Data · dataset · 2026
Real‑world pharmacovigilance of rituximab‑associated adverse reactions in children and adolescents: Disproportionality signal detection and time‑to‑onset pattern characterization using the FAERS database
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<p>Rituximab is widely used for B‑cell malignancies and autoimmune diseases, but its pediatric safety profile requires systematic real‑world evaluation.
Description
This research mined safety signals of adverse events (AEs) linked to rituximab in pediatric patients using U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) data from 2004 to 2025. A total of 2,231 pediatric AE reports involving rituximab were identified and analyzed using four disproportionality methods (ROR, PRR, BCPNN, and MGPS) for signal detection and Weibull distribution analysis for time-to-onset patterns.
Results showed a balanced sex distribution in AE reports (47.7% male, 48.5% female). Most reports involved adolescents aged 12–17, with an overall increasing trend. A total of 149 positive safety signals were detected via disproportionality analysis.
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Several of these signals pertained to immune system – and infection‑related adverse events. Notably, the elevated signal magnitudes for certain signals were substantially confounded by indication bias, pre‑existing comorbidities, and reports associated with therapeutic failure. Stratified analyses revealed differences in the signal distribution of adverse‑event reports across age and sex subgroups.
Time‑to‑onset analysis of adverse‑event reports indicated that most events documented in these reports occurred within 30 d of therapy initiation, with a median of 7 d. This study characterizes the reported safety profile of rituximab in pediatric patients based on FAERS spontaneous reports. Our findings represent exploratory signal hypotheses only and can serve as a reference for subsequent relevant investigations.</p>
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Where it is published
- DOI doi.org/10.6084/m9.figshare.34023907.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/34023907 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
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| concepts[field].anzsrc:group:3105 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Genetics'] |
| concepts[field].anzsrc:group:3204 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Immunology'] |
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| concepts[field].local:field:medicine-health | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
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| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |