During the 10th ICTHIC, prof Kristen Sanfilippo (Assistant Professor at the Washington University School of Medicine in St. Louis, USA) discussed risk prediction of venous thromboembolism (VTE) in multiple myeloma patients. She presented some recent data on VTE risk in this subset of patients, the pros and cons of the available risk assessment models, and she shared some unpublished data on biomarkers that could improve existing risk models.
VTE risk in patients with multiple myeloma
The risk of VTE in patients with multiple myeloma is higher than in the general population.
A large study including more than 4 million US veterans investigated the risk of deep vein thrombosis (DVT) in people with monoclonal gammopathy of undetermined significance (MGUS, a multiple myeloma precursor condition) and multiple myeloma . The study found that patients with multiple myeloma have a 9-fold increased risk of developing DVT, with the greatest risk during the first year after diagnosis. MGUS cases had a stable 3-fold increased risk of DVT over time .
The risk of VTE in multiple myeloma is multifactorial and is related to myeloma-specific therapy, disease-related physiologic changes, iatrogenic procedures, and patient comorbidities .
The emergence of both VTE and arterial thromboembolism (ATE) in patients with cancer is typically highest in the period that immediately follows diagnosis. A study looked at thrombotic events that occurred following initiation of myeloma-directed therapy and found that most events occur within the first six-month window .
The impact of VTE in patients with multiple myeloma leads to an increase in morbidity and mortality. The same study found almost a 2.37-fold increase in the risk of thrombosis in patients with newly diagnosed multiple myeloma starting therapy compared to those who did not have VTE .
These findings were similar to those collected by another study that looked at both VTE and ATE events. However, focusing on the VTE events, a three-fold increased risk of death exists one year following myeloma diagnosis .
Risk assessment scores for multiple myeloma
The IMWG model
Given the morbidity and mortality and the increased incidence in patients with multiple myeloma, the international myeloma working group (IMWG) generated a risk assessment model in 2008, which was subsequently adopted by the National Cancer Comprehensive Network (NCCN) guidelines [5, 6].
The risk model was developed using an extrapolation of risk factors from alternate patient populations, including those with cancer and the general population. The patients were categorized into lower risk (0-1 risk factor) and higher risk (2 or more risk factors) groups, and prophylaxis was recommended accordingly based on risk group .
Analyzing the model’s performance and consequent prophylaxis guidelines in real practice, a study by Bradbury and colleagues found an incidence of VTE greater than 10% even in the presence of prophylaxis .
The study analyzed two therapeutic studies in myeloma, the Myeloma IX and Myeloma XI trials. There was no protocol-mandated prophylaxis in the Myeloma IX trial, which resulted in a prophylaxis prevalence of about 20%. However, by the time the Myeloma XI trial protocol was written, the prophylaxis guideline by the IMWG existed, and prophylaxis increased to 80% .
Comparing overlapping regimens in the Myeloma IX and XI trials, high rates of VTE can be seen in both, regardless of changes in prophylaxis rates. Looking specifically at the Myeloma XI study, six months after starting therapy (both lenalidomide-based or thalidomide-based therapy), the incidence of VTE was still greater than 10%, even in the presence of prophylaxis .
This correlates with the results of another study that evaluated the performance of the IMWG guidelines.
The study found that the IMWG model did not accurately predict the early onset of VTE and had a C-index of about 0.5. The C-index measures the discriminatory power of a risk model to categorize patients between high and low risk. An index of 0.5 is roughly like flipping a coin, whereas a C-index of 1.0 would means the model can accurately discriminate patients every time .
These results indicate that the IMWG proposed model has a low ability to identify which patients are at high risk of developing VTE.
The Khorana score
The Khorana score is the first risk model developed, and national guidelines recommend its use to identify high-risk patients for consideration of thromboprophylaxis when starting therapy. It predicts the VTE risk in ambulatory cancer patients, and it has been validated in numerous studies [9, 10].
Prof. Sanfilippo’s group examined whether the Khorana score could effectively predict the VTE risk in patients with multiple myeloma, given the small number of myeloma patients involved in the initial derivation cohort.
Yet unpublished work from Prof. Sanfilippo’s Group found that the Khorana score does not efficiently predict VTE development in patients with multiple myeloma.
The SAVED score
The need for a myeloma-specific VTE risk prediction model led to the development of the SAVED score, published in 2019 to predict VTE in patients with multiple myeloma on an immunomodulatory therapy .
The score comprises five clinical variables; it was developed using the SEER-Medicare database and was validated using data from the Veterans Health Administration database .
A significant association existed between high-risk patients (identified by at least two points) and VTE development compared to patients identified at low risk (with one point or less).
The discriminatory ability was better than the IMWG model, with a C-index of 0.61 in the derivation cohort, which remained relatively stable at 0.60 in the validation cohort .
The score was recently externally validated in a study focused on patients with multiple myeloma receiving daratumumab-based regimens .
The IMPEDE score
Prof. Sanfilippo and colleagues also developed the IMPEDE score looking at patients with newly diagnosed multiple myeloma who were starting a myeloma-specific therapeutic regimen .
As expected, immunomodulatory therapy posed a significant risk VTE within the IMPEDE model. Additional variables include weight, patient comorbidities, additional concurrent therapies, race, and history of VTE .
Patients are divided into three risk groups:
- the lowest risk group has a score of three or less and a cumulative VTE incidence at six months of 3.3%
- the second risk group has a score between 4 and 7 and a VTE risk at six months of 8.3%
- the highest risk group is characterized by a score equal to or higher than 8 and a six-month risk of 15.2% 
The score was externally validated within three cohorts: the SEER-Medicare, the MELISSE, and the Cleveland Clinic Foundation [12-14]. The C-index is about 0.65, similar to the SCORED score and higher than the IMWG model.
For this reason, based on the evidence available thus far, the SAVED score and IMPEDE score are now recommended for use in patients with multiple myeloma receiving immunomodulatory therapy to stratify the risk of VTE during treatment (NCCN Guidelines – Multiple Myeloma – Version 1.2021).
Unpublished data on biomarkers for multiple myeloma risk assessment
To improve the IMPEDE risk model, Prof. Sanfilippo and colleagues tried to identify biomarkers that could facilitate risk prediction.
In a yet unpublished study, they used a cohort of 546 patients. In addition, they did a nested case-control study identifying 35 patients with VTE and matching them to random controls at a rate of 1 to 4.
They obtained a total cohort of 38 VTE patients and 137 patients without VTE during the first six months of therapy. The median time to develop VTE within the first six-month period was 51 days, and about a third of the patients had a pulmonary embolism.
This cohort analyzed the IMPEDE VTE score, D-dimer, and insoluble P-selectin before initiation of myeloma-directed therapy.
IMPEDE VTE score was significantly higher in patients with VTE, as was the D-dimer; no significant difference in soluble P-selecting between the two groups was found.
Looking at the association between these variables and VTE prediction within the first six months, the IMPEDE score highlighted an 18% increase in the risk of thrombosis, with a C-index of 0.60 (which is relatively unchanged from what found in bigger cohorts previously described).
Regarding soluble P-selectin, no significant association was found with the development of VTE.
Finally, when looking at D-dimer as a continuous variable, there was a significant association with VTE risk, as high D-dimer levels were associated with the development of VTE within the first six-month period. Both the IMPEDE score and D-dimer remained significant when adjusted for each other.
Both the IMWG model and the Khorana score modestly predict VTE in patients with multiple myeloma.
The SAVED and the IMPEDE models are specific for multiple myeloma disease and improve VTE prediction in this setting.
Biomarkers such as D-dimer can improve clinical risk prediction of thrombosis in patients with multiple myeloma.
Perspective studies in this population are needed to determine the optimal prophylaxis based on VTE risk.
1. Kristinsson SY, Fears TR, Gridley G, et al. Deep vein thrombosis after monoclonal gammopathy of undetermined significance and multiple myeloma. Blood. 2008;112(9):3582-3586.
2. Swan D, Rocci A, Bradbury C, Thachil J. Venous thromboembolism in multiple myeloma – choice of prophylaxis, role of direct oral anticoagulants and special considerations. Br J Haematol. 2018;183(4):538-556.
3. Schoen MW, Carson KR, Luo S, et al. Venous thromboembolism in multiple myeloma is associated with increased mortality. Res Pract Thromb Haemost. 2020;4(7):1203-1210. Published 2020 Sep 25.
4. Kristinsson SY, Pfeiffer RM, Björkholm M, Schulman S, Landgren O. Thrombosis is associated with inferior survival in multiple myeloma. Haematologica. 2012;97(10):1603-1607.
5. Palumbo A, Rajkumar SV, Dimopoulos MA, et al. Prevention of thalidomide- and lenalidomide-associated thrombosis in myeloma. Leukemia. 2008;22(2):414-423.
6. Kumar SK, Callander NS, Adekola K, et al. Multiple Myeloma, Version 3.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2020;18(12):1685-1717. Published 2020 Dec 2.
7. Bradbury CA, Craig Z, Cook G, et al. thrombosis in patients with myeloma treated in the Myeloma IX and Myeloma XI phase 3 randomized controlled trials. Blood. 2020;136(9):1091-1104. Blood. 2020;136(17):1994.
8. Li A, Wu Q, Luo S, et al. Derivation and Validation of a Risk Assessment Model for Immunomodulatory Drug-Associated Thrombosis Among Patients With Multiple Myeloma. J Natl Compr Canc Netw. 2019;17(7):840-847.
9. Khorana AA, Kuderer NM, Culakova E, Lyman GH, Francis CW. Development and validation of a predictive model for chemotherapy-associated thrombosis. Blood. 2008;111(10):4902-4907.
10. Mulder FI, Candeloro M, Kamphuisen PW, et al. The Khorana score for prediction of venous thromboembolism in cancer patients: a systematic review and meta-analysis. Haematologica. 2019;104(6):1277-1287.
11. Wang J, Park C, Arroyo-Suarez R. Venous thromboembolism in patients with multiple myeloma receiving daratumumab-based regimens: a post hoc analysis of phase 3 clinical trials [published online ahead of print, 2021 Apr 9]. Leuk Lymphoma. 2021;1-8.
12. Sanfilippo KM, Luo S, Wang TF, et al. Predicting venous thromboembolism in multiple myeloma: development and validation of the IMPEDE VTE score. Am J Hematol. 2019;94(11):1176-1184.
13. Tardy B. Predicting the risk of venous thromboembolism in newly diagnosed myeloma with immunomodulatory drugs: External validation of the IMPEDE VTE score. Am J Hematol. 2020;95(1):E18-E20.
14. Covut F, Ahmed R, Chawla S, et al. Validation of the IMPEDE VTE score for prediction of venous thromboembolism in multiple myeloma: a retrospective cohort study. Br J Haematol. 2021;193(6):1213-1219.