Transplant Eligibility in DLBCL: A Real-World Analysis of German Hospital Billing Data
Author(s)
Ann-Cathrine Froitzheim, MSc1, Tabea Poos, MSc2, Melina Sophie Kurte, MA2, Udo Holtick, Dr3, Florian Kron, PhD2.
1Senior Manager Value & Access, VITIS Healthcare Group, Köln, Germany, 2VITIS Healthcare Group, Cologne, Germany, 3University Hospital Cologne, Cologne, Germany.
1Senior Manager Value & Access, VITIS Healthcare Group, Köln, Germany, 2VITIS Healthcare Group, Cologne, Germany, 3University Hospital Cologne, Cologne, Germany.
OBJECTIVES: According to treatment guidelines, second-line (2L) therapy for diffuse large B-cell lymphoma (DLBCL), treatment strategies depend on transplant eligibility. While transplant-eligible (TE) patients receive salvage immunochemotherapy (e.g., R-ICE, R-DHAP, R-GDP) followed by high-dose chemotherapy + SCT or CAR-T cell therapy, transplant-ineligible (TIE) patients are generally treated with conventional immunochemotherapy or targeted therapies. Although around 50% of patients are assumed to be TE based on earlier studies, real-world data for Germany are lacking. This study aimed to estimate the TE/TIE distribution using national inpatient data.
METHODS: Billing data provided by the Institute for the Hospital Remuneration System (InEK) covering all German hospitals for 2024 were analyzed. First, all cases with main diagnosis “C83.3” (DLBCL) formed the base population. Second, the base population size was reduced by the proportion of cases cured after 1L therapy using literature-based range in cure rates (60-70%), forming the uncured 2L population. Third, TE patients were identified via OPS-codes corresponding to R-ICE (8-544), R-DHAP (8-543.22), R-GDP (8-542.12/11), other immunotherapy (8-547.0). Lastly, respective TE cases were summed up and divided by the 2L population to calculate the TE proportion.
RESULTS: The base population consisted of 32,851 cases. The 2L population was estimated to range between 9,855 (minimum) and 13,140 (maximum). Across all coding combinations, 7,523 cases met criteria for TE classification. This corresponds to a TE rate of 57% in the minimum scenario and 76% in the maximum scenario within the 2L population.
CONCLUSIONS: Using inpatient billing data, this study is the first to assess the proportion of TE versus TIE in 2L DLBCL patients in Germany, demonstrating that more than 50% are TE. Since current guideline changes now differentiate between CAR-T-eligible and -ineligible in the 2L setting, future research should investigate whether and to what extent identified TIE patients are eligible for CAR-T treatment.
METHODS: Billing data provided by the Institute for the Hospital Remuneration System (InEK) covering all German hospitals for 2024 were analyzed. First, all cases with main diagnosis “C83.3” (DLBCL) formed the base population. Second, the base population size was reduced by the proportion of cases cured after 1L therapy using literature-based range in cure rates (60-70%), forming the uncured 2L population. Third, TE patients were identified via OPS-codes corresponding to R-ICE (8-544), R-DHAP (8-543.22), R-GDP (8-542.12/11), other immunotherapy (8-547.0). Lastly, respective TE cases were summed up and divided by the 2L population to calculate the TE proportion.
RESULTS: The base population consisted of 32,851 cases. The 2L population was estimated to range between 9,855 (minimum) and 13,140 (maximum). Across all coding combinations, 7,523 cases met criteria for TE classification. This corresponds to a TE rate of 57% in the minimum scenario and 76% in the maximum scenario within the 2L population.
CONCLUSIONS: Using inpatient billing data, this study is the first to assess the proportion of TE versus TIE in 2L DLBCL patients in Germany, demonstrating that more than 50% are TE. Since current guideline changes now differentiate between CAR-T-eligible and -ineligible in the 2L setting, future research should investigate whether and to what extent identified TIE patients are eligible for CAR-T treatment.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
RWD183
Topic
Epidemiology & Public Health, Methodological & Statistical Research, Real World Data & Information Systems
Topic Subcategory
Health & Insurance Records Systems
Disease
Oncology