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Background: There is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes. Objective: Identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. Design, Settings, and Participants: Retrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020. Methods: Ensemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes. Main outcomes and measures: Phenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality. Results: The database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III. When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (p<0.001), renal (p<0.001), and cardiac (p<0.001) comorbidities were most commonly characterized by phenotype I. The adjusted odds of respiratory (p<0.001), renal (p<0.001), and metabolic (p<0.001) complications were highest for patients with phenotype I, followed by phenotype II. Patients with phenotype I had a far greater odds of hepatic (p<0.001) and hematological (p=0.02) complications than the other two phenotypes. Phenotypes I and II were associated with 7.30-fold (HR: 7.30, 95% CI: (3.11-17.17), p<0.001) and 2.57-fold (HR: 2.57, 95% CI: (1.10-6.00), p=0.03) increases in the hazard of death, respectively, when compared to phenotype III. Conclusion: In this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.
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BackgroundCovid-19 disease causes significant morbidity and mortality through increase inflammation and thrombosis. Non-alcoholic fatty liver disease and non-alcoholic steatohepatitis are states of chronic inflammation and indicate advanced metabolic disease. We sought to understand the risk of hospitalization for Covid-19 associated with NAFLD/NASH. MethodsRetrospective analysis of electronic medical record data of 6,700 adults with a positive SARS-CoV-2 PCR from March 1, 2020 to Aug 25, 2020. Logistic regression and competing risk were used to assess odds of being hospitalized. Additional adjustment was added to assess risk of hospitalization among patients with a prescription for metformin use within the 3 months prior to the SARS-CoV-2 PCR result, history of home glucagon-like-peptide 1 receptor agonist (GLP-1 RA) use, and history of metabolic and bariatric surgery (MBS). Interactions were assessed by gender and race. ResultsA history of NAFLD/NASH was associated with increased odds of admission for Covid-19: logistic regression OR 2.04 (1.55, 2.96, p<0.01), competing risks OR 1.43 (1.09-1.88, p<0.01); and each additional year of having NAFLD/NASH was associated with a significant increased risk of being hospitalized for Covid-19, OR 1.86 (1.43-2.42, p<0.01). After controlling for NAFLD/NASH, persons with obesity had decreased odds of hospitalization for Covid-19, OR 0.41 (0.34-0.49, p<0.01). NAFLD/NASH increased risk of hospitalization in men and women, and in all racial/ethnic subgroups. Mediation treatments for metabolic syndrome were associated with non-significant reduced risk of admission: OR 0.42 (0.18-1.01, p=0.05) for home metformin use and OR 0.40 (0.14-1.17, p=0.10) for home GLP-1RA use. MBS was associated with a significant decreased risk of admission: OR 0.22 (0.05-0.98, p<0.05). ConclusionsNAFLD/NASH is a significant risk factor for hospitalization for Covid-19, and appears to account for risk attributed to obesity. Treatments for metabolic disease mitigated risks from NAFLD/NASH. More research is needed to confirm risk associated with visceral adiposity, and patients should be screened for and informed of treatments for metabolic syndrome. Key QuestionsO_ST_ABSQuestionC_ST_ABSDoes NAFLD/NASH independently increase risk for poor outcomes from Covid-19? FindingsIn this observational study, a history of NAFLD/NASH was associated with a significantly increased odds of hospitalization. Metabolic surgery was protective against admission in persons with NAFLD/NASH and Covid-19. Metformin and glucagon like peptide 1 receptor agonists were associated with non-significant protecting against admission. MeaningTreatment for metabolic syndrome greatly reduce the elevated risk of hospitalization for Covid-19 among persons with NAFLD/NASH.