Garlapati, Sameer Krishna Prasad

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.

e18542 Background: Outcomes in adult acute lymphocytic leukemia (ALL) remain heterogeneous and strongly influenced by age. However, population-level survival gradients across older age strata and the future burden of disease in an aging population remain incompletely characterized. We evaluated long-term survival patterns and projected disease burden in adult ALL using national registry data. Methods: We conducted a retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER) database including adults aged 50–89 years diagnosed with ALL between 2000 and 2022. Overall survival (OS) was estimated using Kaplan–Meier methods and compared across age and racial subgroups. Multivariable Cox proportional hazards regression was performed to identify independent predictors of mortality. Temporal trends in annual case counts were analyzed and projected through 2035 using time-series forecasting methods. Results: A total of 7,298 adult ALL patients were included. OS declined progressively with advancing age, demonstrating clear stepwise separation across age strata. Compared with patients aged 50–54 years, mortality risk increased incrementally across successive age groups, reaching more than fourfold higher risk among patients aged 85–89 years on multivariable analysis. Race-based survival differences were observed on unadjusted analysis; however, these differences were attenuated after adjustment for age and clinical factors. More recent year of diagnosis was independently associated with modest survival improvement, reflecting incremental therapeutic progress. Forecasting analysis projected a continued rise in adult ALL case burden through 2035, with widening uncertainty intervals over time, indicating sustained future healthcare impact. Conclusions: Adult ALL outcomes remain strongly age dependent, with sharply escalating mortality risk among older patients despite gradual therapeutic improvements. Projections indicate a sustained and growing disease burden in the United States. These findings highlight the urgent need for age-adapted treatment strategies, improved inclusion of older adults in clinical trials, and healthcare system preparedness to address the needs of an aging leukemia population.