Incidence and Risk Factors of Venous Thromboembolism in Patients with Multiple Myeloma Receiving Immunomodulatory Agents
Bibliographic record
Abstract
Abstract Multiple myeloma (MM) is an incurable plasma cell disorder representing 10% of all hematologic malignancies. Cancer is a known risk factor for venous thromboembolism (VTE). Patients with MM are at a particularly high risk of developing VTE owing to patient characteristics (e.g. previous history of VTE), disease characteristics, and treatment characteristics including use of the immunomodulatory agents (IMIDs). Unfortunately, standard criteria to identify the patients most at risk for developing VTE in MM while receiving IMIDs are unknown. We sought to assess the incidence of VTE and its associated risk factors in MM patients receiving IMID therapy. A retrospective cohort study including 1680 consecutive patients with multiple myeloma treated at our centre between January 01, 1995 and January 26, 2016 was conducted. The annual incidence of VTE on immunomodulatory agents including thalidomide, lenalidomide, and pomalidomide was derived. Univariate incidence ratio analyses of VTE for different risk factors was performed including: previous history of VTE, concomitant use of dexamethasone, and ≥/< 6 months after IMID initiation. A total of 309 MM patients treated with an immunomodulatory agent were identified. Nineteen patients were excluded (incomplete data, lost to follow). Of the remaining 290 patients, the mean age was 67.9 and 42.4% were female. Twenty-seven VTE events were recorded. The overall risk ratio was 6.1 for the development of VTE. Patients with a personal history of VTE had an increased risk of suffering a VTE while on IMID therapy (IRR 5.4; CI, 1.9-13.6). The time from the initiation of the IMID therapy (less than 6 months) also increased the risk of developing a VTE (IRR 51.7; CI,19.4-160.1). The concomitant use of dexamethasone was not associated with a statistically significant increased risk (IRR 1.7; CI, 0.3-69.5). Incidence risk ratios for these risk factors are depicted in Table 1. Our results suggest that a personal history of VTE and the time from the initiation of the IMID (less than 6 months) are associated with an increased risk of VTE in MM patients receiving IMID therapy. This may be helpful in determining which multiple myeloma patients treated with an IMID agent warrant more aggressive thromboprophylaxis. Further prospective studies are needed to determine the optimal agent, intensity, and duration of thromboprophylaxis in patients with MM on IMID therapy. Disclosures McCurdy: Celgene: Honoraria.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".