ABSTRACT
BACKGROUND: Several studies have explored hospitalization risk factors with the novel coronavirus disease 2019 (COVID-19) infection. Our goal was to identify clinical characteristics outside of laboratory or radiologic data associated with intubation or death within 7 days of admission. METHODS: The first 436 patients admitted to the University of Colorado Hospital (Denver metropolitan area) with confirmed COVID-19 were included. Demographics, comorbidities, and select medications were collected by chart abstraction. Missing height for calculating body mass index (BMI) was imputed using the median height for patients' sex and race/ethnicity. Adjusted odds ratios (aOR) were estimated using multivariable logistic regression and a minimax concave penalty (MCP) regularized logistic regression explored prediction. RESULTS: Participants had a mean [standard deviation (SD)] age 55 (17), BMI 30.9 (8.2), 55% were male and 80% were ethnic/racial minorities. Increasing age [aOR: 1.24 (1.07, 1.45) per 10 years], higher BMI (aOR 1.03 (1.00, 1.06), and poorly controlled diabetes [hemoglobin A1C (HbA1c) ⩾ 8] (aOR 2.26 (1.24, 4.12) were significantly (p < 0.05) associated with greater odds of intubation or death. Female sex [aOR: 0.63, 95% CI (0.40, 0.98); p value = 0.043] was associated with lesser odds of intubation or death. The odds of death and/or intubation increased 19% for every 1 unit increase in HbA1c value [OR: 1.19 (1.01, 1.43); p = 0.04]. Our final MCP model included indicators of A1C ⩾ 8, age > 65, sex, and minority status, but predicted intubation/death only slightly better than random chance [area under the receiver operating characteristic curve (AUC) = 0.61 (0.56, 0.67)]. CONCLUSION: In a hospitalized patient cohort with COVID-19, worsening control of diabetes as evidenced by higher HbA1c was associated with increased risk of intubation or death within 7 days of admission. These results complement and help clarify previous associations found between diabetes and acute disease in COVID-19. Importantly, our analysis is missing some known predictors of severity in COVID-19. Our predictive model had limited success, suggesting unmeasured factors contribute to disease severity differences.