Long-term survival, disparities, and future burden of multiple myeloma: A population-based analysis.

Abstract

e19562 Background: Multiple myeloma is a chronic blood cancer in which patients often live for many years, yet the risk of death remains ongoing. While survival has improved over time, population-level studies that combine traditional survival analysis, advanced modeling techniques, and future burden projections are limited. Methods: We performed a population-based retrospective study using the Surveillance, Epidemiology, and End Results (SEER) database. Overall survival was evaluated using Kaplan–Meier analysis and Cox proportional hazards regression. Parametric survival modeling was conducted using Weibull regression to better characterize long-term mortality patterns. Machine-learning survival analysis was performed using random survival forests to assess the relative importance of clinical and demographic factors. Annual case counts were analyzed and projected through 2035 using time-series forecasting methods. Results: The study included 8,627 patients with multiple myeloma. Overall survival declined gradually over long-term follow-up, with approximately one-quarter to one-third of patients surviving at extended time points. Female patients experienced better survival than males. Significant survival differences were observed across racial groups, with Asian or Pacific Islander patients demonstrating the most favorable outcomes and American Indian or Alaska Native patients the poorest. In multivariable analyses, age, sex, race, and year of diagnosis were independently associated with survival. Parametric modeling closely mirrored non-parametric survival estimates and supported a time-dependent pattern of mortality. Machine-learning analysis identified year of diagnosis as the strongest predictor of survival, followed by age and race. Forecasting suggested that the burden of multiple myeloma will remain substantial through 2035. Conclusions: Multiple myeloma is associated with long-term survival but persistent mortality risk and ongoing demographic disparities. Combining traditional survival analysis with parametric and machine-learning approaches provides a more complete understanding of outcomes and highlights the sustained future burden of this disease, with important implications for clinical care and healthcare planning.