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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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ImportanceType 2 diabetes (T2DM) and obesity are significant risk factors for mortality in Covid19. Metformin has sex specific immunomodulatory effects which may elucidate treatment mechanisms in COVID-19. Objective: We sought to identify whether metformin reduced mortality from Covid19 and if sex specific interactions exist. DesignRetrospective review of de-identified claims from UnitedHealth Groups Clinical Discovery Database. Unadjusted and multivariate models were conducted to assess risk of mortality based on metformin and tumor necrosis factor alpha (TNF) inhibitors as home medications in individuals with T2DM and obesity, controlling for comorbidities, medications, demographics, and state. Heterogeneity of effect was assessed by sex. SettingThe database includes all 50 states in the United States. Participants: Persons with at least 6 months of continuous coverage from UnitedHealth Group in 2019 who were hospitalized with Covid-19. Persons in the metformin group had > 90 days of metformin claims in the 12 months before hospitalization. Results6,256 persons were included; 52.8% female; mean age 75 years. Metformin was associated with decreased mortality in women by logistic regression, OR 0.792 (0.640, 0.979); mixed effects OR 0.780 (0.631, 0.965); Cox proportional-hazards: HR 0.785 (0.650, 0.951); and propensity matching, OR of 0.759 (0.601, 0.960). There was no significant reduction in mortality among men. TNF inhibitors were associated with decreased mortality, by propensity matching in a limited model, OR 0.19 (0.0378, 0.983). ConclusionsMetformin was significantly associated with reduced mortality in women with obesity or T2DM in observational analyses of claims data from individuals hospitalized with Covid-19. This sex-specific finding is consistent with metformins reduction of TNF in females over males, and suggests that metformin conveys protection in Covid-19 through TNF effects. Prospective studies are needed to understand mechanism and causality. Key PointsO_ST_ABSQuestionC_ST_ABSMetformin has many anti-inflammatory effects, including sex-specific effects on TNF. Is metformin protective from the Sars-CoV-2 virus, and does the effect differ by sex? FindingsMetformin was associated with reduced mortality in women who were hospitalized with Covid-19, but not in men who were hospitalized with Covid-19. MeaningThe sex-dependent survival by metformin use points towards TNF reduction as a key mechanism for protection from Covid-19.