Abstract Number: PB2124
Meeting: ISTH 2020 Congress
Background: Immunomodulatory drug (IMID) based multiple myeloma (MM) treatments are associated with high risk of venous thromboembolism (VTE). Several VTE risk models have been validated recently, including SAVED and IMPEDE VTE, to help stratify patients for thromboprophylaxis. Risk models for serious bleeding complications in this population are lacking.
Aims: We aim to derive a new model to predict major bleeding in IMID-treated older MM patients.
Methods: We selected patients 66 or older with newly diagnosed MM who concurrently received IMID and had complete fee-for-service coverage from the SEER-Medicare database over 7 years. Demographics and risk factors were ascertained from SEER and Medicare files. Major bleeding was defined as bleeding requiring hospitalization using inpatient ICD9 codes as previously validated (Arnason Thromb Res 2006). Patients were followed from IMID initiation to first major bleeding event; they were censored for death, disenrollment, discontinuation of IMID, or end of claims data. Cox regression model was used to determine significant risk factors for major bleeding. Integer points were assigned according to beta coefficients to create risk groups. The model´s discrimination was assessed by Harrell´s c index, hazard ratio (HR), and 95% confidence interval (CI).
Results: Among 2397 IMID-treated MM patients, inpatient major bleeding occurred in 40 during follow-up. Risk factors included history of major bleeding (HR 2.88), renal disease (HR 1.88), and older age (HR 1.38 to 2.63) (Table 1). The final model significantly predicted major bleeding for every point increase (HR 1.79, 1.28-2.50, c index 0.66). Patients with 2+ points (n=526) had more than 3-fold increase in major bleeding (HR 3.31, 1.67-6.58) compared to those
with 0-1 point (n=1848) (Figure 1).
Conclusions: The incidence of major bleeding requiring hospitalization appears low in older MM patients. Nonetheless, a selective higher risk group may be identified. Our model, if externally validated, can help clinicians determine the best risk-benefit trade-off for thromboprophylaxis.
|Hazard Ratio||P Value||95% CI||Points|
|Comorbidity last year|
|__History of major bleeding (n=158)||2.88||0.01||1.26-6.57||2|
|__History of renal disease (n=624)||1.88||0.06||0.98-3.61||1|
|Age (baseline 66-75)|
|__Age 76-85 (n=859)||1.38||0.35||0.70-2.71||1|
|__Age 86+ (n=192)||2.63||0.04||1.04-6.67||2|
|Model Discrimination||C Index|
|__Every 1 point increase||1.79||0.001||1.28-2.50||0.66|
|__2+ vs. 0-1 point||3.31||0.001||1.67-6.58||0.63|
[Bleeding Risk Assessment Model for IMID-Treated Myeloma Patients]
To cite this abstract in AMA style:Li A, Warnick G, Libby E, Garcia D, Lyman G. Bleeding Risk Assessment Model for Older Multiple Myeloma Patients on Immunomodulatory Drugs (IMIDs) [abstract]. Res Pract Thromb Haemost. 2020; 4 (Suppl 1). https://abstracts.isth.org/abstract/bleeding-risk-assessment-model-for-older-multiple-myeloma-patients-on-immunomodulatory-drugs-imids/. Accessed December 6, 2021.
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