Obinutuzumab‐related adverse events: A systematic review and meta‐analysis
Bibliographic record
Abstract
Rituximab, the first anti-CD20 monoclonal antibody, has dramatically improved outcomes for patients with B-cell lymphoproliferative disorders. Obinutuzumab was developed to potentiate activity and overcome resistance to rituximab. Clinical data suggest that obinutuzumab is superior to rituximab in follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL). Yet, it has increased toxicity. This systematic review and meta-analysis compiled all randomized controlled trials (RCTs) comparing obinutuzumab-based regimens with rituximab-based regimens to better assess their toxicity profile. Primary outcome was grade 3-4 infections; secondary outcomes included any adverse events (AE), grade 3-4 AE, drug discontinuation rate, and 3-years mortality. Relative risks (RRs) were estimated and pooled using a fixed-effect model, unless there was significant heterogeneity, in which case a random-effects model was used. Our comprehensive search yielded five RCTs conducted between 2009 and 2014, including 4247 patients. The trials included FL patients, CLL and diffuse large B cell lymphoma. Monoclonal antibodies were given with different chemotherapy regimens (in four trials) or as monotherapy (in one trial). The point estimate favored increase in both grade 3-4 infections rate (RR 1.17 [95% CI, 1.0-1.36]) and any AE rate (RR 1.05 [95% 1-1.1]) with obinutuzumab, although this was not statistically significant. There was a significantly increased rate of grade 3-4 AE (RR 1.15 [95% CI, 1.09-1.2]), as well as grade 3-4 toxicities including thrombocytopenia (RR 2.8 [95% CI, 1.92-4.06]), infusion related reactions (RR 2.8 [95% CI, 2.16-3.64]) and cardiac events (RR 1.65 [95% CI, 1.11-2.46]). There was no significant difference in grade 3-4 anemia and neutropenia nor in the 3-year mortality rate. The point estimate favored increase in discontinuation rate due to AE with obinutuzumab, although without statistical significance (RR 1.24 [95% CI, 1.0-1.54]). In conclusion, physicians need to weigh the clinical benefits of this agent against higher toxicity.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".