Risk of Rituximab-Associated Severe Adverse Events Increases with Young Age in Children with Nephrotic Syndrome
Bibliographic record
Abstract
Background: Rituximab prevents relapse in steroid-dependent frequently relapsing nephrotic syndrome (SDFRNS). We aimed to assess the safety of rituximab in children with steroid-resistant nephrotic syndrome (SRNS) or SDFRNS. Methods: This single-center retrospective study included all children with SRNS/SDFRNS treated with rituximab since 2007 at our institution. All information concerning adverse events (AE) were obtained from medical records. Severity of adverse events was graded according to the Common Terminology Criteria for Adverse Events. We performed a survival analysis and log-rank tests or proportional hazards models to determine hazard ratios (HR) with 95% confidence intervals (CI) of risk factors associated with severe AE (SAE). Results: Of the 38 children included in this study, most had a SDFRNS (n=36, 95%). Median age at diagnostic was 3.4 (interquartile range, 2.4-6.2) years and median age at rituximab initiation was 9.0 (6.8-13.6) years. Median [95% CI] time to relapse was 1.4 [1.16-2.27] years. Median follow-up time was 3.7 (2.2-4.9) years. No patient died during follow-up. Fourteen SAE occurred in 12 (32%) patients, including one case of Pneumocystis jiroveci pneumonia, 6 cases of severe neutropenia and 2 cases of inflammatory colitis. Rituximab initiation before 10 years of age was associated with a higher risk of SAE (HR [95%CI], 11.3 [1.44, 88.6], Figure 1) and all SAE occurred in children aged <10 years except for anaphylactic reactions. The occurrence of a SAE was not associated with an increased risk of relapse.Figure 1.: Cumulative risk of SAE according to age at rituximab initiationConclusions: A young age at rituximab initiation for SRNS/SDFRNS is associated with an increased risk of SAE. Rituximab should be used with particular caution in children under 10 years old. Funding: Government Support - Non-U.S.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".