Background: The global outbreak of COVID-19 has resulted in over 378 million infections and worldwide casualties have surpassed 5.6 million. Identifying individuals at highest risk of COVID-19 death may help risk evaluation and health service provision. Personalized risk prediction that uses a broad range of comorbidities requires a cohort size larger than that reported in prior studies.
Methods: Medicare claims data was used to identify patients age 65 years or older with diagnosis of COVID-19 between April 1, 2020 and August 31, 2020. Demographic characteristics, chronic medical conditions, and other patient risk factors that existed before the advent of COVID-19 were identified. A random forest model was used to empirically explore factors associated with COVID-19 death. The independent impact of factors identified were quantified using multivariate logistic regression with random effects.
Results: We identified 534,023 COVID-19 patients of whom 38,066 had an inpatient death. Demographic characteristics associated with COVID-19 death included advanced age (85 years or older: aOR: 2.07; 95% CI, 1.99-2.16), male sex (aOR, 1.88; 95% CI, 1.82-1.94), and non-white race (Hispanic: aOR, 1.74; 95% CI, 1.66-1.83). Leading comorbidities associated with COVID-19 mortality included sickle cell disease (aOR, 1.73; 95% CI, 1.21-2.47), chronic kidney disease (aOR, 1.32; 95% CI, 1.29-1.36), leukemias and lymphomas (aOR, 1.22; 95% CI, 1.14-1.30), heart failure(aOR, 1.19; 95% CI, 1.16-1.22), and diabetes (aOR, 1.18; 95% CI, 1.15-1.22).
Conclusions: We created a personalized risk prediction calculator to identify candidates for early vaccine and therapeutics allocation (www. predictcovidrisk.com). These findings may be used to protect those at greatest risk of death from COVID-19.
COVID-19, Machine learning, Risk prediction