Risk of Severe COVID-19 Infection in Patients With Inflammatory Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: To describe the cohort of patients with inflammatory rheumatic diseases (IRD) hospitalized due to SARS-CoV-2 infection in the Ramón y Cajal Hospital, and to determine the increased risk of severe coronavirus disease 2019 (COVID-19) in patients with no IRD. METHODS: This is a retrospective single-center observational study of patients with IRD actively monitored in the Department of Rheumatology who were hospitalized due to COVID-19. RESULTS: Forty-one (1.8%) out of 2315 patients admitted due to severe SARS-CoV-2 pneumonia suffered from an IRD. The admission OR for patients with IRD was 1.91 against the general population, and it was considerably higher in patients with Sjögren syndrome, vasculitis, and systemic lupus erythematosus. Twenty-seven patients were receiving treatment for IRD with corticosteroids, 23 with conventional DMARDs, 12 with biologics (7 rituximab [RTX], 4 anti-tumor necrosis factor [anti-TNF], and 1 abatacept), and 1 with Janus kinase inhibitors. Ten deaths were registered among patients with IRD. A higher hospitalization rate and a higher number of deaths were observed in patients treated with RTX (OR 12.9) but not in patients treated with anti-TNF (OR 0.9). CONCLUSION: Patients with IRD, especially autoimmune diseases and patients treated with RTX, may be at higher risk of severe pneumonia due to SARS-CoV-2 compared to the general population. More studies are needed to analyze this association further in order to help manage these patients during the pandemic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".