Ang Li1, Qian Wu2, Suhong Luo3, Greg S Warnick4, Neil A Zakai5, Edward N Libby6, Brian F Gage7, David A Garcia1, Gary H Lyman4,6, Kristen M Sanfilippo3,7. 1. Division of Hematology, University of Washington School of Medicine, and. 2. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington. 3. Research Service, St. Louis Veterans Affairs Medical Center, St. Louis, Missouri. 4. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington. 5. Departments of Medicine and Pathology and Laboratory Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont. 6. Division of Medical Oncology, University of Washington School of Medicine, Seattle, Washington; and. 7. Division of General Medical Sciences, Washington University School of Medicine, St. Louis, Missouri.
Abstract
BACKGROUND: Although venous thromboembolism (VTE) is a significant complication for patients with multiple myeloma (MM) receiving immunomodulatory drugs (IMiDs), no validated clinical model predicts VTE in this population. This study aimed to derive and validate a new risk assessment model (RAM) for IMiD-associated VTE. METHODS: Patients with newly diagnosed MM receiving IMiDs were selected from the SEER-Medicare database (n=2,397) to derive a RAM and then data from the Veterans Health Administration database (n=1,251) were used to externally validate the model. A multivariable cause-specific Cox regression model was used for model development. RESULTS: The final RAM, named the "SAVED" score, included 5 clinical variables: prior surgery, Asian race, VTE history, age ≥80 years, and dexamethasone dose. The model stratified approximately 30% of patients in both the derivation and the validation cohorts as high-risk. Hazard ratios (HRs) were 1.85 (P<.01) and 1.98 (P<.01) for high- versus low-risk groups in the derivation and validation cohorts, respectively. In contrast, the method of stratification recommended in the current NCCN Guidelines for Cancer-Associated Venous Thromboembolic Disease had HRs of 1.21 (P=.17) and 1.41 (P=.07) for the corresponding risk groups in the 2 datasets. CONCLUSIONS: The SAVED score outperformed the current NCCN Guidelines in risk-stratification of patients with MM receiving IMiD therapy. This clinical model can help inform providers and patients of VTE risk before IMiD initiation and provides a simplified clinical backbone for further prognostic biomarker development in this population.
BACKGROUND: Although venous thromboembolism (VTE) is a significant complication for patients with multiple myeloma (MM) receiving immunomodulatory drugs (IMiDs), no validated clinical model predicts VTE in this population. This study aimed to derive and validate a new risk assessment model (RAM) for IMiD-associated VTE. METHODS:Patients with newly diagnosed MM receiving IMiDs were selected from the SEER-Medicare database (n=2,397) to derive a RAM and then data from the Veterans Health Administration database (n=1,251) were used to externally validate the model. A multivariable cause-specific Cox regression model was used for model development. RESULTS: The final RAM, named the "SAVED" score, included 5 clinical variables: prior surgery, Asian race, VTE history, age ≥80 years, and dexamethasone dose. The model stratified approximately 30% of patients in both the derivation and the validation cohorts as high-risk. Hazard ratios (HRs) were 1.85 (P<.01) and 1.98 (P<.01) for high- versus low-risk groups in the derivation and validation cohorts, respectively. In contrast, the method of stratification recommended in the current NCCN Guidelines for Cancer-Associated Venous Thromboembolic Disease had HRs of 1.21 (P=.17) and 1.41 (P=.07) for the corresponding risk groups in the 2 datasets. CONCLUSIONS: The SAVED score outperformed the current NCCN Guidelines in risk-stratification of patients with MM receiving IMiD therapy. This clinical model can help inform providers and patients of VTE risk before IMiD initiation and provides a simplified clinical backbone for further prognostic biomarker development in this population.
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