Comparing the Efficacy of Aspirin or Low Molecular Weight Heparin or Vitamin K Antagonists in the Risk of Thromboembolic Events in Patients with Multiple Myeloma Treated with Lenalidomide-Based Therapy
Bibliographic record
Abstract
Abstract Background Malignancy is a well-recognized risk factor for venous thromboembolism (VTE). In multiple myeloma the incidence of VTE varies between 3% and 10%. Immunomodulatory drugs (IMiDs) play a crucial role in the treatment of myeloma and are known to be associated with an increased risk of arterial and venous thromboembolic events (TE). It appears that patients with newly diagnosed multiple myeloma (NDMM) are at higher risk for TE compared to patients with relapsed or refractory myeloma (RRMM) at the start of IMiD therapy. Lenalidomide is a second-generation IMiD which in combination to dexamethasone has shown to be an effective and well-tolerated therapy for patients with NDMM or RRMM. However, studies have consistently demonstrated the need for TE prophylaxis in patients receiving the combination lenalidomide-dexamethasone which leads to a 4.4-fold increased risk for VTE compared to dexamethasone alone in the absence of prophylactic anticoagulants. Panel consensus from the International Myeloma Work Group has agreed that the choice of thromboprophylaxis depends on the individual risk of TE, as determined by patient and treatment-related factors, such as obesity, prior VTE, central venous catheter, immobilization, recent surgery, comorbidities, use of erythropoietin stimulating agents and myeloma therapy. Aspirin (ASA) is recommended for patients with one or no risk factors, and LMWH for those with more than one risk factor. However, the optimal approach to thromboprophylaxis has not yet been established. In this study we sought to compare the efficacy of ASA or low molecular weight heparin (LMWH) or vitamin K Antagonists (VKA) in the prevention of VTE or arterial thromboembolism (ATE) in patients with myeloma using lenalidomide-based therapy. Methods We performed a retrospective chart review in 2 centres (London, Canada; and Salamanca, Spain) on patients with NDMM or RRMM multiple myeloma receiving lenalidomide-based therapy. We collected data from january 2010 to December2014. We included adult patients diagnosed with NDMM or RRMM receiving lenalidomide-based therapy. We did not include who received lenalidomide but refused or had contra-indication to thromboprophylaxis; or used single agent lenalidomide. Results We included 168 patients with multiple myeloma receiving lenalidomide-based therapy. 14 (8%) were NDMM and 154 (92%) had RRMM. Median age was 68 (31-89) and 106 (63%) were males. On average patients with RRMM had 1.6 previous treatments (range:1-11). 104 (62%) patients were low risk and 64 (38%) were high risk for TE. 140 (83%) patients received prophylaxis with ASA, 32 (19%) LMWH and 6 (4%) VKA. 10 patients started with prophylactic LMWH for an average of 2 months then were empirically switched over to ASA. In total, there were 18 (10.7%) TE of which, 16 (9.5%) were VTE: 3 PE, 12 DVT, 1 both. The relative risk for TE was the same regardless of risk stratification [RR=1.27 (95%CI 0.524 - 3.059; p=0.599)]. At TE diagnosis, 16 patients were on ASA, 1 on LMWH and 1 on VKA. The relative risk of TE was significantly higher for patients on ASA compared to LMWH or VKA [RR= 2.17 (0.522 - 9.03; p= 0.286). After the TE, all patients changed anticoagulation strategy: 15 of 16 (94%) patients with VTE switched to therapeutic LMWH and 1 who was on VKA had ASA added. In the 2 patients with ATE, 1 started on full dose LMWH and the other one continued on ASA. 16 of 18 patients with a TE continued on lenalidomide-based therapy. There was no recurrent arterial or venous TE within the first 6 months of anticoagulation after the TE. Univariate analysis suggested that BMI, use of ASA and sex could be potential predictors of TE; but the logistic regression was not statistically significant (Table). Conclusions In patients with multiple myeloma on lenalidomide-based therapy the preferred TE prophylactic approach is low dose ASA irrespective of patients' risk assessment for thromboembolism. However, VTE risk in these patients is not negligible (9.5%) and low dose ASA may not be the best prophylactic strategy for them. It appears that patients on ASA, obese and males are at higher risk for TE. Future studies are needed to confirm these assumpations. Table 1. Multivariate Analysis of the TE risk for patients with myeloma on lenalidomide-based therapy Odds Ratio Variable Point Estimate 95% CI p -value Sex 2.316 0.854 6.278 0.0989 BMI 1.705 0.501 5.804 0.3935 ASA 3.870 0.486 30.796 0.2010 Disclosures Louzada: Celegene: Consultancy, Other: advisory board and expert opinion; pfizer: Consultancy, Other: advisory board and expert opinion; janssen: Consultancy, Other: advisory board and expert opinion. Mateos:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Ocio:Array BioPharma: Consultancy, Research Funding; Celgene: Consultancy, Honoraria; Amgen/Onyx: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy; Mundipharma: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; MSD: Research Funding; Pharmamar: Consultancy, Research Funding; Janssen: Honoraria. Porras:Celgene: Consultancy, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".