Risk of infectious adverse events of venetoclax therapy for hematologic malignancies: a systematic review and meta-analysis of RCTs
Bibliographic record
Abstract
ABSTRACT: Venetoclax is a small molecule inhibitor of BCL-2 used in the treatment of acute myelogenous leukemia (AML) and chronic lymphocytic leukemia (CLL). Recent postmarketing studies of ibrutinib, another small molecule inhibitor, suggested that these agents may predispose to opportunistic infections. We sought to systematically review the randomized controlled trial (RCT) evidence of venetoclax to assess whether it predisposes patients to infectious adverse events (IAEs) and neutropenia. We systematically reviewed RCTs comparing venetoclax therapy with active or placebo controls for patients with hematologic malignancies. Data on IAEs and neutropenia were pooled by Bayesian meta-analysis, and we computed the probability of any increased risk (P[risk ratio (RR) > 1]) of IAEs or neutropenic complications. Seven RCTs were included, comprising 2067 patients. In CLL (n = 1032), there was a low probability of increased risk of high-grade (P[RR > 1] = 71.2%) and fatal IAEs (P[RR > 1] = 64.5%) and high-grade neutropenia (P[RR > 1] = 63.4%). There were insufficient data to perform a meta-analysis of IAEs in AML; however, 1 trial suggested an increased risk of IAEs with venetoclax. Furthermore, in AML (n = 642), venetoclax was associated with a high probability of increased risk of high-grade neutropenia (P[RR > 1] = 94.6%) and febrile neutropenia (P[RR > 1] = 90.6%). Our results suggest that venetoclax has a low probability of increased risk of IAEs or neutropenia in CLL. By contrast, there is likely increased risk of high-grade neutropenia and febrile neutropenia in AML. Importantly, our analyses did not identify any specific IAEs that would benefit from routine antimicrobial prophylaxis or pre-emptive testing.
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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".