Association Between Patient Demographics and Smoldering Multiple Myeloma Progression to Multiple Myeloma: A SEER-Medicare Data Analysis.

Abstract

Background: While several risk stratification models for smoldering multiple myeloma (SMM) to symptomatic multiple myeloma (MM) progression have been developed, the association between patient demographics, such as race, gender, and age, and SMM progression is not well understood.

Methods: Analyzing surveillance, epidemiology, and end results (SEER)-Medicare data, we applied a previously developed algorithm to identify patients with SMM diagnosed between 2007 and 2019. We used noncancer patients from the 5% random sample of Medicare beneficiaries as the controls. Cox proportional hazards models were applied to assess the association between race/gender/age and the development of hypercalcemia, renal failure, anemia and bone disease among SMM patients and the controls. We applied bootstrapping to calculate the estimates hazard ratios (aHRs) and 95% confidence intervals (CIs) of progression among SMM patients, adjusting for that of the noncancer controls.

Results: Out of 1235 identified SMM patients (median age 75 years, White 76.7%), 856 (69.3%) of them progressed to symptomatic MM. Race (Black vs. White aHR = 0.82, 95% CI: 0.65-1.01) and gender (male vs. female aHR = 0.99, 95% CI: 0.86-1.13) were not significantly associated with SMM progression. Only age was negatively associated with SMM progression (75-79 years vs. 66-69 years aHR = 0.71, 95% CI: 0.58-0.87; 80-84 years vs. 66-69 years aHR = 0.59; 95% CI: 0.46-0.74; and ≥ 85 years vs. 66-69 years aHR = 0.59; 95% CI: 0.45-0.75).

Conclusion: This analysis provided insight into important parameters for MM natural history modeling by demonstrating that only age, but not race and gender, is negatively associated with SMM progression.