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1.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333770

ABSTRACT

BACKGROUND: Rates of severe illness and mortality from SARS-CoV-2 are greater for males, but the mechanisms for this difference are unclear. Understanding the differences in outcomes between males and females across the age spectrum will guide both public health and biomedical interventions. METHODS: Retrospective cohort analysis of SARS-CoV-2 testing and admission data in a health system. Patient-level data were assessed with descriptive statistics and logistic regression modeling was used to identify features associated with increased male risk of severe outcomes. RESULTS: In 213,175 SARS-CoV-2 tests, despite similar positivity rates (8.2%F vs 8.9%M), males were more frequently hospitalized (28%F vs 33%M). Of 2,626 hospitalized individuals, females had less severe presenting respiratory parameters and males had more fever. Comorbidity burden was similar, but with differences in specific conditions. Medications relevant for SARS-CoV-2 were used at similar frequency except tocilizumab (M>F). Males had higher inflammatory lab values. In a logistic regression model, male sex was associated with a higher risk of severe outcomes at 24 hours (odds ratio (OR) 3.01, 95%CI 1.75, 5.18) and at peak status (OR 2.58, 95%CI 1.78,3.74) among 18-49 year-olds. Block-wise addition of potential explanatory variables demonstrated that only the inflammatory labs substantially modified the OR associated with male sex across all ages. CONCLUSION: Higher levels of clinical inflammatory labs are the only features that are associated with the heightened risk of severe outcomes and death for males in COVID-19. Trial registration: Na. FUNDING: Hopkins inHealth;COVID-19 Administrative Supplement (HHS Region 3 Treatment Center), Office of the ASPR;NIH/NCI U54CA260492 (SK), NIH/NIA U54AG062333 (SK).

2.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-329400

ABSTRACT

Background: The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. Methods and Findings: In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics;we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients. Conclusions: This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

3.
Journal of the American College of Surgeons ; 233(5):S116-S116, 2021.
Article in English | Web of Science | ID: covidwho-1535674
4.
Journal of the American Society of Nephrology ; 32:107, 2021.
Article in English | EMBASE | ID: covidwho-1489889

ABSTRACT

Background: Acute kidney injury (AKI) is common in patients with COVID-19 and associated with poor outcomes. Urinary biomarkers have been associated with adverse kidney outcomes in other settings and may provide additional prognostic information in patients with COVID-19. Methods: We evaluated 19 urinary biomarkers of injury, inflammation, and repair in patients hospitalized with COVID-19 at 2 academic medical centers between April and June 2020. We associated biomarkers with a primary composite outcome of KDIGO stage 3 AKI, requirement for dialysis, or death within 60 days of admission. We also compared various kidney biomarker levels in the setting of COVID-19 versus other common AKI settings. Results: Out of 157 patients, 24 (15.3%) experienced the primary outcome. Twofold higher levels of neutrophil gelatinase-associated lipocalin (NGAL) (HR: 1.53;95% CI: 1.33-1.76), monocyte chemoattractant protein (MCP-1) (HR: 1.86;95% CI: 1.48-2.33), and kidney injury molecule-1 (KIM-1) (HR: 2.32;95% CI: 1.69-3.18) were associated with highest risk of the primary outcome. Higher epidermal growth factor (EGF) levels were associated with a lower risk of the primary outcome (HR 0.52;95% CI: 0.40-0.69). Individual biomarkers provided moderate discrimination and biomarker combinations improved discrimination for the primary outcome. Conclusions: Urinary biomarkers are associated with severe kidney complications in patients with COVID-19 and provide valuable information to monitor kidney disease recovery and progression.

5.
Pharmacoepidemiology and Drug Safety ; 30(SUPPL 1):402, 2021.
Article in English | EMBASE | ID: covidwho-1465776

ABSTRACT

Background: It is not clear how to best control for comorbidities when examining short-term mortality among individuals with COVID-19. The Charlson and Elixhauser Comorbidity Index were developed to predict 1-year and in-hospital mortality, respectively, and both indices can be operationalized using individual comorbidities or a weighted summary score. We compared the predictive accuracy for these comorbidity scores in predicting in-hospital death among adults hospitalized with COVID-19 from 5 hospitals comprising a health care system in the Mid-Atlantic United States. Methods: We used electronic health record data from adults hospitalized for COVID-19 from March 4-November 6, 2020. We ascertained comorbidities using all available lookback data from January 1, 2018 through COVID-19 hospital admission.We operationalized both comorbidity scores using individual comorbidities - 17 for Charlson and 29 for Elixhauser. We calculated weighted Charlson scores four ways, separately, using weights proposed by Deyo (1992), Schneeweiss (2003), Quan (2011) and Mehta (2016).We calculated the Elixhauser comorbidity score using weights proposed by van Walraven (2009) and Thompson (2015). We used logistic regression to compare the performance of different comorbidity scores in predicting in-hospital death. Nine models were constructed (1 baseline model that included age and sex, 1 for Charlson individual comorbidities, 4 for weighted Charlson scores, 1 for Elixhauser individual comorbidities and 2 for weighted Elixhauser scores). All models included age and sex as covariates.We evaluated the performance of each model using the c-statistic, and compared cstatistics using chi-square statistics, with a p-value < 0.05 considered significant model fit improvement. Secondarily, we compared model fit using Akaike Information Criteria (AIC), where lower values indicate better model fit.We used PROC LOGISTIC in SAS version 9.4. Results: Of 2,815 COVID-19 hospitalized patients, 12% (n=349) died in the hospital. Each comorbidity score performed significantly better (p < 0.001) than age and sex alone (c-statistic 0.775) at predicting COVID-19 related death. Overall, the ranking of the top 4 comorbidity scores were as follows: individual Elixhauser comorbidities (c-statistic 0.822) > Elixhauser-Thompson (c-statistic 0.803) > Elixhauser-van Walraven (c-statistic 0.796) = individual Charlson comorbidities (c-statistic 0.796).Weighted Elixhauser comorbidity scores (c-statistics ranging from 0.796 to 0.803) had significantly better performance than weighted Charlson comorbidity scores (c-statistics ranging from 0.786 to 0.790). Conclusions were similar when using AIC values to assess model fit. Conclusion: The individual comorbidities in the Elixhauser were the most accurate in predicting in-hospital death. If the weighted score needs to be used due to sample size limitations, we found that the Elixhauser-Thompson score was the most accurate in this training set. While statistically significant, the magnitude of predictive accuracy gained by adding covariates to the model for in-hospital mortality were small. Future research should investigate the utility of a customized COVID-19-specific comorbidity score in predicting mortality among adults hospitalized with COVID-19.

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