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Background: There is limited data on outcomes in patients with coronavirus disease 2019 in rural United States (US). This study aimed to describe the demographics, and outcomes of hospitalized Covid-19 patients in rural Southwest Georgia. Methods: Using electronic medical records, we analyzed data from all hospitalized Covid-19 patients who either died or survived to discharge between 2 March 2020 and 6 May 2020. Results: Of the 522 patients, 92 died in hospital (17.6%). Median age was 63 years, 58% were females, and 87% African-Americans. Hypertension (79.7%), obesity (66.5%) and diabetes mellitus (42.3%) were the most common comorbidities. Males had higher overall mortality compared to females (23 v 13.8%). Immunosuppression [odds ratio (OR) 3.6; (confidence interval (CI): 1.52-8.47, p¼.003)], hypertension (OR 3.36; CI:1.3-8.6, p¼.01), age !65 years (OR 3.1; CI:1.7-5.6, p<.001) and morbid obesity (OR 2.29; CI:1.11-4.69, p¼.02), were independent predictors of in-hospital mortality. Female gender was an independent predictor of decreased in-hospital mortality. Mortality in intubated patients was 67%. Mortality was 8.9% in <50 years, compared to 20% in !50 years. Conclusions: Immunosuppression, hypertension, age ! 65 years and morbid obesity were independent predictors of mortality, whereas female gender was protective for mortality in hospitalized Covid-19 patients in rural Southwest Georgia. KEY MESSAGES1. Patients hospitalized with Covid-19 in rural US have higher comorbidity burden. 2. Immunosuppression, hypertension, age ! 65 years and morbid obesity are independent predictors of increased mortality. 3. Female gender is an independent predictor of reduced mortality.

Background The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. Methods This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower’s dissimilarity matrix between each patient’s clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. Results One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P &lt; .01, all comparisons). Conclusions Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19.

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