Oral Antiviral Treatment for COVID-19 in Patients With Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: To describe data on the safety and efficacy of molnupiravir (MP) and nirmatrelvir/ritonavir (NM/R) in patients with systemic autoimmune rheumatic diseases (SARDs). METHODS: Among patients with SARD being followed in 2 tertiary outpatient rheumatology clinics, we retrospectively identified those infected with SARS-CoV-2 between February and August 2022 who received MP or NM/R. Patients' medical files were reviewed for demographics and disease-related characteristics, as well as coronavirus disease (COVID-19) characteristics, including vaccination status, antiviral treatment, side effects, and COVID-19 outcomes. RESULTS: Seventy-four patients with SARD (52 females) were identified who had been infected with SARS-CoV-2 and received MP (n = 26, 35.1%) or NM/R (n = 48, 64.9%). Most patients were vaccinated against SARS-CoV-2 (n = 62, 83.8%). Among frequently used regimens were glucocorticoids (n = 43, 58.1%), mycophenolate mofetil (n = 26, 35.1%), tumor necrosis factor inhibitors (n = 14, 18.9%), methotrexate (n = 13, 17.6%), and rituximab (n = 12, 16.2%). Common adverse events were reported only by 4 patients receiving NM/R (metallic taste, gastrointestinal upset, hypertension), not leading to drug discontinuation. During follow-up, all but 2 patients (n = 72, 97.3%) recovered at home without COVID-19-related complications. Nonetheless, we describe 2 presumptive cases of COVID-19 rebound who progressed to severe COVID-19. CONCLUSION: These data show a favorable outcome and acceptable safety profile of the 2 oral antiviral therapies MP and NM/R among a high-risk SARD population. However, cases of COVID-19 rebound are being increasingly identified. These findings call for continuous surveillance to capture the real-world efficacy and safety profiles in our subpopulations of interest.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".