Incidence and Risk of Hematological Adverse Events Associated With Immune Checkpoint Inhibitors: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) have been a breakthrough in cancer therapy. ICI therapy is generally better tolerated than cytotoxic chemotherapy; however, hematological adverse events (AEs) have not been fully analyzed. Hence, we performed a meta-analysis to evaluate the incidence and risk of ICI-related hematological AEs. Methods: A systematic literature search was performed using PubMed, EMBASE, Cochrane Library, and the Web of Science Core Collection. Phase III randomized controlled trials (RCTs) involving ICI combination regimens were selected. The experimental group received ICIs with systemic treatment, and the control group received only the same systemic treatment. Odds ratios (ORs) for anemia, neutropenia, and thrombocytopenia were calculated using a random-model meta-analysis. Results: We identified 29 RCTs with 20,033 patients. The estimated incidence rates for anemia of all grades and grades III-V were 36.5% (95% confidence interval (CI) 30.23 - 42.75) and 4.1% (95% CI 3.85 - 4.42), respectively. The incidence of neutropenia (all grades 29.7%, grades III-V 5.3%) and thrombocytopenia (all grades 18.0%, grades III-V 1.6%) was also calculated. Conclusion: Treatment with ICIs seemed unlikely to increase the incidence of anemia, neutropenia, and thrombocytopenia in all grades. However, programmed cell death-1 receptor ligand inhibitors significantly increased the risk of grades III-V thrombocytopenia (OR 1.53; 95% CI 1.11 - 2.11). Further research is needed to examine the potential risk factors.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.045 |
| Bibliometrics | 0.008 | 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".