A pharmacovigilance study of rituximab-associated adverse events in immune-mediated kidney diseases and transplant-related kidney diseases
Bibliographic record
Abstract
BACKGROUND: Rituximab, a chimeric monoclonal antibody targeting CD20+ B lymphocytes, has demonstrated efficacy in various immune-mediated kidney diseases beyond its original indications for non-Hodgkin lymphoma, rheumatoid arthritis, and granulomatosis with polyangiitis. Because traditional therapies for kidney diseases often involve substantial toxicity and limited efficacy, rituximab offers a more targeted approach. However, comprehensive safety evaluations in kidney diseases remain limited, necessitating a detailed investigation of adverse reactions associated with long-term rituximab use. METHODS: This study analyzed adverse events (AEs) associated with rituximab across 55 kidney diseases (49 immune-mediated and 6 transplant-related) using the FDA Adverse Event Reporting System, Japanese Adverse Drug Event Report Database, and the Canada Vigilance Adverse Reaction Database through signal detection, WHO-UMC causality assessment, multivariate regression, and Bayesian analysis, with temporal patterns, differential analysis, and omics data integration to elucidate underlying mechanisms. RESULTS: This comprehensive pharmacovigilance study revealed novel safety signals for rituximab use in immune-mediated kidney diseases. Beyond confirming known adverse reactions, significant associations emerged for malignancies, particularly bladder cancer [reporting odds ratio (ROR) = 6.33] and pituitary tumors (ROR = 12.66). Notable psychiatric adverse reactions were also identified, including anxiety (ROR = 2.08), reading disorder, and attention deficit hyperactivity disorder. Time-to-onset analysis revealed that most AEs occurred beyond 100 days of treatment initiation. In extended therapy (>720 days), increased frequencies of malignancies and psychiatric disorders were observed. Multivariate analysis confirmed that tumor risk was significantly associated with rituximab use (odds ratio = 1.41), while psychiatric adverse reactions showed different risk patterns. CONCLUSION: These findings highlight the importance of vigilant, personalized surveillance in patients with immune-mediated kidney diseases receiving prolonged rituximab therapy, particularly considering the potential risks of malignancies. Given the extended treatment duration, further prospective studies and controlled trials are essential to better understand these long-term effects, optimize monitoring strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".