ABSTRACT
Background. Insufficient information on SARS-CoV-2 testing results exists in clinical practice from the United States. Methods. We conducted an observational retrospective cohort study using Optum(R) de-identified COVID-19 electronic health records from the United States to characterize patients who received a SARS-CoV-2 viral or antibody test between February 20, 2020 and July 10, 2020. We assessed temporal trends in testing and positivity by demographic and clinical characteristics; evaluated concordance between viral and antibody tests; and identified factors associated with positivity via multivariable logistic regression. Results. Our study population included 891,754 patients. Overall positivity rate for SARS-CoV-2 was 9% and 12% for viral and antibody tests, respectively. Positivity rate was inversely associated with the number of individuals tested and decreased over time across regions and race/ethnicities. Among patients who received a viral test followed by an antibody test, concordance ranged from 90%-93% depending on the duration between the two tests which is notable given uncertainties related to specific viral and antibody test characteristics. The following factors increased the odds of viral and antibody positivity in multivariable models: male, Hispanic or non-Hispanic Black and Asian, uninsured or Medicaid insurance, Northeast residence, dementia, diabetes, and obesity. Charlson Comorbidity Index was negatively associated with test positivity. We identified symptoms that were positively associated with test positivity, as well as, commonly co-occurring symptoms / conditions. Pediatric patients had reduced odds of a positive viral test, but conversely had increased odds of a positive antibody test. Conclusions. This study identified sociodemographic and clinical factors associated with SARS-CoV-2 testing and positivity within routine clinical practice from the United States.
Subject(s)
COVID-19 , Obesity , Dementia , Diabetes MellitusABSTRACT
Objectives To develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19. Design Retrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores, and calibration plots in the test set. Setting Optum de-identified COVID-19 Electronic Health Record dataset. Participants 17,086 patients hospitalized with COVID-19 between February 20, 2020 and June 5, 2020. Main outcome measure All-cause mortality during hospital stay. Results The full model that included information on demographics, comorbidities, laboratory results and vital signs had good discrimination (C-index = 0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were generally similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index = 0.79) was only slightly better than a model that only included age (C-index = 0.76). Across the study period, predicted mortality was 1.2% for 18-year olds, 8.4% for 55-year olds, and 28.6% for 85-year olds. Predicted mortality across all ages declined over the study period from 21.7% by March to 13.3% by May. Conclusion Age was the most important predictor of all-cause mortality although vital signs and laboratory results added considerable prognostic information with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase, and white blood cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.
Subject(s)
COVID-19ABSTRACT
BACKGROUND: Despite the significant morbidity and mortality caused by the 2019 novel coronavirus disease (COVID-19), our understanding of basic disease epidemiology remains limited. This study aimed to describe key patient characteristics, comorbidities, treatments, and outcomes of a large U.S.-based cohort of patients hospitalized with COVD-19 using electronic health records (EHR). METHODS: We identified patients in the Optum De-identified COVID-19 EHR database who had laboratory-confirmed COVID-19 or a presumptive diagnosis between 20 February 2020 and 6 June 2020. We included hospitalizations that occurred 7 days prior to, or within 21 days after, COVID-19 diagnosis. Among hospitalized patients we describe the following: vital statistics and laboratory results on admission, relevant comorbidities (using diagnostic, procedural, and revenue codes), medications (NDC, HCPC codes), ventilation, intensive care unit (ICU) stay, length of stay (LOS), and mortality. RESULTS: We identified 76,819 patients diagnosed with COVID-19, 16,780 of whom met inclusion criteria for COVID-related hospitalization. Over half the cohort was over age 50 (74.5%), overweight or obese (77.2%), or had hypertension (58.1%). At admission, 30.3% of patients presented with fever (>38C) and 32.3% had low oxygen saturation (<90%). Among the 16,099 patients with complete hospital records, we observed that 58.9% had hypoxia, 23.4% had an ICU stay during hospitalization, 18.1% were ventilated, and 16.2% died. The median LOS was 6 days (IQR: 4, 11). CONCLUSIONS: To our knowledge, this is the largest descriptive study of patients hospitalized with COVID-19 in the United States. We report summary statistics of key clinical outcomes that provide insights to better understand COVID-19 disease epidemiology.