How Do Presenting Symptoms and Outcomes Differ by Race/Ethnicity Among Hospitalized Patients With Coronavirus Disease 2019 Infection? Experience in Massachusetts.

BACKGROUND: Population-based literature suggests severe acute respiratory syndrome coronavirus 2 infection may disproportionately affect racial/ethnic minorities; however, patient-level observations of hospitalization outcomes by race/ethnicity are limited. Our aim in this study was to characterize coronavirus disease 2019 (COVID-19)-associated morbidity and in-hospital mortality by race/ethnicity. METHODS: This was a retrospective analysis of 9 Massachusetts hospitals including all consecutive adult patients hospitalized with laboratory-confirmed COVID-19. Measured outcomes were assessed and compared by patient-reported race/ethnicity, classified as white, black, Latinx, Asian, or other. Student t test, Fischer exact test, and multivariable regression analyses were performed. RESULTS: A total of 379 patients (aged 62.9 ± 16.5 years; 55.7% men) with confirmed COVID-19 were included (49.9% white, 13.7% black, 29.8% Latinx, 3.7% Asian), of which 376 (99.2%) were insured (34.3% private, 41.2% public, 23.8% public with supplement). Latinx patients were younger, had fewer cardiopulmonary disorders, were more likely to be obese, more frequently reported fever and myalgia, and had lower D-dimer levels compared with white patients (P < .05). On multivariable analysis controlling for age, gender, obesity, cardiopulmonary comorbidities, hypertension, and diabetes, no significant differences in in-hospital mortality, intensive care unit admission, or mechanical ventilation by race/ethnicity were found. Diabetes was a significant predictor for mechanical ventilation (odds ratio [OR], 1.89; 95% confidence interval [CI], 1.11-3.23), while older age was a predictor of in-hospital mortality (OR, 4.18; 95% CI, 1.94-9.04). CONCLUSIONS: In this multicenter cohort of hospitalized COVID-19 patients in the largest health system in Massachusetts, there was no association between race/ethnicity and clinically relevant hospitalization outcomes, including in-hospital mortality, after controlling for key demographic/clinical characteristics. These findings serve to refute suggestions that certain races/ethnicities may be biologically predisposed to poorer COVID-19 outcomes.

Development and external validation of a prediction risk model for short-term mortality among hospitalized U.S. COVID-19 patients: A proposal for the COVID-AID risk tool.

BACKGROUND: The 2019 novel coronavirus disease (COVID-19) has created unprecedented medical challenges. There remains a need for validated risk prediction models to assess short-term mortality risk among hospitalized patients with COVID-19. The objective of this study was to develop and validate a 7-day and 14-day mortality risk prediction model for patients hospitalized with COVID-19. METHODS: We performed a multicenter retrospective cohort study with a separate multicenter cohort for external validation using two hospitals in New York, NY, and 9 hospitals in Massachusetts, respectively. A total of 664 patients in NY and 265 patients with COVID-19 in Massachusetts, hospitalized from March to April 2020. RESULTS: We developed a risk model consisting of patient age, hypoxia severity, mean arterial pressure and presence of kidney dysfunction at hospital presentation. Multivariable regression model was based on risk factors selected from univariable and Chi-squared automatic interaction detection analyses. Validation was by receiver operating characteristic curve (discrimination) and Hosmer-Lemeshow goodness of fit (GOF) test (calibration). In internal cross-validation, prediction of 7-day mortality had an AUC of 0.86 (95%CI 0.74-0.98; GOF p = 0.744); while 14-day had an AUC of 0.83 (95%CI 0.69-0.97; GOF p = 0.588). External validation was achieved using 265 patients from an outside cohort and confirmed 7- and 14-day mortality prediction performance with an AUC of 0.85 (95%CI 0.78-0.92; GOF p = 0.340) and 0.83 (95%CI 0.76-0.89; GOF p = 0.471) respectively, along with excellent calibration. Retrospective data collection, short follow-up time, and development in COVID-19 epicenter may limit model generalizability. CONCLUSIONS: The COVID-AID risk tool is a well-calibrated model that demonstrates accuracy in the prediction of both 7-day and 14-day mortality risk among patients hospitalized with COVID-19. This prediction score could assist with resource utilization, patient and caregiver education, and provide a risk stratification instrument for future research trials.