Chronological age predicts suboptimal multiple myeloma treatment independent of frailty and socioenvironmental factors: A retrospective multi-center cohort study

Abstract

Abstract Background: Multiple myeloma (MM) is the second most common hematologic malignancy in the United States, with a median age at diagnosis of 69 years (Siegel, Cancer Statistics, 2020).1 Despite advances in well-tolerated therapies, many older adults with MM remain undertreated, with lower rates of triplet-based induction and autologous stem cell transplant (ASCT) in those who may benefit (Munshi, Cancer, 2020; Flannelly, Biol Blood Marrow Transplant, 2020).2,3 In a prior multi-institutional retrospective analysis of 5,691 patients across three U.S. health systems; Veterans Affairs (VA), MedStar Health, and Boston Medical Center, increasing chronological age was the only consistent demographic predictor of suboptimal treatment (Bodanapu, Blood, 2024).4 However, frailty was not captured in the original analysis, limiting insight into physiological aging. In this planned secondary analysis of the VA and MedStar cohorts, we evaluated the associations of age and frailty, measured using the validated Veterans Affairs Frailty Index (VA-FI), with receipt of optimal care. Methods: Adults with newly diagnosed MM between 2012 and 2024 who received MM-directed therapy at the VA or MedStar and had sufficient data to calculate VA-FI were included. The outcome was receipt of optimal therapy, defined as triplet or quadruplet induction and/or ASCT. We applied the validated VA-FI and stratified patients into three frailty categories: robust (VA-FI score ≤ 0.1), prefrail (VA-FI score 0.1-0.2), and frail (VA-FI > 0.2). We used multivariable logistic regressions separately fitted for each cohort to assess associations between age, frailty, and receipt of optimal therapy, adjusting (when available) for sex, race/ethnicity, Charlson Comorbidity Index, ISS (International Staging System) or Revised-ISS (R-ISS), and socioenvironmental factors including Area Deprivation Index, distance from patient's residence to hospital, household income, housing status, insurance status, and preferred language. Results: The VA cohort included 4,887 MM patients (median age 69 [interquartile range (IQR) 64–76]; 46.6% frail [n=2,277]), while the MedStar cohort included 804 patients (median age 66 [IQR 58–73]; 9.8% frail [n=79]). Across both cohorts, the majority of patients received optimal therapy (VA: 71.9%, n=3514; MedStar: 75.7%, n=609). In both the VA cohort and the MedStar cohort, each year of increasing age associated with lower odds of receiving optimal treatment (VA: OR 0.93 [95% CI 0.92-0.94], p<0.001; MedStar: OR 0.94 [95% CI 0.92-0.96], p<0.001), even after adjusting for frailty, comorbidities, and socioenvironmental characteristics. Frail patients in the VA cohort were significantly less likely to receive optimal treatment compared to robust patients (OR 0.78 [0.61-1.00, p=0.047]). No difference in receipt of optimal treatment was found between frail and robust patients in the MedStar cohort (OR 1.10 [0.58–2.13], p = 0.778). Conclusion: In this secondary analysis of two large, real-world cohorts of patients with MM, increasing age remained a strong and independent predictor of suboptimal treatment, even after adjusting for frailty, comorbidities, and socioenvironmental factors. These findings suggest that chronological age continues to disproportionately influence treatment decisions, despite the availability of well-tolerated therapies and validated tools to assess physiological reserve. In the era of precision medicine, integrating routine frailty assessments into MM care pathways is essential to move beyond age-based decision-making and support individualized, equitable treatment strategies for older adults. References 1. Cancer statistics, 2020 - Siegel - 2020 - CA: A Cancer Journal for Clinicians - Wiley Online Library. Accessed August 4, 2025. 2. Flannelly C, Tan BEX, Tan JL, et al. Barriers to Hematopoietic Cell Transplantation for Adults in the United States: A Systematic Review with a Focus on Age. Biol Blood Marrow Transplant. 2020;26(12):2335-2345 3. Munshi PN, Vesole D, Jurczyszyn A, et al. Age no bar: A CIBMTR analysis of elderly patients undergoing autologous hematopoietic cell transplantation for multiple myeloma. Cancer. 2020;126(23):5077-5087 4. Bodanapu G, Corrigan J, Culnan J, et al. Risk Factors for Suboptimal Treatment in Patients with Newly Diagnosed Multiple Myeloma across US Healthcare Systems. Blood. 2024;144(Supplement 1):3341