Time Since Rituximab Treatment Is Essential for Developing a Humoral Response to COVID-19 mRNA Vaccines in Patients With Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: We aimed to investigate (1) whether patients with rheumatic disease (RD) treated with rituximab (RTX) raise a serological response toward the coronavirus disease 2019 (COVID-19) mRNA vaccines, and (2) to elucidate the influence of time since the last RTX dose before vaccination on this response. METHODS: We identified and included 201 patients with RDs followed at the outpatient clinic at the Department of Rheumatology, Aarhus University Hospital, who had been treated with RTX in the period 2017-2021 and who had completed their 2-dose vaccination series with a COVID-19 mRNA vaccine. Total antibodies against the SARS-CoV-2 spike protein were measured on all patients and 44 blood donors as reference. RESULTS: We observed a time-dependent increase in antibody response as the interval from the last RTX treatment to vaccination increased. Only 17.3% of patients developed a detectable antibody response after receiving their vaccination ≤ 6 months after their previous RTX treatment. Positive antibody response increased to 66.7% in patients who had RTX 9-12 months before vaccination. All blood donors (100%) had detectable antibodies after vaccination. CONCLUSION: Patients with RDs treated with RTX have a severely impaired serological response toward COVID-19 mRNA vaccines. Our data suggest that the current recommendations of a 6-month interval between RTX treatment and vaccination should be reevaluated.
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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 | 0.003 |
| 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) | 0.002 | 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".