Treatment of Venous Thromboembolism in Acute Leukemia: A Systematic Review
Bibliographic record
Abstract
Background: Patients with hematologic malignancies are up to 26 times more likely to develop venous thromboembolism (VTE) than the general population. The standard treatment for VTE is anticoagulation, however, it9s safety and efficacy in this patient population has not been formally evaluated. In addition, severe thrombocytopenia and coagulopathies in patients with hematologic malignancies makes treatment of VTE challenging and the optimal treatment strategy for VTE in this patient population is unclear. Methods: We conducted a systematic review of the literature aiming to identify observational studies and randomized trials describing treatment of VTE in the setting of acute leukaemia (AL) including, acute myeloid leukemia (AML), acute promyelocytic leukemia (APL), and acute lymphoblastic leukemia (ALL). Due to the heterogeneity of findings, no meta-analysis was attempted. Results: A total of 13 observational studies, including 11 cohort studies and 2 case control studies, were included with 5,359 participants. Number of patients who developed VTE among the total population was 330 (6.2%; 95% CI 5.5-6.8). Of patients with VTE, 229 patients received treatment with anticoagulation. Agents used for anticoagulation included low-molecular-weight heparin (LMWH), unfractionated heparin (UFH), and vitamin K antagonists (VKA). Despite lack of consensus, most studies adjusted dose of anticoagulant based on platelet (PLT) counts. Most commonly, LMWH dose was reduced by 50-75% for PLT counts less than 50 X 109 /L. Many studies temporarily held anticoagulation in the face of severe thrombocytopenia (PLT Conclusion: In summary, our systematic review obtained information about different anticoagulation regimens used for treatment of VTE in AL patients, highlighting the significant lack of data in this area. In addition, there is a high degree of heterogeneity in choice of anticoagulant, dose adjustments in the face of thrombocytopenia, duration of anticoagulation and bleeding and recurrence rates. Further studies are required to develop guidelines and suggestions for treatment of VTE in AL. Disclosures Lazo-Langner: Daiichi Sankyo: Research Funding; Pfizer: Honoraria; Bayer: Honoraria; Alexion: Research Funding.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| 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".