A systematic review of direct oral anticoagulants for thromboprophylaxis in multiple myeloma
Bibliographic record
Abstract
Multiple myeloma (MM) is associated with increased venous thromboembolism (VTE) risk. Current guidelines recommend aspirin or low-molecular-weight heparin for thromboprophylaxis depending on VTE risk. Nevertheless, VTE risks remain high: a recent meta-analysis reported an incidence of 6.2% during the entire MM course. Direct oral anticoagulants (DOACs) showed promising results in other malignancies. This systematic review provides an overview of evidence on DOAC thromboprophylaxis in MM. PubMed and Embase were searched up to November 21, 2023, for studies evaluating MM and DOAC thromboprophylaxis (PROSPERO: CRD42022376152). Two authors independently screened titles, abstracts, and texts, assessed bias using a modified version of the Newcastle Ottawa Scale and certainty of evidence with the Grading of Recommendations Assessment, Development and Evaluation approach, and performed data extraction and analysis. Seven articles comprising 416 patients with DOAC thromboprophylaxis were included, primarily involving newly diagnosed patients with MM (56.3%) receiving lenalidomide-based regimens (69.1%). Overall Newcastle Ottawa Scale study quality was moderate. Four studies reported follow-up duration ranging from 90 days after induction to 7 months. VTE proportions ranged from 0% to 23.5%, with 4 studies reporting 0%. The proportions of minor, clinically relevant nonmajor, and major bleeding ranged from 0% to 18.2%, 0% to 7.7%, and 0% to 4.5%, respectively. Arterial thrombosis proportions ranged from 0% to 2.9%. Only 2 studies reported on mortality (2% and 7.1%). Overall Grading of Recommendations Assessment, Development and Evaluation certainty of evidence was very low for all outcomes. Current evidence regarding routine DOACs in MM is insufficient, warranting further research to establish the DOAC thromboprophylaxis risk-to-benefit ratio in MM.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".