Rituximab-Induced Hypogammaglobulinemia in Childhood Nephrotic Syndrome: Systematic Review and Meta Analysis
Bibliographic record
Abstract
Background: Rituximab, a chimeric anti-CD20 monoclonal antibody, is used as a steroid-sparing agent in complicated pediatric nephrotic syndrome. Hypogammaglobulinemia is a known side effect that can lead to severe infections and mortality, yet its incidence and risk factors remain unclear. This review aimed to determine the incidence of rituximab-induced hypogammaglobulinemia and related infections in children with nephrotic syndrome. Methods: A systematic review of 5 databases from 1974-2024 was completed. Randomized and non-randomized studies with pediatric (<18 years) nephrotic syndrome patients receiving ≥1 rituximab dose were included. Hypogammaglobulinemia was defined by IgG levels >2 SD below the age-matched reference range. Results: 33 studies (n=3,495) were eligible for meta-analysis. The pooled incidence of hypogammaglobulinemia was 11.4% (95%CI: 6.4-19.5). Patients receiving rituximab had increased risk of hypogammaglobulinemia (RR: 1.81, 95%CI: 1.46-2.25). Meta-regression showed no significant effect of follow-up duration, age, sex, or cumulative dose. Of 517 hypogammaglobulinemia episodes, 93 (18%) infections occurred—4 fatal, 2 requiring ICU admission, 2 chronic, and 73 severe. Conclusion: Hypogammaglobulinemia is a relatively common side effect of rituximab that can result in life threatening or chronic infections. The risk of hypogammaglobulinemia and infections should be weighed against the effectiveness of rituximab for nephrotic syndrome management.Random effects meta-regression model for incidence of hypogammaglobinemia adjusting for follow-up, age, sex and cumulative dose of Rituximab - Random effects, z-distribution Point Estimate Standard Error 95% Lower bound 95% Upper bound Z-value P-value Intercept -4.91 4.553 -13.82 4.02 -1.077 0.281 Follow-up -0.02 0.162 -0.34 0.30 -0.130 0.896 Age 0.08 0.210 -0.33 0.50 0.400 0.689 Sex (% male) 0.02 0.060 -0.10 0.14 0.325 0.746 Cumulative dose 0.001 0.001 -0.001 0.003 0.641 0.522
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".