Comparison of Healthcare Utilization in Patients Undergoing CAR T-Cell Therapy for Multiple Myeloma and Lymphoma
Authors: Aldecoa Kim Abbegail Tan , Loh Kah Poh , Lee Yi , Soror Noha , Arsene Camelia , Krishnamoorthy Geetha , Mohan Meera 
Published: December 2024

INTRODUCTION:
Chimeric antigen receptor (CAR) T-cell therapy offers promising clinical benefits for patients with hematologic malignancies. Currently, various CAR T-cell products are FDA-approved for use including relapsed/refractory multiple myeloma (MM) and lymphomas. Administering CAR-T cell therapy necessitates inpatient hospitalization at a specialized center for an extended duration, further adding to the significant strain on healthcare resources in addition to the cost of therapeutics. The current study investigates the inpatient healthcare utilization patterns of CAR T-cell therapy for MM and lymphoma.
METHODS:
We utilized data from the 2021 National Inpatient Sample Database. Adult patients (Age≥18) who underwent CAR T-cell therapy for MM and lymphoma (Diffuse Large B-cell, mantle cell, and follicular lymphomas) were included.
Chi-square or Fischer's exact tests were applied to compare categorical variables, while t-tests were used to compare differences between groups for continuous variables. Univariate screening followed by multivariable linear regression analysis was conducted to identify factors associated with length of stay (LOS) and hospital costs. Model was adjusted for sociodemographic factors (age, sex, race, insurance), Charlson comorbidity index (CCI), associated complications [cytokine release syndrome (CRS), Immune effector cell-associated neurotoxicity syndrome (ICANS), vasopressor use, need for invasive mechanical ventilation (IMV), receipt of blood transfusion], type of admission, and hospital characteristics (hospital bed size, hospital region). Factors were considered based on available literature. Given that MM and lymphomas are very different clinical entities, we conducted subgroup analyses in those with MM and lymphomas. Stata 16.0 was used, with p-value&lt;0.05 indicating statistical significance.
RESULTS:
A total of 1,480 weighted patients who underwent CAR T-cell therapy were identified. Of these, 75.3% had lymphoma and 24.7% had MM. Demographic characteristics were as follows: mean age 63.0 years, 62.5% male, 77.3% White, 99.7% treated in teaching hospitals, and 48.6% had private insurance. CRS developed in 63.0% of patients with MM and 62.3% of patients with lymphoma.
Patients with MM had a shorter LOS with a mean of 14.1 days (vs. lymphoma: mean 17.8 days, p=0.0121) and a lower total hospital mean costs of $219,638 (vs. lymphoma: mean $322,543, p=0.007). These differences remained consistent in multivariable analyses after adjusting for age, sex, and CCI. LOS was positively correlated with total costs (r=0.70, p=&lt;0.001).
For patients with lymphoma, increased LOS was associated with age≥65, need for IMV, presence of CRS, Medicaid and private insurance (vs. Medicare), and a CCI ≥3. Shorter LOS was observed among those admitted electively and in Southern hospital region. Higher hospital costs were associated with the need for IMV, presence of CRS, and medium/large hospital bed size.
For patients with MM, a longer LOS was observed among the Asian population and those with Medicaid (vs. Medicare), while a shorter LOS was noted among the Hispanic population and those treated in Southern hospital region. Higher hospital costs were found among Asian population and with vasopressor use.
Conclusion:
Different healthcare utilization patterns were observed with various indications for CAR T-cell therapy. Patients with lymphoma undergoing CAR T-cell therapy have longer LOS and higher hospital costs compared to MM. Immediate complications, sociodemographic, and hospital-level factors, such as age, race, insurance, hospital region, and Charlson index, were found to influence this healthcare utilization. Further research is warranted for tailored strategies to optimize care for different patient populations undergoing CAR T-cell therapy.