Risk of Severe COVID-19 Outcomes Among Patients with Rheumatoid Arthritis in the United States (preprint)

Introduction Coronavirus disease 2019 (COVID-19) has infected over 22 million individuals worldwide. It remains unclear whether patients with COVID-19 and Rheumatoid Arthritis (RA) experience worse clinical outcomes compared to similar patients with COVID-19 without RA. Objective The aim of this study is to provide insights on how COVID-19 impacted patients with RA given the nature of the disease and medication used. Methods RA cases were identified via International Classification of Diseases (ICD) codes and COVID-19 cases by laboratory results in the U.S. based TriNetX network. Patients with COVID-19 and RA were propensity-score matched based on demographics with patients with COVID-19 without RA at a 1:3 ratio. A hospitalized sub-population was defined by procedure codes. Results We identified 1,014 COVID-19 patients with RA and 3,042 non-RA matches selected from 137,757 patients. The odds of hospitalization (non-RA:23%, RA:24.6%, OR:1.08, 95% CI: 0.88 to 1.33) or mortality (non-RA:5.4%, RA:6%, OR:0.93, 95% CI: 0.65 to 1.34) were not significantly different. The hospitalized sub-population included 249 patients with COVID-19 and RA and 745 non-RA matches selected from 21,435 patients. The risk of intensive care unit (ICU) admission (non-RA:18.8%, RA:18.1%, OR:0.94, 95% CI: 0.60 to 1.45), and inpatient mortality (non-RA:14.4%, RA:14.5%, OR:0.86, 95% CI: 0.53 to 1.40) were not significantly different. Conclusion We did not find evidence suggesting patients with COVID-19 and RA are more likely to have severe outcomes than patients with COVID-19 without RA.

Risk factors for mortality among hospitalized patients with COVID-19 (preprint)

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.

Descriptive epidemiology of 16,780 hospitalized COVID-19 patients in the United States (preprint)

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.