Systematic Review and Meta-Analysis Of Rituximab For The Treatment Of Immune Thrombocytopenia In Adults
Bibliographic record
Abstract
Abstract Background Rituximab, a monoclonal anti-CD20 antibody is commonly used to treat immune thrombocytopenia (ITP). Results of randomized controlled trials (RCTs) evaluating the efficacy of rituximab are conflicting. We conducted a systematic review and meta-analysis of RCTs to determine a more precise estimate of the effect of rituximab on platelet count response in adults with ITP. Methods We searched MEDLINE (from 1946), EMBASE (from 1980), and the Cochrane database using the MeSH terms antibodies, monoclonal, and purpura thrombocytopenia idiopathic and the textwords rituximab, rituxan, mabthera, and immune thrombocytopenic purpura. In duplicate, two reviewers independently assessed study eligibility, abstracted data and assessed each study for methodological quality. Results We identified 4 RCTs (n=360) that met our eligibility criteria. Each trial compared rituximab to placebo combined with other ITP treatments, including dexamethasone, or standard of care. Each trial enrolled non-splenectomized patients only. The likelihood of achieving a platelet count >100 x109/L at 6 months was greater with rituximab than placebo (relative risk [RR] 1.38, 95% CI 1.08-1.76). More patients receiving rituximab achieved a platelet count greater than 50 x109/L at 6 months (RR 1.46, 95% CI 1.18-1.80) compared to placebo. Rituximab was not associated with a reduction in the risk of any bleeding (RR 1.49, 95% CI 0.55-4.04) or an increase in the risk of infection (RR 1.33, 95% CI 0.74-2.38). Conclusions Rituximab is associated with a modest increase in the likelihood of achieving a platelet count greater than >100 x109/L at 6 months compared to placebo. No significant reduction in bleeding or increased risk of infection was observed at 6 months. Randomized trials were generally small, with relatively short follow-up. Large pragmatic multicenter comparative trials are needed to examine durability of response over a longer period of follow-up. Disclosures: Arnold: Amgen: Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding, Speakers Bureau; GlaxoSmithKline: Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Hoffman-LaRoche: Research Funding. Lim:Leo Pharma: Honoraria, Research Funding; Pfizer: Consultancy, Honoraria. Crowther:Asahi Kasai: Membership on an entity’s Board of Directors or advisory committees; Baxter: Membership on an entity’s Board of Directors or advisory committees, Speakers Bureau; Boehringer Ingelheim: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; CSL Behring: Speakers Bureau; Leo Pharma: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Merck: Consultancy; Octapharma: Consultancy, Membership on an entity’s Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria, Research Funding; Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding; Viropharma: Membership on an entity’s Board of Directors or advisory committees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.035 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".