Purpose: Diffuse large B-cell lymphoma (DLBCL) is an aggressive lymphoma often refractory to currently available treatments (immuno-chemotherapy/autologous-stem-cell-transplantation-ASCT). Recently, new cell therapies have been approved for patients failing two conventional treatments, CAR-T (Chimeric-Antigen-Receptor-T-cell), committing payers in planning and implementing their use. We aim to define, using Real World Data (RWD), a reproducible procedure that allows identification of CAR-T target population for DLBCL.
Methods: Through the linking of electronic healthcare datasets (EHD), we identified patients with non-Hodgkin's Lymphoma (NHL), resident in Lazio region (2010-2015), aged ≥20 years. DLBCL patients were followed using pathological anatomy (PA) reports, up to 3 years. To be defined as relapsed after two treatment lines, patients must have had new chemotherapy and/or NHL hospitalization after ASCT or at the end of the second chemotherapy. The incident rate of second relapse (R2-rate) was extended to the population without PA reports.
Result: NHL incident patients were 7384, 68% presented a PA report and, 29% of these had DLBCL codes. Patients who relapsed after two treatment lines were 47 (39%) in the subgroup of patients who received ASCT and 138 (41%) in that with second chemotherapy treatment. Patients in the two subgroups were very different in terms of age and comorbidity. The annual incident number of DLBCL was estimated to be 329 which multiplied by R2-rate (13.7%) gives 45 patients per year eligible for CAR-T.
Discussion: This study shows how RWD allows the identification of a target population with new advanced therapies. This approach is rigorous, transparent and verifiable over time.
Keywords: CAR-T; DLBCL; algorithm; pharmacoepidemiology; real world data; target population; targeted drug development.
© 2020 John Wiley & Sons Ltd.