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Background Increased mortality rates among coronavirus disease 2019 (COVID-19) positive patients admitted to intensive care units (ICUs) highlight a compelling need to establish predictive criteria for ICU admissions. The aim of our study was to identify criteria for recognizing patients with COVID-19 at elevated risk for ICU admission. Methods We identified patients who tested positive for COVID-19 and were hospitalized between March and May 2020. Patients' data were manually abstracted through review of electronic medical records. An ICU admission prediction model was derived from a random sample of half the patients using multivariable logistic regression. The model was validated with the remaining half of the patients using c-statistic. Results We identified 1,094 patients; 204 (18.6%) were admitted to the ICU. Correlates of ICU admission were age, body mass index (BMI), quick Sequential Organ Failure Assessment (qSOFA) score, arterial oxygen saturation to fraction of inspired oxygen ratio, platelet count, and white blood cell count. The c-statistic in the derivation subset (0.798, 95% confidence interval [CI]: 0.748, 0.848) and the validation subset (0.764, 95% CI: 0.706, 0.822) showed excellent comparability. At 22% predicted probability for ICU admission, the derivation subset estimated sensitivity was 0.721, (95% CI: 0.637, 0.804) and specificity was 0.763, (95% CI: 0.722, 0.804). Our pilot predictive model identified the combination of age, BMI, qSOFA score, and oxygenation status as significant predictors for ICU admission. Conclusion ICU admission among patients with COVID-19 can be predicted by age, BMI, level of hypoxia, and severity of illness.
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[This corrects the article DOI: 10.1016/j.heliyon.2021.e08566.].
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BACKGROUND & OBJECTIVES: Race plays an important role in healthcare disparities, often resulting in worse health outcomes. It is unclear if other patient factors and race interactions may influence mortality in patients with COVID-19. We aimed to evaluate how multiple determinants of all-cause in-hospital mortality from COVID-19 were linked to race. METHODS: A retrospective observational study was conducted at two hospitals in metropolitan Detroit. We identified patients aged ≥18 years-old who had tested positive for COVID-19 and were admitted between March 9 through May 16, 2020. Multivariable logistic regression was performed assessing predictors of all-cause in-hospital mortality in COVID-19. RESULTS: We identified 1064 unique patients; 74% were African Americans (AA). The all-cause in-hospital mortality was 21.7%, with the majority of deaths seen in AA (65.4%, P = 0.002) and patients 80 years or older (52%, P < 0.0001). AA women had lower all-cause mortality than AA men, white women, and white men based on race-gender interactions. In multivariable logistic regression analysis, older age (>80-year-old), dementia, and chronic kidney disease were associated with worse all-cause in-hospital mortality. Adjusted for race and body mass index (BMI), the main odds ratios (OR) and 95% confidence intervals (CI) are: Age 80 and older vs < 60 in females: OR = 7.4, 95% CI: 2.9, 18.7; in males OR = 7.3, 95% CI: 3.3, 16.2; Chronic Kidney Disease (CKD): OR = 1.7, 95% CI: 1.2, 2.6; Dementia: OR = 2.2, 95% CI: 1.5, 3.3. CONCLUSION: Gender significantly modified the association of race and COVID-19 mortality. African American females had the lowest all-cause in-hospital mortality risk compared to other gender-race groups.
