MELISSE, a large multicentric observational study to determine risk factors of venous thromboembolism in patients with multiple myeloma treated with immunomodulatory drugs
Bibliographic record
Abstract
Immunomodulatory drugs (IMiDs) are associated with an increased risk of venous thromboembolism (VTE) in multiple myeloma (MM) patients. We designed MELISSE, a multicentre prospective observational study, to evaluate VTE incidence and identify risk factors in IMiDs-treated MM. Our objective was to determine the real-life practice of VTE prophylaxis strategy. A total of 524 MM patients were included, and we planned to collect information at baseline, at four and at 12 months, on MM therapy, on VTE risk factors and management. VTE incidence was 7% (n=31), including 2.5% pulmonary embolism (PE) (n=11), similar at four or 12 months. VTE was observed at all risk assessment levels, although the increased risk assessment level correlated to a lower rate of VTE, maybe due to the implemented thromboprophylaxis strategy. VTE occurred in 7% on aspirin vs 3% on low-molecular-weight heparin (LMWH) prophylaxis, and none on vitamin K antagonists (VKA). New risk factors for VTE in IMiDs-treated MM were identified. In conclusion, VTE prophylaxis is compulsory in IMiDs-treated MM, based on individualised VTE risk assessment. Anticoagulation prophylaxis with LMWH should clearly be prioritised in MM patients with high VTE risk, along with VKA. Further prospective studies will identify most relevant VTE risk factors in IMiDs-treated MM to select accurately which MM patients should receive LMWH prophylaxis and for which duration to optimise VTE risk reduction.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".