Evolution of real-world frontline treatment patterns in multiple myeloma.

Introduction

The number of Food and Drug Administration–approved treatments for multiple myeloma (MM), the second most common hematologic malignant neoplasm, has increased substantially over the past 2 decades. 1 Approved agents now include immunomodulatory agents, proteasome inhibitors (PIs), and immunotherapies, including monoclonal antibodies, antibody-drug conjugates, chimeric antigen receptor T cells, and bispecific T-cell engagers. 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11

With rapidly expanding therapeutic options, treatment decisions have become more complex with the variable adoption of new agents in clinical practice. 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 Although survival has improved with therapeutic advances, gains are not equitably distributed across patient populations. 25 , 26 , 27 , 28 , 29 , 30 Historically, treatment decisions were mainly driven by drug availability, toxicity profiles, and patient fitness, whereas real-world evidence on treatment use across diverse patient populations remains limited. This evidence gap is particularly pronounced in groups that comprise a substantial portion of MM cases but are underrepresented in clinical trials, including older adults, medically complex patients, and patients experiencing socioeconomic and health system barriers. Few large-scale studies have systematically examined how treatment patterns and survival trends have evolved over time in socioeconomically and geographically diverse populations and the potential contribution of social determinants of health (SDOH) to the evolution of these trends.

The US Department of Veterans Affairs (VA) health system offers a unique setting to address this gap. Given that the VA is the nation’s largest integrated health care system, it functions as a single-payer system that minimizes cost-related access barriers. 31 , 32 Within this equal-access context, we sought to characterize the real-world temporal trends in frontline treatment patterns and overall survival (OS) of veterans with newly diagnosed MM (NDMM) and evaluate how clinical, demographic, and socioeconomic factors, including the deprivation of neighborhood-level resources, affect MM treatment and outcomes.

Methods

Study design and patients

We performed a retrospective, longitudinal cohort study of patients with NDMM using data from the nationwide VA health care system. Data were obtained from the VA Corporate Data Warehouse (CDW), which collates electronic health record data from facilities nationwide. The cohort included patients receiving initial treatment for MM at the VA from 1 January 2000 to 31 December 2024. Patients were required to have >3 International Classification of Diseases (ICD-9 [203.0] or ICD-10 [C90.0]) codes and receipt of at least 1 MM treatment (excluding corticosteroids) on or after the date of their first MM ICD code, as in our prior work. 32 , 33 The index date for each patient was determined to be the date of their first MM treatment after their first MM ICD code. Patients were excluded if they were not VA users (having ≥1 appointment, with an ICD code before their first MM ICD code) or were diagnosed with another hematologic malignant neoplasm (acute and chronic leukemias, aplastic anemia, myelodysplastic syndrome, hairy cell leukemia, small lymphocytic lymphoma, and myeloproliferative neoplasms) before the index date ( supplemental Table 1 ).

Outcome

OS was the primary outcome, defined as the length of time from the start of treatment that patients are still alive. Eligible patients were followed through 31 December 2024, at which point patients were censored if still alive. In addition, patients were censored at the diagnosis of another hematologic malignant neoplasm after the index date. The main exposure was MM diagnosis era, categorized as <2007, ≥2007 to <2012, ≥2012 to <2017, ≥2017 to <2022, and ≥2022.

Study variables

All baseline variables were defined based on data recorded in the CDW before the index date. This included age, sex, self-identified race (White, Black, multiracial, other/unknown), self-identified ethnicity (not Hispanic or Latino, Hispanic or Latino, unknown), body mass index (BMI; underweight [BMI <18.5 kg/m 2 ], normal weight [BMI 18.5-24.9 kg/m 2 ], overweight [BMI 25.0-29.9 kg/m 2 ], obese [BMI 30.0-39.9 kg/m 2 ], and severely obese [BMI >40.0 kg/m 2 ]), smoking status (current/former, never, and unknown), autologous hematopoietic stem cell transplantation status (received or did not receive a transplant), weighted Charlson comorbidity index (CCI; no comorbidities [score 0], mild [score 1-2], moderate [score 3-4], and severe [score >5]) based on comorbidities in the 3 years before the index date, distance from VA hospital (defined by distance in kilometers between the zip codes of the VA hospital in which the patient was initially treated for MM and their address associated with that hospital; 0-25, 25-50, 50-75, 75-100, 100-125, 125+, or unknown), geographic region (Northeast, Midwest, North Atlantic, Pacific, Southeast), and area deprivation index (ADI; 0-20, 21-40, 41-60, 61-80, and 81-100). Baseline laboratory values were obtained using the laboratory test date closest to the index date within the year prior until 7 days after and included serum lactate dehydrogenase (LDH; >243 or ≤243 U/L), serum β 2 -microglobulin (β 2 m; >3.5 or ≤3.5 μg/mL), serum creatinine ( <0.8, ≥0.8 to ≤2, or >2 mg/dL), and serum albumin (>3.5 or ≤3.5 g/dL). Disease staging was extracted as documented (either International Staging System [ISS] or Revised ISS [R-ISS] stage I, II, III, if available) using our previously validated algorithm. 34 If ISS or R-ISS stage was not documented, it was calculated from the albumin/β 2 m values.

MM treatment regimens were determined by combining the list of MM treatments that were prescribed within the first 90 days of the index date. Treatments were then grouped in the following categories: immunomodulator (IMiD) monotherapy, PI monotherapy, combined IMiD and proteoasome inhibitor (IMiD + PI), combined monoclonal antibody and PI (MA + PI), combined IMiD, MA, and PI (IMiD + MA + PI), other. An “other” category included all other treatment combinations, including various conventional chemotherapy regimens.

The ADI was derived using the 12-digit Federal Information Processing System codes for states and counties that corresponded to the patients’ addresses and categorized into quintiles, where higher quintiles reflect greater socioeconomic disadvantage. 35 , 36 ADI integrates factors such as income, education, employment, and housing quality to determine socioeconomic status and identify areas requiring targeted interventions to improve health outcomes. ADI categorizes neighborhoods into quintiles based on level of deprivation or lower socioeconomic status. Geographic region was defined based on the location of the patient’s hospital. CCI was calculated using ICD-9/10 codes and the R “comorbidity” package version 1.0.7. 37

Statistical analyses

Descriptive statistics, including medians and interquartile ranges for continuous variables and percentages for categorical variables, were generated for demographic, clinical, and treatment variables at the index date. Age and laboratory values for LDH, β 2 m, creatinine, and albumin were analyzed as continuous variables. Sex, self-identified race, self-identified ethnicity, BMI, smoking status, transplantation status, ISS stage, weighted CCI, ADI, geographic region, distance from the VA, treatment regimen, and diagnosis era were analyzed as categorical variables. The laboratory values were also categorized into groups based on clinically relevant levels. A descriptive analysis of the frequency of use of each treatment combination was performed across 5 treatment eras (<2007, ≥2007 to <2012, ≥2012 to <2017, ≥2017 to <2022, and ≥2022). The Kaplan-Meier analysis was used to estimate survival, and the Cox proportional hazards models were used for multivariable analysis. The following sensitivity analyses were conducted: restricting the cohort to IMiD + PI recipients, restricting the cohort to patients with ≥30 days of follow-up, and incorporating a race-ethnicity interaction. In addition, sensitivity analyses using inverse probability weighting were performed to address potential confounding in the comparison of triplet (IMiD + PI) vs doublet regimens. Propensity scores were calculated using a logistic regression model adjusted for age, race, ethnicity, sex, ADI, geographic region, distance from hospital, smoking status, BMI, transplant status, CCI, creatinine, LDH, β 2 m, and albumin, stratified by treatment era; stabilized weights were calculated based on the propensity scores. Doubly robust Cox proportional hazards models adjusted for the same variables were used to estimate the hazard ratio (HR). Missing values were coded as a separate level in all analyses. Statistical significance was defined as P value < .05. All analyses were performed using R version 4.4.1.

Study oversight

This study was approved by the VA Boston Research and Development Committee as an exempt study before data collection and analysis, with a waiver of informed consent because of the use of existing data, per the Common Rule.

Results

We evaluated 20 135 patients with NDMM treated within the VA health system from 1 January 2000 to 31 December 2024 ( supplemental Figure 1 ). Baseline demographic and clinical characteristics at the time of initial treatment are outlined in Table 1 . The median age at diagnosis was 71.20 (interquartile range, 64.23-77.43). Most patients were male (n = 19 556 [97.1%]) and self-identified as White (n = 11 938 [59.3%]), although the proportion of Black and multiracial patients increased in more recent treatment eras.

Treatment regimen and era with OS

For frontline treatment, IMiDs (as monotherapy, as well as combined IMiD + MA + PI and IMiD + PI regimens) were used in 11 738 (58.3%) patients. The use of MA-containing regimens increased substantially in the ≥2022 treatment era as compared to earlier periods (13.4% vs 2.1%; P < .001). Similarly, quadruplet therapy was more frequently used in the ≥2022 treatment era (10.8% vs 1.4%; P < .001). Compared with IMiD + PI regimen, patients receiving IMiD or PI monotherapy experienced inferior OS (IMiD: HR, 1.16; 95% confidence interval [CI], 1.09-1.23; P < .01; PI: HR, 1.36; 95% CI, 1.27-1.47; P < .01; Table 2 ). Consistent results were shown in standalone analyses using inverse probability weighting comparing IMiD + PI regimen vs PI alone (HR, 1.33; 95% CI, 1.20-1.49; P < .001; supplemental Figure 2 ) and IMiD + PI regimen vs IMiD alone (HR, 1.17; 95% CI, 1.04-1.27; P = .010; supplemental Figure 3 ). Median OS was the longest in the IMiD + PI cohort ( Figure 1 ). Across diagnosis eras, OS improved progressively ( Table 2 ).

Inferior OS in older, frail patients

Patients aged ≥65 years had a 43% higher risk of death compared to those aged <65 years (HR, 1.43; 95% CI, 1.37-1.49; P < .001; Table 2 ). Median OS was 3.2 years in those aged ≥65 years and 5.4 years in those aged <65 years ( supplemental Figure 4 ). Increasing comorbidity burden, measured by the weighted CCI, was associated with progressively worse OS ( Table 2 ).

Demographic trends and OS

Female patients had a 21% lower risk of death than male patients (HR, 0.79; 95% CI, 0.70-0.90; P < .01; Table 2 ). There was a higher use of IMiD + PI triplet regimen in Black and Hispanic or Latino patients. Compared with White patients, Black patients had improved OS (HR, 0.79; CI, 0.75-0.82; P < .001; Table 2 ), whereas Hispanic or Latino patients had improved OS relative to non-Hispanic or Latino patients (HR, 0.83; 95% CI, 0.76-0.91; P < .001; Table 2 ). When restricted to patients receiving IMiD + PI triplet regimens, the survival advantage in Black patients persisted (HR, 0.82; 95% CI, 0.73-0.93; P = .002); the survival advantage in Hispanic or Latino patients similarly persisted, although it was no longer statistically significant because of the wider CIs (HR, 0.81; 95% CI, 0.64-1.03; P = .084; supplemental Table 2 ). Median OS did not differ significantly across major geographic regions.

SDOH associated with MM survival

Moreover, a higher neighborhood deprivation, measured by ADI, was independently associated with worse OS. Compared to patients residing in the least-deprived neighborhoods (lowest ADI quintile [0-20]), patients residing in neighborhoods within the second-highest quintile (61-80) had a 7% increased risk of death (HR, 1.07; 95% CI, 1.00-1.14; P = .044; Table 2 ), whereas patients in the highest quintile (81-100) experienced a 13% increase in risk (HR, 1.13; 95% CI, 1.06-1.21; P < .001; Table 2 ). The Kaplan-Meier curves, stratified by ADI, are found in supplemental Figure 5 . In sensitivity analyses that evaluated the interactions between ADI and race and between ADI and ethnicity, the effect of ADI did not differ by race ( supplemental Table 3 A) or ethnicity ( supplemental Table 3 B), other than marginally worse survival for Black patients in the second-lowest ADI quintile (HR, 1.19; 95% CI, 1.00-1.42; P = .044).

Increasing distance from the treating VA facility was paradoxically associated with improved OS, with patients residing >125 km from a VA center experiencing lower mortality than those living within 25 km (HR, 0.85; 95% CI, 0.81-0.89; P < .001). A sensitivity analysis restricted to patients with ≥30 days of follow-up from treatment initiation was performed and yielded consistent results ( supplemental Table 4 ).

Validated prognostic indicators are all associated with OS

Established prognostic indicators including lower BMI and higher ISS, LDH, creatinine, and weighted CCI were all significantly associated with inferior OS, highlighting the clinical validity of this study ( Table 2 ). Specifically, patients with lower albumin (<3 g/dL), higher β 2 m (>3.5 μg/mL), higher LDH (>243 U/L), abnormal creatinine values (<0.8 or >2.0 mg/dL), more comorbidities (mild, moderate, or severe CCI), underweight BMI (<18.5 kg/m 2 ), and higher ISS (stage II or III) had statistically significant worsened OS as compared to their peer groups with higher albumin (>3 g/dL), lower β 2 m (<3.5 μg/mL), lower LDH (<243 U/L), normal creatinine values (between 0.8 and 2.0 mg/dL), no comorbidities, normal to severely obese BMI (>18.5 kg/m 2 ), and lower ISS ( Table 2 ). Median OS and survival curves, stratified by validated prognostic indicators, are displayed in supplemental Figure 6 .

Treatment era affects OS

Frontline treatment regimens have changed with new advancements. The percentage of “other” drastically reduced from a peak of 59.8% in the pre-2007 era to 24.5% in the post-2022 era ( Figure 2 ). During this time, combined IMiD + PI treatment reached its peak use of 31.0% in the 2017-2022 era and combined IMiD + MA + PI continues to rise with 10.9% in the post-2022 era. A stepwise improvement in OS was demonstrated in successive treatment eras as compared to the <2007 era ( Table 2 ).

Discussion

In this large national VA cohort of patients with NDMM, we demonstrate that advances in MM therapy have translated into improved OS over time, but socioeconomic disadvantage remains independently associated with worse outcomes, even within an equal-access health care system. Overall, these findings demonstrate that both biologic risk and social context (SDOH) consistently shape survival in MM, as demonstrated by currently validated prognostic markers and the stepwise association between increasing ADI and mortality. Moreover, we show that the removal of cost-related barriers alone is insufficient to eliminate disparities over time. Consistent with prior studies in other cancers, our findings suggest that SDOH, such as housing instability, transportation challenges, and social support, as measured by ADI, can potentially affect MM outcomes beyond insurance coverage and drug access alone. 38 , 39 , 40 , 41

Our results are also consistent with prior studies suggesting that modern combined regimens (triplet and quadruplet therapy and novel agents) have improved OS as compared to monotherapy and doublet regimens for patients with MM. 42 , 43 Unlike prior studies, however, our cohort represents, to our knowledge, the largest real-world study examining these trends at time of initial treatment start date of NDMM and with the use of a VA patient cohort, consequently mitigating the role of cost and insurance barriers driving treatment decisions. We did not observe a statistically significant OS difference between combined IMiD, PI, and MA (ie, quadruplet) regimens, the newest iteration of guideline-recommended treatment to date, to our knowledge, and triplet regimens. This is contrary to previous findings of large head-to-head studies; however, these results are likely attributable to the relatively small number of patients treated with quadruplet therapy and their more advanced age, coupled with the limited follow-up inherent to evaluating a regimen introduced only recently in our health system. 44 , 45 This may have precluded the detection of its full survival benefit and, consequently, should not be considered as evidence of equivalence of the 2 regimens. A 2-year OS analysis stratified by treatment era demonstrated the highest survival rates among patients treated after 2022, suggesting that our quadruplet cohort is underpowered and that the expected survival benefit of this regimen has not yet been fully captured in the current analysis ( supplemental Table 5 ). Future studies with a longer follow-up period and a larger patient cohort will be necessary to more thoroughly characterize real-world outcomes specifically in this treatment group.

Importantly, higher socioeconomic burden, as evidenced by the higher quintiles of ADI, was associated with a stepwise increase in the risk of death. We demonstrated that despite the removal of health system barriers through VA-provided care, there remains a negative impact of SDOH on survival outcomes in patients with NDMM. Future prospective studies will be needed with a comparable non-VA cohort to make causal inferences regarding the true impact of health system barriers on such outcomes. Patients living in more disadvantaged neighborhoods are more likely to face barriers to healthy food access, opportunities for exercise, and weight management. Altogether, these factors likely contribute to the differences in comorbidities and treatment tolerance and may partially explain the association between higher ADI and worse survival.

BMI emerged as another notable factor in our analysis. Our finding that higher BMI was associated with improved OS, although counterintuitive, is consistent with previously reported results in female populations, in which greater adipose tissue is postulated to confer a higher tolerance and energy reserve during the physiologic stress of MM treatment. 46 , 47

Our findings of improved OS in Black patients compared to White patients and Hispanic or Latino patients compared to non-Hispanic or Latino patients are consistent with previous findings in settings where treatment use is similar across race and ethnicity. 29 , 48 Additional work is needed to investigate the role of biologic mechanisms, such as differences in cytogenetics, in these findings, as well as the influence of different comorbidity profiles across self-identified race and ethnicity.

Interestingly, the inverse association between distance from a VA medical center and mortality was unexpected. A prior large multi-institutional comprehensive cancer study of patients with MM showed a similar association, although studies of other cancers, both solid and hematologic, have been mixed. 49 , 50 In similar studies, 1 explanation for this finding was that distance could serve as a proxy for functional status, by which patients able to travel longer distances for care may be healthier and have more resources. 49 Alternatively, because CCI (a well-established marker of functional status) linearly correlated with worse outcomes, it is conceivable that VA-specific transportation and temporary lodging programs may facilitate care for patients traveling farther distances, leading to improved OS. These hypotheses warrant prospective evaluation and validation.

Several limitations of our study warrant consideration. First, the VA population is overwhelmingly male. Second, treatment categorization necessitated grouping heterogeneous treatment regimens into broader classes within the “other” category (eg, chemotherapy as monotherapy and in combined regimens) in an attempt to focus on specific era-defining treatment modalities. In addition, ISS and R-ISS were combined based on the availability of data. Maintenance therapy and cytogenetic data were unable to be effectively extracted from the CDW. Given their established survival benefit in NDMM and likely unequal distribution across regimens, their omission is a potential source of residual confounding that may affect the interpretation of our findings. Finally, because of the retrospective, observational nature of our study, future prospective studies are needed to validate these findings.

In conclusion, our study provides real-world evidence from a large national NDMM cohort demonstrating that modern therapies have improved survival, but socioeconomic factors, such as neighborhood disadvantage, persistently adversely affect outcomes, even within an equal-access health care system. Altogether, these findings emphasize the need for interventions that address social and structural barriers to care in tandem with therapeutic innovations to achieve equitable improvements in MM survival across diverse populations.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

The current affiliation for C.V.E. is Division of Plasma Cell Neoplasias, Dana-Farber Cancer Institute, Boston, MA. The current affiliation for A.L. is Division of Hematology and Oncology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA.

Acknowledgments

The authors acknowledge financial support from National Institutes of Health (NIH), National Cancer Institute grants P01-155252-07, including supplements 3P01CA155258-07S1 and 3P01CA155258-07S2, Department of Veterans Affairs (VA) research funding 1I01BX001584, NIH Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity Program grant 1OT2OD032581-01 (C.V.E. and N.R.F.), and the VA Cooperative Studies Program (N.R.F.).

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of VA or the US government.

Authorship

Contribution: A.L., K.N., N.R.F., and C.V.E. designed the study and interpreted the results; K.N. performed the research and analyzed the data; A.L. wrote the manuscript; and all authors edited the manuscript.

Footnotes

A.L. and K.N. are joint first authors.

N.R.F. and C.V.E. are joint senior authors.

Data are available from the corresponding author, Alexander Lazzaro ( alexander.lazzaro@bmc.org ), on request.

The full-text version of this article contains a data supplement.

Supplementary Material

Supplemental Tables and Figures

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