Evaluation of Bleeding and Thrombocytopenia in Older Adults with Acute Myeloid Leukemia Treated with Hypomethylating Agents: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Acute myeloid leukemia (AML) is a myeloid malignancy associated with cytopenias and significant morbidity and mortality. Hypomethylating agents (HMA), such as azacitidine and decitabine, form the backbone of outpatient chemotherapy for many patients with AML. Thrombocytopenia, a well-established risk factor for bleeding, is common and related to both underlying disease and as well as treatment. However, the frequency and severity of bleeding, particularly with novel combination therapies, has not been well described (CRD42022339160). Objectives: Our primary objective was to evaluate the incidence and severity of bleeding in patients with AML receiving hypomethylating agents. We also aimed to explore the relationship between bleeding and thrombocytopenia and the use of supportive care strategies to prevent bleeding. Population: Adult patients (age ≥ 18 years) with AML (≥80% of the study population) treated with hypomethylating agents. Outcomes: Our primary outcome was grade 3 or 4 bleeding, defined using National Cancer Institute's Common Toxicity Criteria for Adverse Events (NCI-CTCAE). Secondary outcomes included bleeding of any grade, deaths due to bleeding, grade 3 or 4 thrombocytopenia using NCI-CTCAE, duration of thrombocytopenia, platelet count at time of bleeding, and number of patients who received tranexamic acid to prevent or treat bleeding. Methods: We searched Medline (Ovid), Embase (Ovid), CENTRAL (Cochrane Library) and CINAHL from inception to April 2021 to identify relevant citations of published trials, using individualized systematic search strategies for each database. We included randomized control trials of adults with AML receiving hypomethylating therapy. Studies were included if they evaluated HMA therapy in patients with AML and reported at least one outcome of interest related to bleeding, thrombocytopenia, or supportive measures used. We used Freeman-Tukey transformation to calculate the weighted summary proportion using a random effects model. Results: We included 12 unique trials enrolling 2,105 patients. Azacitidine was studied in 6 trials (n=998 patients), while 5 trials evaluated decitabine (n=580 patients), and 2 trials studied combination hypomethylating agent therapy (HMA and venetoclax) (n=401 patients). The median patient age was 75, and 69% were male. Grade 3 or 4 bleeding occurred in 9% of patients (95% confidence interval (CI) 6 to 12%; n=4 trials; 313 patients). Death due to bleeding was reported in 3% of patients (95% CI 1 to 6%; n=3 trials; 215 patients). Grade 3 or 4 thrombocytopenia occurred in 35% of patients (95% (CI) 26 to 44%; n=11 trials; 1,506 patients). Duration of thrombocytopenia, platelet count at time of bleeding event, and number of patients who received tranexamic acid to prevent or treat bleeding was not reported. Conclusion: The incidence and severity of bleeding and thrombocytopenia in patients with AML treated with hypomethylating agents have not been systematically reported. Further, the relationship between bleeding and thrombocytopenia and the use of supportive care strategies to prevent bleeding were not well characterized. Comprehensive reporting of bleeding risk factors and bleeding events, as well as prophylactic strategies used to prevent bleeding, are needed to inform best practice and optimize supportive care for this high-risk patient population.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".