ABSTRACT
ObjectivesTo identify factors associated with COVID-19 test positivity and assess viral and antibody test concordance.DesignObservational retrospective cohort study.SettingOptum de-identified electronic health records including over 700 hospitals and 7000 clinics in the USA.ParticipantsThere were 891 754 patients who had a COVID-19 test identified in their electronic health record between 20 February 2020 and 10 July 2020.Primary and secondary outcome measuresPer cent of viral and antibody tests positive for COVID-19 (‘positivity rate’);adjusted ORs for factors associated with COVID-19 viral and antibody test positivity;and per cent concordance between positive viral and subsequent antibody test results.ResultsOverall positivity rate was 9% (70 472 of 771 278) and 12% (11 094 of 91 741) 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. Antibody test concordance among patients with an initial positive viral test was 91% (71%–95% depending on time between tests). Among tests separated by at least 2 weeks, discordant results occurred in 7% of patients and 9% of immunocompromised patients. Factors associated with increased odds of viral and antibody positivity in multivariable models included: male sex, Hispanic or non-Hispanic black or Asian race/ethnicity, uninsured or Medicaid insurance and Northeast residence. We identified a negative dose effect between the number of comorbidities and viral and antibody test positivity. Paediatric patients had reduced odds (OR=0.60, 95% CI 0.57 to 0.64) of a positive viral test but increased odds (OR=1.90, 95% CI 1.62 to 2.23) of a positive antibody test compared with those aged 18–34 years old.ConclusionsThis study identified sociodemographic and clinical factors associated with COVID-19 test positivity and provided real-world evidence demonstrating high antibody test concordance among viral-positive patients.
ABSTRACT
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 including over 700 hospitals and 7000 clinics in the USA. PARTICIPANTS: 17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020. MAIN OUTCOME MEASURE: All-cause mortality while hospitalised. 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 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 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% 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 cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.