction Model for COVID-19 Mortality in the Southern United
Aashish Gupta1, Sergey M Kachur2, Jose D Tafur1
1John Ochsner Heart and Vascular Institute, Ochsner Clinic Foundation, New Orleans, LA; University of Queensland School of Medicine, Brisbane, Australia.
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.
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Assumptions of Survival Analysis

