TY - JOUR
T1 - Derivation and validation of a risk assessment model for immunomodulatory drug-associated thrombosis among patients with multiple myeloma
AU - Li, Ang
AU - Wu, Qian
AU - Luo, Suhong
AU - Warnick, Greg S.
AU - Zakai, Neil A.
AU - Libby, Edward N.
AU - Gage, Brian F.
AU - Garcia, David A.
AU - Lyman, Gary H.
AU - Sanfilippo, Kristen M.
N1 - Publisher Copyright:
© JNCCN - Journal of the National Comprehensive Cancer Network.
PY - 2019
Y1 - 2019
N2 - 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 fromthe Veterans Health Administration database (n=1,251) were used to externally validate the model. A multivariable causespecific 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 highversus 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.
AB - 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 fromthe Veterans Health Administration database (n=1,251) were used to externally validate the model. A multivariable causespecific 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 highversus 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.
UR - http://www.scopus.com/inward/record.url?scp=85070113776&partnerID=8YFLogxK
U2 - 10.6004/jnccn.2018.7273
DO - 10.6004/jnccn.2018.7273
M3 - Article
C2 - 31319391
AN - SCOPUS:85070113776
SN - 1540-1405
VL - 17
SP - 840
EP - 847
JO - JNCCN Journal of the National Comprehensive Cancer Network
JF - JNCCN Journal of the National Comprehensive Cancer Network
IS - 7
ER -