Characteristics, Comorbidities, and Outcomes of SARS-CoV-2 Infection in Patients With Autoimmune Conditions Treated With Systemic Therapies: A Population-based Study
Bibliographic record
Abstract
OBJECTIVE: To describe characteristics and coronavirus disease 2019 (COVID-19) clinical outcomes of patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), or ulcerative colitis (UC) receiving systemic therapies vs the general population. METHODS: This descriptive retrospective cohort study used data from the United States Optum deidentified COVID-19 electronic health record dataset (2007-2020). Adults with COVID-19 were stratified into 3 disease cohorts (patients with RA, PsA, or UC who had received systemic therapy) and a comparator cohort not meeting these criteria. Incidence proportions of hospitalization and clinical manifestations of interest were calculated. Using logistic regression analyses, risk of endpoints was estimated, adjusting for demographics and demographics plus comorbidities. RESULTS: This analysis (February 1 to December 9, 2020) included 315,101 patients with COVID-19. Adjusting for demographics, COVID-19 patients with RA (n = 2306) had an increased risk of hospitalization (OR 1.54, 95% CI 1.39-1.70) and in-hospital death (OR 1.61, 95% CI 1.30-2.00) compared with the comparator cohort (n = 311,563). The increased risk was also observed when adjusted for demographics plus comorbidities (hospitalization OR 1.25, 95% CI 1.13-1.39 and in-hospital death OR 1.35, 95% CI 1.09-1.68]). The risk of hospitalization was lower in COVID-19 patients with RA receiving tumor necrosis factor inhibitors (TNFi) vs non-TNFi biologics (OR 0.32, 95% CI 0.20-0.53) and the comparator cohort (OR 0.77, 95% CI 0.51-1.17). The risk of hospitalization due to COVID-19 was similar between patients receiving tofacitinib and the comparator cohort. CONCLUSION: Compared with the comparator cohort, patients with RA were at a higher risk of more severe or critical COVID-19 and, except for non-TNFi biologics, systemic therapies did not further increase the risk. (ENCePP; registration no. EU PAS 35384).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".