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3423 Association between Spending in Oncology Care Model-Defined Episodes and Overall Survival in Multiple Myeloma Using 2012–2017 Medicare Data

Program: Oral and Poster Abstracts
Session: 902. Health Services Research—Malignant Conditions (Lymphoid Disease): Poster III
Hematology Disease Topics & Pathways:
multiple myeloma, Adult, Diseases, Plasma Cell Disorders, Lymphoid Malignancies, Study Population
Monday, December 7, 2020, 7:00 AM-3:30 PM

Ruben Niesvizky, MD1, Zoe Clancy2*, Ronda Copher, PhD2*, Ryan B. Thomas, MD2, Cynthia Z. Qi, MBA3*, Zheng-Yi Zhou3*, Miriam L. Zichlin4*, Sarah H. Koenigsberg3* and James E. Signorovitch3*

1NewYork-Presbyterian/Weill Cornell Medical Center, New York, NY
2Bristol Myers Squibb, Princeton, NJ
3Analysis Group Inc., Boston, MA
4Analysis Group, Inc., Boston, MA

Introduction: The Oncology Care Model (OCM) is an innovative payment model introduced by the Centers for Medicare & Medicaid Services in 2016, which aims to improve quality and reduce the cost of cancer care. Under this framework, practices are incentivized to reduce spending in chemotherapy-centered episodes. Previous studies using data from pre-OCM periods (i.e. before July 1, 2016) suggested that reducing OCM episode costs, particularly disease-specific drug costs, may adversely affect overall survival (OS) in patients with multiple myeloma (MM). Using more recent data that covers 1.5 years after OCM implementation, the current study aimed to evaluate trends in OCM-defined episode costs and OS over time. Additionally, the association between OCM-defined MM episode costs and OS in MM, as well as changes in the association between the pre- and post-OCM periods, were evaluated.

Methods: Patients with newly diagnosed MM (NDMM) and ≥ 1 qualifying OCM-defined MM episode between 2012 and 2017 were selected from the 100% Medicare data. OCM episodes were defined as the 6-month period following a triggering MM treatment claim. Each episode was linked to a practice and classified based on participation in the OCM and occurrence of pre- versus post-OCM implementation. Regression models were developed, based on the OCM algorithm, to adjust for case severity mix at the practice level. The models evaluated the impact of patient and episode characteristics on total episode costs, and episode subcomponents (e.g. MM-specific drugs, other medical treatment). Based on the regression outputs, standardized costs were calculated for each practice, adjusting for differences in patient and episode characteristics. All costs were inflated to 2017 US dollars (USD).

From initial MM diagnosis, mean unadjusted episode costs, mean standardized episode costs, and 1-year OS rate were described over time by the year of first episode initiation. Two Cox proportional hazards models were constructed for OS with adjustment for key patient covariates (i.e. age, gender, comorbidity index, race, number of OCM episodes, and disability entitlement). Model 1 evaluated the association between OS and standardized total episode costs and the proportion of episode costs attributed to MM-specific drugs. Model 2 evaluated the effect on OS of the interaction between the time period (i.e. pre-OCM vs post-OCM) and total standardized episode costs. The analyses were repeated for key patient subgroups stratified by comorbidity status (i.e. low Charlson Comorbidity Index [CCI] vs high CCI) and practice type (i.e. OCM vs non-OCM).

Results: A total of 17,363 patients with NDMM (51.0% male) were included in the analysis. Mean age at diagnosis was 74.8 years (30.0–102.0). Patient characteristics were comparable between pre-OCM and post-OCM periods. There were a total of 41,972 OCM episodes during the mean 2.2 years (standard deviation [SD] 1.4) of follow-up. Average (SD) MM-drug costs, other medical and drug costs, and total costs per OCM episode were USD 51,482 (USD 31,752), USD 22,625 (USD 28,452), and USD 74,107 (USD 38,606), respectively. From 2012 to 2017, average total episode costs, MM-drug costs, and survival all increased (Table). Model 1 indicated that a USD 10,000 increase in standardized total costs was associated with a 27.5% lower risk of death (hazard ratio [HR] 0.725; P < 0.05) and that MM-drug costs were the primary driver for the improved OS. The hazard of death decreased by 21.3% (HR 0.787; P < 0.05) for every 10% increase in the proportion of costs attributed to MM-drug costs versus other medical costs. Model 2 showed the association was similar during pre-OCM and post-OCM periods. Consistent results were observed between subgroups of high versus low CCI and OCM versus non-OCM practices.

Conclusions: The analysis identified a positive correlation between average spending within OCM-defined episodes and OS among patients with NDMM, demonstrating observable clinical value for patients. This correlation remained consistent across pre-OCM and post-OCM periods. A closer evaluation of the cost subcomponents suggested that MM-specific drug costs are a primary driver for the observed OS benefit. Patient benefit from the innovative, albeit higher cost, therapies is noteworthy given the improved survival of MM. Careful evaluation is warranted for healthcare providers when attempting to reduce spending in response to new payment models.

Disclosures: Niesvizky: BMS: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Clancy: BMS: Current Employment, Current equity holder in publicly-traded company. Copher: Bristol Myers Squibb: Current Employment. Thomas: BMS: Current Employment. Qi: BMS: Other: Employee of Analysis Group Inc., which received consulting fees; Astellas Pharma, Inc.: Research Funding. Zhou: BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Zichlin: BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Koenigsberg: BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Signorovitch: BMS: Other: Employee of Analysis Group Inc., which received consulting fees.

*signifies non-member of ASH