Follicular lymphoma or diffuse large B-cell lymphoma: a population based analysis of epidemiological and health economic aspects in Germany

Follicular lymphoma or diffuse large B-cell lymphoma: a population based analysis of epidemiological and health economic aspects in Germany | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Follicular lymphoma or diffuse large B-cell lymphoma: a population based analysis of epidemiological and health economic aspects in Germany Karin Berger, Bernhard Moertl, Michael von Bergwelt-Baildon, Dominik Obermueller, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4830530/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Sep, 2025 Read the published version in Annals of Hematology → Version 1 posted 12 You are reading this latest preprint version Abstract Epidemiological data and information on resource consumption, costs and clinical outcomes of the care of patients (pts) with follicular lymphoma (FL) or diffuse large b-cell lymphoma (DLBCL) in Germany are rare. Objective of this study was to generate information filling these evidence gaps. This retrospective cohort study (2015–2020) is based on anonymized, longitudinal health claims data. Subgroup analyses on pts with stem-cell transplant (SCT) were performed. About n = 950 annual prevalent FL-pts and n = 1.360 DLBCL-pts were analysed per year. Mean age of FL-pts was 67 years (SD ± 13), 50,7%-females. In the DLBCL-cohort mean age was 68,6 years (SD ± 13,6), 44,4%-females. The share of “agranulocytosis and neutropenia” as an example of the analyzed side effects was: FL 7,2% and DLBCL 16%. Of the FL-pts 64% had min. one hospital admission, with mean 2 admissions (SD ± 2,3) and a mean duration of 21 days (SD ± 44,7) per year. In the DLBCL-cohort 78% had a hospitalization with 2,9 admissions (SD ± 3,1) and 29 inpatient days (SD ± 47,5). Mean annual costs: FL €15.258 per-patient (SD ± 20.367) and DLBCL €23.455 (SD ± 32.892) per-patient. Mean 12-month costs after autologous-SCT were: FL €46.270 (SD ± 21.936) and DLBCL €56.558 (SD ± 45.926); for allogeneic-SCT (only DLBCL-cohort): €161.662 (SD ± 75.266). This study demonstrate a high burden associated with malignant lymphomas. A considerable number or side effects is documented, indicating a difference between the entities. Length of inpatient stay is stressful for patients and associated with significant costs. Total spending for r/r-pts who require intensive treatments like SCTs are significant. Future efforts including linkage to additional data sources with complementary clinical-information are needed. Figures Figure 1 INTRODUCTION Hematological malignancies include a heterogeneous group of lymphomas, multiple myeloma, and leukemia [ 1 ]. They differ according to cell type, clinical and molecular characteristics as well as prognosis and treatment options [ 2 , 3 ]. Follicular- (FL) and diffuse large B-cell lymphomas (DLBCL) are the most common subtypes of malignant lymphomas (ML) [ 4 , 5 ]. Both entities arise from B-cells and occur with varying aggressiveness and heterogeneity. While in DLBCL, the treatment aim is mostly curative the relative five-year survival of FL patients is also close to 70% due to the indolent course of the disease [ 6 ]. On the other hand, watch and wait is an established approach in FL whereas DLBCL progress rapidly without treatment. Cure rates of approx. 60–80% for DLBCL patients have been published for DLBCL first-line therapy [ 7 , 8 ]. In contrast, there is no curative setting for FL patients, the overall treatment-aim is long-term remission [ 2 , 9 ]. International publications show significantly reduced survival rates for patients with relapsed or refractory disease (r/r) in both entities [ 7 , 10 – 14 ]. Therefore, a wide variety of treatment concepts are applied to treat these patients, depending on age, general condition, response rate, timing, and other clinical indicators [ 15 ]. A various number of chemo-regimes, immunotherapies, radiotherapies, stem cell transplantations (SCT) and also targeted options are recommended by the national FL and DLBCL guidelines [ 16 , 17 ]. In the last years, innovative therapies for r/r patients have been approved by the EMA e.g. bispecific antibodies or CAR-T-cell therapies [ 11 , 18 , 19 ]. Beside the clinical aspects and benefits, the economic consequences of these novel treatment options on third party payers are not entirely known. Recently, a first Budget-Impact analysis based on inpatient data, estimates a €39M to €166M impact on payers budget of implementing CAR-T-cell therapy in Germany [ 20 ]. Especially, both the standard of care and innovative treatments require comprehensive inpatient & outpatient data on e.g. patient characteristics, treatment patterns, resource use, costs and outcomes for all comparators. Comprehensive trans-sectoral longitudinal information on FL- and DLBCL-patients treatment journeys, treatment patterns and resource consumption are still lacking. This evidence gap is challenging for value demonstration, decision-making and development of future care models for responsible stakeholders. This analysis was conducted to partially address this evidence gap, by generating additional information on FL and DLBCL-care in Germany. MATERIALS AND METHODS STUDY DESIGN This analysis was a retrospective cohort study based on the InGef research database. The observation period covers the years from 2014 to 2020. Depending on the respective research questions, individual analyses were conducted in the design of a cross-sectional analysis (e.g. incidence and prevalence) and individual analyses in the design of a longitudinal analysis (e.g. the follow-up of stem cell transplanted patients). In the cross-sectional part, prevalent patients were analysed per year. For the determination of the incidence, a prior diagnosis-free pre-observation period of one-year was taken into account. Consequently, the year 2014 was only used as the pre-observation period for the analysis year 2015. Further variables such as demographic and clinical patient characteristics, diagnostic methods, medical therapy, procedures, adverse events, resource utilization, costs, and mortality were considered separately for prevalent patients of both entities. DATA SOURCE The InGef database contains anonymized, longitudinal statutory health insurance (SHI) data of about 9 million individuals who are insured by one of more than 50 German health insurance companies included in this database. For this analysis, an annual representative sample of about 3.25 million insured adult persons was used, which is representative to the age and sex distribution of the German population. [ 21 ] The total observation period available is limited to six years. The InGef research database contains socio-demographic information such as gender, age and region of residence. In addition, detailed information on inpatient and outpatient care are obtained from the database. The inpatient data include the date of admission, the date of discharge, diagnostic and therapeutic information (OPS-Level) with exact dates and diagnoses. The outpatient data also contain diagnostic and therapeutic information. Outpatient prescription data contain information (type and quantities) on the ATC-Level. All diagnoses are coded according to the German Modification of the International Classification of Diseases, 10th Revision (ICD-10-GM). Since all insured individual data in the InGef database are made anonymized and are no longer social data in the sense of § 67 para. 2 SGB X in combination with Art. 4 No. 1 DSGVO, their use for scientific research purposes is compliant with German laws, accordingly no further permission from an ethics committee is required. The analysis follows the recommendations of Good Epidemiological Practice (GEP) and Good Practice Secondary Data Analysis (GPS). Case definition Follicular lymphoma (ICD-10: C82.0 – C82.3) and DLBCL (ICD-10: C83.3) diagnosis as an inpatient main or secondary diagnosis and/or two outpatient diagnoses in different quarters of the respective analysis year (M2Q criterion). For incidence patients, the index date was defined according to the setting of the initial diagnosis: For inpatient main or secondary diagnoses, the date of hospital discharge; for confirmed outpatient diagnoses (with corresponding fulfilment of the M2Q criterion), the first physician contact with the diagnosing physician representing the index date. If an initial outpatient diagnosis was followed by an inpatient diagnosis in another quarter, the outpatient diagnosis defines the index date. If there was an outpatient and an inpatient diagnosis in the same quarter, the date of the inpatient diagnosis was defined as the index date. Study variables Patient characteristics were assessed according to the individual index year during the observation period. Sociodemographic variables included age and gender. The Elixhauser- and Charlson Comorbidity Scores were used to describe the general comorbidity burden. Entity-relevant ICD-codings (e.g. Lymphoma) were taken into account for the comorbidity-scoring. In addition, the specific comorbidity burden was assessed through another data-driven approach, identifying and describing the five most common comorbidities. Medical variables and measures: diagnostics (ICD-10-Code), outpatient medication (ATC-L-Code), relevant inpatient and outpatient procedures (OPS-code). Side effects (ICD-10-Code; e.g. sepsis, thrombosis, etc.). Economic variables; Inpatient stay, costs. Outcomes in terms of mortality. Subgroup analysis A and B on patients with stem-cell-transplant Identification via OPS-coding (auto-SCT: 5-411.0, 8-805.0; allo-SCT: 5-411.2–5-411.5, 8-805.2-8-805.5). The subgroups of patients with respective therapy were analysed for both entities in 6, 12 or 24 months after the treatment-coding. The following measures were assessed within the available follow-up period: 1.) Patient characteristics and comorbidities at the time of therapy-coding. 2.) Number and duration of hospitalisations in the 6, 12 and 24 months after therapy. 3) Costs of transplant in the 6, 12 or 24 months after SCT-coding. 4) Mortality incl. Kaplan-Meier curve in the 6, 12 or 24 months after therapy. RESULTS Prevalence and Incidence Out of total 8.8 million individuals in the database, an annual subsample from the years 2015–2020 of ~ 3.25 million individuals were included in this analysis, being representative for the German population [ 21 ]. With, a mean of 956 prevalent patients with follicular lymphoma and 1.362 patients with diffuse large B-cell lymphoma, corresponding to a 1-year FL prevalence rate of 29,4/ 100.000 [median 30, range 25,6–31,9] and 41,9 / 100.000 [median 42,6, range 36,8–44,6] for the DLBCL-cohort, respectively. The mean incidence rate was 7,3 per 100.000 [median 7,2, range 6,7–8,1] for FL and 14,4 [median 14,3, range 13,1–15,3] for DLBCL-pts. Patient characteristics and comorbidities Details are shown in Table 1 . The annual distribution of the TOP-5 comorbidities did not change during the observation period (Supp_Table 1). Table 1 Patient characteristics of prevalent FL/ DLBCL cohort FL DLBCL Number of individuals per year; n absolute (n per 100.000) n per 100.000 n per 100.000 2015 837 26 1205 37 2016 901 27 1324 40 2017 927 28 1349 41 2018 1012 31 1414 44 2019 1030 32 1445 44 2020 1028 32 1437 45 Age in years Mean (SD) 67 13 69 14 Median (range) 69 18–98 71 18–99 Age group in respective year; n absolute (%) 18-39y 27 3 53 4 40-59y 210 22 262 19 60-80y 596 62 796 58 > 80y 123 13 252 18 Sex; n absolute (%) Male 471 49 758 56 Female 485 51 605 44 Comorbidities; mean score (standard deviation) Charlson 4,1 +/- 2,4 4,8 +/- 2,7 Elixhauser 5,2 +/- 3,0 6,1 +/- 3,3 [Description Table 1 ] For comorbidity scoring all variables (incl. entity-specific) were included. Diagnostic, treatment and procedures Diagnostic In the inpatient-setting 33% of the FL-patients received a minimum of one CT-diagnostic, 9% an MRT and 7% an ultrasound per year. In the outpatient setting: 42% of the FL-patients received minimum one CT-diagnostic, 19% a MRT and 86% an ultrasound. Similarly, in the inpatient-setting 46% of the DLBCL-patients received a minimum of one CT-diagnostic, 18% an MRT and 9% an ultrasound. In the outpatient setting, 36% of the DLBCL-patients received a minimum of one CT-diagnostic, 16% a MRT and 74% an ultrasound. Treatment For outpatient FL medication on ATC-level the annual distribution was comparable between 2015 and 2017, including Rituximab, Bendamustin, Vincristin, Doxorubicin and Cyclophosphamide with descending shares. The percentage of Obinutuzumab increased between 2018 and 2020. For DLBCL patients, the distribution was comparable including Rituximab, Vincristin, Cyclophosphamid, Doxorubicin and Filgrastim/ Pegfilgrastim with descending shares. The percentage of Rituximab decreased slightly over the years. For the inpatient medication on OPS-Level, the annual distribution does not change between 2015 and 2020 (Table 2 ). For more detailed information, see Supp_Tables 2 & 3 in the supplements. Table 2 Overview mean inpatient treatment procedures between 2015 and 2020 per year FL DLBCL n % N % Total patients 956 100 1362 100 Non complex chemotherapy 85 9 265 19 Moderately complex and intensive block chemotherapy 29 3 159 12 High complex and intensive block chemotherapy 8 1 35 3 Other immunotherapy 89 9 310 23 Highly active antiretroviral therapy 0 0 7 1 Radiotherapy 15 2 50 4 CAR-T-cells 0 0 < 5 < 5 Stem cell transplant autogeneous 7 1 25 2 Stem cell transplant allogenic < 5 < 5 7 1 Side effects: For the prevalent FL-patients following top-5 side effects (ICD-Codings) were documented: Reaction to severe stress and anxiety (12,8%), agranulocytosis and neutropenia (7,2%), immunodeficiency with predominant antibody deficiency (5,3%), thrombosis, phlebitis and thrombophlebitis (4,9%), and acute renal failure (4,3%). For the DLBCL-pts: agranulocytosis and neutropenia (16%), Reaction to severe stress and anxiety (11,7%), acute renal failure (8,9%), Aplastic anemia due to cytostatic therapy (7,8%), other secondary thrombocytopenia not designated as transfusion refractory (7,2%). Healthcare resource utilization (HCRU) and costs Mean HCRU and associated costs are presented in the Tables 3 and 4 . Table 3 Physician visit and hospitalization, mean annual numbers per patient total pat. n Pat. n mean SD min Median max FL Number of physician consultations (n, ppt) 956 955 42 27 1 37 290 Number of hospitalizations 956 614 2 2 0 1 15 Inpatient stay (days) 956 614 21 45 0 3 323 DLBCL Number of physician consultations 1362 1355 42 30 0 36 251 Number of hospitalizations 1362 1058 3 3 0 2 34 Inpatient stay (days) 1362 1058 29 48 0 9 335 In the FL study cohort, 64% of the patients were observed with at least one hospitalization. A rate of 78% were documented for the DLBCL patients. Associated mean inpatient costs per patient amounted to € 6.740 per year for a FL patient and € 14.188 for the DLBCL-cohort. In the outpatient setting mean annual costs per FL patient of € 2.012 and €1.906 for DLBCL were documented. The total mean costs were €15.258 per patient (ppt) in the FL-cohort (SD 20.367) and €23.455 (SD 32.892) among patients with DLBCL. More details are shown in Table 4 . Table 4 Annual total costs for FL and DLBCL patients in € FL n mean median min max 2015 837 15.283 6.971 189 202.453 2016 907 15.743 7.251 101 136.487 2017 926 15.683 6.987 - 255.353 2018 1012 14.938 6.823 287 239.043 2019 1030 14.846 7.255 147 166.633 2020 1028 15.054 7.041 187 152.636 DLBCL 2015 1204 22.309 11.212 - 244.870 2016 1324 25.046 13.065 116 337.495 2017 1346 23.906 11.110 - 287.162 2018 1413 23.130 10.857 - 602.243 2019 1445 22.235 10.750 104 572.105 2020 1434 24.101 10.964 - 591.068 The distribution of costs for the mean costs per FL-patient was: 13% outpatient care, 44 inpatient care, 39% drugs and 3% remedies and aids. For the DLBCL-patients: 8% outpatient care, 60% inpatient care, 29% drugs and 3% remedies and aids. In total, mean costs for the FL-cohort (mean n = 956pts) of €14.566.390 are documented per year, for the DLBCL patients (mean n = 1.361pts) €31.960.242, respectively. Outcomes Documented mortality The annual mortality rate for the prevalent FL-patients was mean n = 47,8 patients (5%). In the DLBCL cohort n = 182 patients died (13,4%). Subgroup analysis A: FL Patients with stem cell transplant In the FL-cohort n = 38 patients (2,01%) received a stem cell transplant per year. An auto-SCT received 27 patients (1,27%) and 8 patients received allo-SCT (0,42%). Because of data protection (n > 5 patients) no analysis was feasible for patients with auto- and allo-SCT. Characteristics and comorbidities For patients with auto-SCT the mean age was 59,5y (SD: 11,68y; median: 63y; range 34-74y). The gender distribution for this cohort was n = 7 (18,4%) female and n = 20 (52,6%) male. Because of data protection, no detailed information for allo-patients can be shown. In terms of comorbidities, the mean Charlson Comorbidity Index (CCI) for autologous transplanted patients was: 4,11 (SD: 2,33; median: 3,0; range 2–5). The Elixhauser Comorbidity Score for auto-SCT patients was: 6,78 (SD: 2,65; median: 7; range 3–14). Hospitalization during 12M after transplant The mean number of inpatient stays for the auto-SCT cohort was 4,05 (SD: 2,52; median: 3,5; range 1–10) with a mean number of n = 67 inpatient days (SD: 69,86; median: 35; range 23–258). Total costs transplant and 12M after The mean total costs for patients with auto-SCT were €46.270 (SD: 21.936; median: 41.882; range 12.596–106.842). Due to 4% for outpatient care, 79% for inpatient care, 16% for medicines and 1% for remedies and aids. Because of limited FL patients, overall survival probability cannot be shown. Subgroup analysis C: DLBCL Patients with stem cell transplant Over the years 2015 until 2019 n = 146 prevalent DLBCL patients (4,67%) received a stem cell transplant. A number of 117 patients received an auto-SCT (3,75%) and 23 patients an allo-SCT (0,74%). Because of data protection (n > 5 patients) no results were shown for patients with auto- and allo-SCT together. Characteristics and comorbidities For patients with auto-SCT the mean age at SCT-procedure was 61y (SD: 10,2y; median: 63y; range 33-77y). The sex distribution for the auto-SCT cohort was n = 42 (36%) female and n = 75 (64%) male. For the n = 23 allo-SCT patients a mean age of 52,7y (SD: 12,06y; median: 55y; range 24-67y) was documented. The sex distribution was: n = 6 pts (26%) were female and n = 17 (74%) male. In terms of comorbidities, the mean CCI-score for autologous transplanted patients were: 6,09 (SD: 2,99; median: 6,0; range 2–13). In the allo-SCT cohort a score of 4,22 (SD: 1,91; median: 4,0; range 2–9) were documented. The Elixhauser comorbidity score for auto-SCT patients was: 8,38 (SD: 2,9; median: 8; range 3–17) and 6,17 (SD: 2,46; median: 6; range 4–14) for the allo-SCT cohort. Hospitalization in 12M after transplant The mean number of inpatient stays in the 12M after transplant, was n = 3,9 per auto-SCT patient (SD: 2,29; median: 4; range 1–10) with a mean number of inpatient days of 79d (SD: 75,6; median: 44; range 1–318). The mean number of inpatient stays for patients with allo-SCT were 6,1 (SD: 3,38; median: 5,5; range 1–12) with a mean 183 inpatient days (SD: 116,9; median: 144; range 40–345). Total costs during and 12M after transplant The total costs in the 12M during and after transplant for all patients with auto-SCT (n = 95) were €5.373.020 and for the allo-SCT cohort n = 14 €2.263.269. The mean total costs were €56.558 per auto-SCT patient (SD: 45.926; median: 42.064; range 12.596–338.009). Due to 3% for outpatient care, 82% for inpatient care, 11% for medicines and 4% for remedies and aids. The mean total costs for allo-SCT patients were €161.662 ppt (SD: 75.266; median: 148.105; range 86.128–294.691) in the 12 months after coding. Due to 2% for outpatient care, 79% for inpatient care, 18% for medicines and 0% for remedies and aids. Outcomes in terms of documented mortality after transplant The mortality rate of the auto-SCT cohort (n = 95 pts) was 5,23% (n = 5pts) in the 12M follow-up after transplant. In the allo-SCT cohort n = < 5 patients had a documented death. For more details, see Fig. 1 . DISCUSSION The findings of this study demonstrate the high burden of patients with malignant lymphomas (FL, DLBCL). Nearly one-thousand prevalent patients of both entities per year, the maximal documented annual inpatient stay of 335 days, given intensive therapy options like stem cell transplants, and maximal total costs of €620.000€ per patient in one year. These results indicating a challenging setting for all stakeholders in the care of these patients. Annually €87.4m for approx. n = 956 FL-patients and €191.8m for n = 1.361 DLBCL-patients were spent by third party payers between the years 2015–2020. This study generates valuable insights on FL & DLBCL-care in Germany. To our knowledge, this is likely to be one of the first comprehensive analyses of German claims data for malignant lymphomas such as FL and DLBCL. On European level, annual prevalence of 37/100.000 for FL [ICD:C82.0, C82.1, C82.2, C82.3, C82.4, C82.5, C82.6, C82.7, C82.9] and 43/100.000 for DLBCL-patients [ICD-10 C83.3] were recently published. [ 22 ] In our German study cohort, the FL prevalence extrapolated for Germany was slightly lower (30,3/100.000). The extrapolated prevalence for the DLBCL cohort was 42,4/100.000 what is comparable to aforementioned European data. One reason for these lower FL-rates can be the methodologic difference by the including ICD-codes. Also, there is a trend of rising prevalence over the observation period. Between 2015 and 2020 the prevalence increased by 23% for FL and 22% for DLBCL. This trend of increasing patient numbers is comparable with German data, provided by the “Krebs in Deutschland für 2017/2018”-report published by the governmental Robert-Koch institute [ 4 ]. In terms of the age-distribution, the majority of the patients had a documented age above 60 years. Up to 75% (FL) and 76% (DLBCL) of the respective patients were in this age group. This observation must be interpreted in a broader view. The number of incident NHL-patients with an age of more than 80years are highest. Discussed in context of a higher mortality-risk for patients > 75y, the low numbers in the eldest age-groups in our prevalent cohort are plausible. [ 4 ] In terms of gender distribution, the InGef-cohort is almost in line with Dürig J et al. gender is balanced in the FL group (men: 49% men), in the DLBCL group a the proportion of men (57.7%)was slightly higher [ 23 ]. This observation is also in line with previous publications. [ 5 , 16 , 17 ]. No German publication on comorbidities has been found, therefore no national comparisons are possible. Yang X. et al showed in a US claims data analysis on n = 2.500 DLBCL patients a mean CCI-Index number for prevalent patients of 2.3 (SD ± 2.4) [ 24 ]. In our study cohort CCI-score for the prevalent DLBCL cohort was 4,8 (SD ± 2.7). The CCI-score is clearly higher, because of e.g. different methodological approaches (entity not excluded). For FL-patients no public available information has been found. Beside the scores, the individual underlying variables indicate a huge patient burden (Supplement Table 1 ). For example, about 30% of all prevalent patients in both entities had a depression in our study cohort. In a previous publication for Germany a depression-rate for NHL-pts of 22,3% within 10-years after index date was communicated [ 25 ]. Also the documented side effects indicate the high burden for these patients. Here, a clear difference between the two entities was documented. The shares of agranulocytosis and neutropenia, reaction to severe stress and anxiety, and acute renal failure are more than doubled for DLBCL patients. This observation as well as the more intense treatment regimens might be responsible for the documented longer inpatient-days in the DLBCL-cohort. In addition, the mean numbers of diagnostic procedures shown underscore the high resource use, as all patients underwent at least one procedure. However, we assume that this number is underestimated and that patients have significantly more procedures. Other results on treatment-details, are generally consistent with the expectations of clinical experts and previous international research. Thus, a substantial percentage of chemotherapy-based treatment approaches for the DLBCL cohort (e.g. R-CHOP) is documented. [ 26 ] In contrast, in the FL-cohort 25% of the prevalent patients received Rituximab in the ambulant setting only. In addition, the increasing usage of innovative treatment after market-approval were documented. After its approval in September 2017, the Obinutuzumab-use for FL-patients continuously increased between 2018 and 2020 [ 27 ]. When discussing patient-burden, hospitalization-rates are an additional indicator of previously discussed aspects. The results identified a mean of 20,7 inpatient days in the FL cohort and 29,3 days for the DLBCL patients. The number of hospitalizations were n = 2,0 and n = 2,9 admissions, in respective. The maximum number of n = 323 (FL) and n = 336 (DLBCL) inpatient days indicate major expenditures of resources, and a huge burden of disease for individual patients. This observation of huge efforts for a small number of high risk patients, is consistent with a German DLBCL study [ 28 ]. In this retrospective analysis of relapsed and refractory DLBCL-patients, a mean number of 63 inpatient days (median 66; range 17–123; SD ± 36) and 5 admissions (3; 1–12; ±3) were documented in DLBCL patients with > 3 lines of therapy. However, in our study the annual costs for inpatient care contributed to highest shares of total cost for both entities and over the total observation period. A high proportion of inpatient cost was also noted in several international real-world cost analysis on DLBCL and FL. [ 29 – 31 ] A recent German claims data analysis on DLBCL-patients documented hospitalization as the main cost driver with 71%. The time-unadjusted absolute costs sum up to €59,868 (43,331), €35,870 (37,387), and €28,832 (40,540) during first-line, second-line, and third-line treatments, respectively. [ 32 ] Due to methodological differences (e.g. results classified in lines of therapy only) no detailed comparison were performed. International publications also show comparable results: In a Canadian study of DLBCL patients, the inpatient stay for second-line treatment was the largest cost driver (62%)[ 30 ]. Tsutsué et al. showed in a Japanese claims data analysis, that the majority of the overall costs as well as per-treatment line were due to inpatient costs (n = 6,821) of 47,903.08 USD (SD, 47,497.30; range, 247.43–488,296.86) [ 29 ]. A major factor in this context is the choice of therapy. One example of a treatment approach that has a huge economic impact is the stem cell transplantation. In a Canadian study, autologous SCT and hospitalization contributed the most to direct costs for DLBCL patients in more than three lines of therapy [ 33 ]. We discussed this in context of the subgroup analysis on SCTs, in the following text. In general, the mean and median numbers of all cost-data differ significantly because of the wide range of min and max numbers, with a maximal annual cost of €255.353 per patient in FL and €602.243 for the DLBCL. A small number of patients with very high total costs were documented. From 2018 to 2019/20 the maximal costs almost doubled in the DLBCL-cohort. This significant increase may be caused by a higher number of treatment-approaches with cost-intensive innovative / personalized medicines. In total, mean costs of €15.4m are documented per year in the FL-cohort, in comparison to €34.6m in the DLBCL patients. The mean annual costs in years 2017 to 2020 amounted to 0.3% (FL) and 0,7% (DLBCL) of the German statutory health insurance expenses in these years on antineoplastic agents (approximately €5 billion) [ 34 ]. In terms of outcomes, we focussed on mortality and survival. The mean annual mortality rate for the prevalent FL-patients was 5% and 13,4% for the DLBCL-cohort. Since these cohorts are a cross-section of all patients in the corresponding year, the outcome-results of the SCT subgroup were discussed only. Regarding the outcomes in the subgroup analysis on stem cell transplanted patients (Fig. 1 ), a recent Canadian study on FL-patients demonstrated also an potential long-term benefit of SCT in Canada [ 35 ]. Because of the insufficient evidence level on German data for both entities, no further comparison with national data was possible. However, when discussing on treatment and respective outcome, innovative approaches must be set into perspective e.g. CAR-T cell therapies [ 36 ]. As an addition, in the subgroup analysis, the mean 12M-total cost after autologous SCT were €46.270 for FL and €56.558 for the DLBCL patients. The mean 12M–costs for DLBCL-patients with allo-SCT were €161.662. Mayerhoff et al. reported direct costs of €230,399/patient (DLBCL/FL allogeneic) and €107,457/patient (DLBCL/FL autologous) for Germany. These reported costs are substantially higher, because they were summed up in the period of two quarters before and eight quarters after SCT. [ 37 ] Moertl et al. documented mean treatment costs per DLBCL-patient with auto-SCT of €55,468 and €131,264 for allo-SCT in the clinical setting[ 28 ]. Further research and detailed information are needed to put conventional treatments in the context of innovative treatment approaches, such as CAR T-cell therapy, which ranged between 276 086 EUR and 328 727 EUR [ 38 ]. Summarizing, in terms of rising prevalence of haematological malignancies in Germany, comprehensive care for patients can lead to high costs for health systems and places pressure on public budgets. Our results can be used as a baseline for future economic studies, in the context of innovative lymphoma therapies in Germany. Certain factors limit our findings. The analyses were based on health claims records, which are collected for billing purposes and not primary for research reasons. Therefore, all results depend on quality of coding. Because of the high level of data protection in Germany, individual case validation was not possible and no results for a patient count of n < 5 could be displayed. Because of general limitations in health claims datasets, classification of socioeconomic status, clinical details (e.g. tumor status, lines of therapy) or quality of life (QoL) criteria is impossible. Despite this lack of data, the impact of high-dose chemotherapies on the quality of life of e.g. FL patients should not be neglected [ 39 ]. Another limitation of the health claims data is the missing of comprehensive information on specific treatments. For instance, singular medicines dispensed in the inpatient setting cannot be assessed separately as these costs are usually included in the compensation schemes for diagnosis related groups (DRG). On the other hand, this also reflects a major strength of this study as it evaluates overall costs and resource utilization based on the perspective of health insurances with a high rate of data completeness. Similarly, the large and representative sample size is a huge strength. CONCLUSION In summary, in the underlying German health insurance claims database, the number of FL and DLBCL continuously increased between 2015 and 2020. The number and length of inpatient stays indicates a high burden for the patients and their families. Especially for patients with relapsed or refractory (r/r) course of disease, it can be associated with an uncertain prognosis. In addition, the costs for r/r patients who require intensive treatments such as SCTs are significant. For a more holistic view future effort should include linkage to additional data sources, e.g. prospective registries, regional cancer registry to enable comprehensive information on patient journeys, treatment patterns and outcome analysis. However, our results from health insurance claims data provide complementary information for initial discussions with payers, administrators and politician in the context of access to innovative treatments. Declarations Conflict of interest: Karin Berger is employed at the LMU Klinikum, Department Haematology / Oncology. D. Beier and D. Pawlowska-Phelan are employed by InGef-Institute for Applied Health Research Berlin GmbH, which received funding from LMU Klinikum. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethical considerations This study was approved by a Scientific Committee, consisting of clinical experts from LMU Klinikum and InGef-Insitute for Applied Health Research Berlin. Due to the non-interventional nature of this research, which used a retrospective anonymized claims dataset, no ethical approval was required for this study. The study followed all the dictates of the Declaration of Helsinki. Competing Interests Karin Berger is employed at the LMU-Clinic, Department Haematology / Oncology. D. Beier and D. Pawlowska-Phelan are employed by InGef-Institute for Applied Health Research Berlin GmbH, which received funding from LMU. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding Statement: The conduct of this study has been supported by a research grant from Novartis Pharma GmbH; InGef received research funding by LMU Klinikum. Novartis Pharma GmbH provided financial support, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript Author Contribution KB and BM contributed half/half to the development of the conceptional study design, critical interpretation of results, and writing the draft as well as the final version of the article. MD and MvB, supported as clinical experts on lymphoma and have been involved in critical interpretation of results. DB and DPP conducted the data collection and data analysis in consultation with KB and BM. All authors were involved in critically reviewing the article and gave approval of the version to be published. The manuscript was prepared according to the ICMJE Recommendations. Acknowledgement Funding Statement:The conduct of this study has been supported by a research grant from Novartis Pharma GmbH; InGef received research funding by LMU Klinikum. Novartis Pharma GmbH provided financial support, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript Data Availability The data used in this study cannot be made available in the article, the supplemental files, or in a public repository due to German data protection laws (Bundesdatenschutzgesetz). To facilitate the replication of results, anonymized data used for this study are stored on a secure drive at the InGef - Institute for Applied Health Research Berlin. Access to the raw data used in this study can only be provided to external parties under the conditions of a cooperation contract and can be accessed upon request, after written approval ( [email protected] ), if required. References Swerdlow, S.H., et al., The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood, 2016. 127 (20): p. 2375-90. Dreyling, M., et al., Newly diagnosed and relapsed follicular lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†☆. Annals of Oncology, 2021. 32 (3): p. 298-308. Tilly, H., et al., Diffuse large B-cell lymphoma (DLBCL): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Annals of Oncology, 2015. 26 : p. v116-v125. Erdmann, F.e.a., Krebs in Deutschland für 2017/2018. Robert Koch-Institut und die Gesellschaft der epidemiologischen Krebsregister in Deutschland, 2021. 13.Ausgabe . Morton, L.M., et al., Lymphoma incidence patterns by WHO subtype in the United States, 1992-2001. Blood, 2006. 107 (1): p. 265-76. Ta, R., et al., Molecular Diagnostic Review of Diffuse Large B-Cell Lymphoma and Its Tumor Microenvironment. Diagnostics (Basel), 2022. 12 (5). Gisselbrecht, C. and E. Van Den Neste, How I manage patients with relapsed/refractory diffuse large B cell lymphoma. Br J Haematol, 2018. 182 (5): p. 633-643. Feugier, P., et al., Long-term results of the R-CHOP study in the treatment of elderly patients with diffuse large B-cell lymphoma: a study by the Groupe d'Etude des Lymphomes de l'Adulte. J Clin Oncol, 2005. 23 (18): p. 4117-26. Casulo, C., Prognostic factors in follicular lymphoma: new tools to personalize risk. Hematology, 2016. 2016 (1): p. 269-276. Gisselbrecht, C., et al., Salvage regimens with autologous transplantation for relapsed large B-cell lymphoma in the rituximab era. J Clin Oncol, 2010. 28 (27): p. 4184-90. Crump, M., et al., Outcomes in refractory diffuse large B-cell lymphoma: results from the international SCHOLAR-1 study. Blood, 2017. 130 (16): p. 1800-1808. Rovira, J., et al., Prognosis of patients with diffuse large B cell lymphoma not reaching complete response or relapsing after frontline chemotherapy or immunochemotherapy. Ann Hematol, 2015. 94 (5): p. 803-12. Han, G., et al., Follicular lymphoma microenvironment characteristics associated with tumor cell mutations and MHC class II expression. Blood Cancer Discov, 2022. Schmitz, R., et al., Genetics and Pathogenesis of Diffuse Large B-Cell Lymphoma. N Engl J Med, 2018. 378 (15): p. 1396-1407. Sarkozy, C. and L.H. Sehn, Management of relapsed/refractory DLBCL. Best Pract Res Clin Haematol, 2018. 31 (3): p. 209-216. Lenz G., e.a., Diffuses großzelliges B-Zell-Lymphom , in Onkopedia Leitlinien . 2022. Buske C., e.a., Follikuläres Lymphom. Onkopedia Leitlinien, 2022. Locke, F.L., et al., Long-term safety and activity of axicabtagene ciloleucel in refractory large B-cell lymphoma (ZUMA-1): a single-arm, multicentre, phase 1–2 trial. The Lancet Oncology, 2019. 20 (1): p. 31-42. Schuster, S.J., et al., Tisagenlecleucel in Adult Relapsed or Refractory Diffuse Large B-Cell Lymphoma. N Engl J Med, 2019. 380 (1): p. 45-56. Skalt, D., et al., Budget Impact Analysis of CAR T-cell Therapy for Adult Patients With Relapsed or Refractory Diffuse Large B-cell Lymphoma in Germany. Hemasphere, 2022. 6 (7): p. e736. Ludwig, M., et al., Sampling strategy, characteristics and representativeness of the InGef research database. Public Health, 2022. 206 : p. 57-62. Prevalence and incidence of rare diseases: Bibliographic data , in Orphanet Report series . 2022, Orphanet. p. 95. Dürig, J., et al., Subcutaneous rituximab in patients with diffuse large B cell lymphoma and follicular lymphoma: Final results of the non-interventional study MabSCale. Cancer Med, 2022. Yang, X., et al., Real-World Characteristics, Treatment Patterns, Health Care Resource Use, and Costs of Patients with Diffuse Large B-Cell Lymphoma in the U.S. 2021. 26 (5): p. e817-e826. Tilch, M.K., et al., Burden of depression and anxiety disorders per disease codes in patients with lymphoma in Germany. Support Care Cancer, 2022. 30 (3): p. 2387-2395. Danese, M.D., et al., Second-line therapy in diffuse large B-cell lymphoma (DLBCL): treatment patterns and outcomes in older patients receiving outpatient chemotherapy. Leuk Lymphoma, 2017. 58 (5): p. 1094-1104. Davies, A., et al., Obinutuzumab in the treatment of B-cell malignancies: a comprehensive review. Future Oncol, 2022. 18 (26): p. 2943-2966. Moertl, B., et al., Inpatient treatment of relapsed/refractory diffuse large B-cell lymphoma (r/r DLBCL): A health economic perspective. Clin Lymphoma Myeloma Leuk, 2022. 22 (7): p. 474-482. Tsutsué, S., et al., Nationwide claims database analysis of treatment patterns, costs and survival of Japanese patients with diffuse large B-cell lymphoma. PLoS One, 2020. 15 (8): p. e0237509. Costa, S., et al., Real-world costing analysis for diffuse large B-cell lymphoma in British Columbia. Curr Oncol, 2019. 26 (2): p. 108-113. Ren, J., et al., Economic burden and treatment patterns for patients with diffuse large B-cell lymphoma and follicular lymphoma in the USA. J Comp Eff Res, 2019. 8 (6): p. 393-402. Borchmann, P., et al., Healthcare Resource Utilization and Associated Costs of German Patients with Diffuse Large B-Cell Lymphoma: A Retrospective Health Claims Data Analysis. Oncol Ther, 2022. Lee, R.C., et al., Costs associated with diffuse large B-cell lymphoma patient treatment in a Canadian integrated cancer care center. Value Health, 2008. 11 (2): p. 221-30. Insitut, I. Ausgaben der GKV für Krebsmedikamente . 2022 [cited 2023 24.04.2023]; L01 Antineoplastische Mittel]. Available from: https://www.arzneimittel-atlas.de/arzneimittel/l01-antineoplastische-mittel/ausgaben/ Puckrin, R., et al., Long-term follow-up demonstrates curative potential of autologous stem cell transplantation for relapsed follicular lymphoma. Br J Haematol, 2023. Salles, G., et al., Efficacy comparison of tisagenlecleucel vs usual care in patients with relapsed or refractory follicular lymphoma. Blood Adv, 2022. 6 (22): p. 5835-5843. Mayerhoff, L., et al., Cost associated with hematopoietic stem cell transplantation: a retrospective claims data analysis in Germany. J Comp Eff Res, 2019. 8 (2): p. 121-131. Heine, R., et al., Health Economic Aspects of Chimeric Antigen Receptor T-cell Therapies for Hematological Cancers: Present and Future. Hemasphere, 2021. 5 (2): p. e524. Andresen, S., et al., The impact of high-dose chemotherapy, autologous stem cell transplant and conventional chemotherapy on quality of life of long-term survivors with follicular lymphoma. Leuk Lymphoma, 2012. 53 (3): p. 386-93. Additional Declarations Competing interest reported. Karin Berger is employed at the LMU-Clinic, Department Haematology / Oncology. D. Beier and D. Pawlowska-Phelan are employed by InGef-Institute for Applied Health Research Berlin GmbH, which received funding from LMU. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4830530","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345986291,"identity":"9e8542d0-d4e8-4801-a174-961ee715d995","order_by":0,"name":"Karin Berger","email":"data:image/png;base64,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","orcid":"","institution":"Department of Medicine III, University Hospital, LMU Klinikum","correspondingAuthor":true,"prefix":"","firstName":"Karin","middleName":"","lastName":"Berger","suffix":""},{"id":345986292,"identity":"891b5dfb-3258-42f9-bbe7-c877ce115b79","order_by":1,"name":"Bernhard Moertl","email":"","orcid":"","institution":"Department of Medicine III, University Hospital, LMU Klinikum","correspondingAuthor":false,"prefix":"","firstName":"Bernhard","middleName":"","lastName":"Moertl","suffix":""},{"id":345986293,"identity":"423b55ce-e376-418b-b559-ae2874594c44","order_by":2,"name":"Michael von Bergwelt-Baildon","email":"","orcid":"","institution":"Department of Medicine III, University Hospital, LMU Klinikum","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"von Bergwelt-Baildon","suffix":""},{"id":345986294,"identity":"999b389a-8422-454f-b132-2ad801d8c89d","order_by":3,"name":"Dominik Obermueller","email":"","orcid":"","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":false,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Obermueller","suffix":""},{"id":345986295,"identity":"20cbd3a3-b1d9-4b49-b43d-cf6e48f17834","order_by":4,"name":"Dorota Pawlowska-Phelan","email":"","orcid":"","institution":"InGef - Institute for Applied Health Research Berlin GmbH","correspondingAuthor":false,"prefix":"","firstName":"Dorota","middleName":"","lastName":"Pawlowska-Phelan","suffix":""},{"id":345986298,"identity":"b2a6a13d-aa65-44b1-9786-33a85341a67b","order_by":5,"name":"Martin Dreyling","email":"","orcid":"","institution":"Department of Medicine III, University Hospital, LMU Klinikum","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Dreyling","suffix":""}],"badges":[],"createdAt":"2024-07-30 17:41:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4830530/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4830530/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00277-025-06592-8","type":"published","date":"2025-09-02T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63914171,"identity":"5797be53-f9b9-4543-be84-70d8f7979cdc","added_by":"auto","created_at":"2024-09-03 17:07:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDocumented overall survival probability for DLBCL patients after SCT\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4830530/v1/43e9f424502229583073cfce.png"},{"id":90827908,"identity":"37966938-1dc1-4517-84d9-4c517060ab18","added_by":"auto","created_at":"2025-09-08 16:02:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1183523,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4830530/v1/6420be06-8ba7-4396-9915-1d2b025a5f00.pdf"},{"id":63914172,"identity":"62604d62-fd3d-4e6b-acee-809c8fb0d5e8","added_by":"auto","created_at":"2024-09-03 17:07:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":286081,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementsfinalAoH.docx","url":"https://assets-eu.researchsquare.com/files/rs-4830530/v1/44ac45d4e5b06c447afd7fba.docx"}],"financialInterests":"Competing interest reported. Karin Berger is employed at the LMU-Clinic, Department Haematology / Oncology. D. Beier and D. Pawlowska-Phelan are employed by InGef-Institute for Applied Health Research Berlin GmbH, which received funding from LMU. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","formattedTitle":"Follicular lymphoma or diffuse large B-cell lymphoma: a population based analysis of epidemiological and health economic aspects in Germany","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHematological malignancies include a heterogeneous group of lymphomas, multiple myeloma, and leukemia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. They differ according to cell type, clinical and molecular characteristics as well as prognosis and treatment options [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Follicular- (FL) and diffuse large B-cell lymphomas (DLBCL) are the most common subtypes of malignant lymphomas (ML) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Both entities arise from B-cells and occur with varying aggressiveness and heterogeneity. While in DLBCL, the treatment aim is mostly curative the relative five-year survival of FL patients is also close to 70% due to the indolent course of the disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. On the other hand, watch and wait is an established approach in FL whereas DLBCL progress rapidly without treatment.\u003c/p\u003e \u003cp\u003eCure rates of approx. 60\u0026ndash;80% for DLBCL patients have been published for DLBCL first-line therapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In contrast, there is no curative setting for FL patients, the overall treatment-aim is long-term remission [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. International publications show significantly reduced survival rates for patients with relapsed or refractory disease (r/r) in both entities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, a wide variety of treatment concepts are applied to treat these patients, depending on age, general condition, response rate, timing, and other clinical indicators [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A various number of chemo-regimes, immunotherapies, radiotherapies, stem cell transplantations (SCT) and also targeted options are recommended by the national FL and DLBCL guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the last years, innovative therapies for r/r patients have been approved by the EMA e.g. bispecific antibodies or CAR-T-cell therapies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Beside the clinical aspects and benefits, the economic consequences of these novel treatment options on third party payers are not entirely known. Recently, a first Budget-Impact analysis based on inpatient data, estimates a \u0026euro;39M to \u0026euro;166M impact on payers budget of implementing CAR-T-cell therapy in Germany [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEspecially, both the standard of care and innovative treatments require comprehensive inpatient \u0026amp; outpatient data on e.g. patient characteristics, treatment patterns, resource use, costs and outcomes for all comparators. Comprehensive trans-sectoral longitudinal information on FL- and DLBCL-patients treatment journeys, treatment patterns and resource consumption are still lacking. This evidence gap is challenging for value demonstration, decision-making and development of future care models for responsible stakeholders. This analysis was conducted to partially address this evidence gap, by generating additional information on FL and DLBCL-care in Germany.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSTUDY DESIGN\u003c/h2\u003e \u003cp\u003eThis analysis was a retrospective cohort study based on the InGef research database. The observation period covers the years from 2014 to 2020. Depending on the respective research questions, individual analyses were conducted in the design of a cross-sectional analysis (e.g. incidence and prevalence) and individual analyses in the design of a longitudinal analysis (e.g. the follow-up of stem cell transplanted patients). In the cross-sectional part, prevalent patients were analysed per year. For the determination of the incidence, a prior diagnosis-free pre-observation period of one-year was taken into account. Consequently, the year 2014 was only used as the pre-observation period for the analysis year 2015. Further variables such as demographic and clinical patient characteristics, diagnostic methods, medical therapy, procedures, adverse events, resource utilization, costs, and mortality were considered separately for prevalent patients of both entities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDATA SOURCE\u003c/h2\u003e \u003cp\u003eThe InGef database contains anonymized, longitudinal statutory health insurance (SHI) data of about 9\u0026nbsp;million individuals who are insured by one of more than 50 German health insurance companies included in this database. For this analysis, an annual representative sample of about 3.25\u0026nbsp;million insured adult persons was used, which is representative to the age and sex distribution of the German population. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The total observation period available is limited to six years.\u003c/p\u003e \u003cp\u003eThe InGef research database contains socio-demographic information such as gender, age and region of residence. In addition, detailed information on inpatient and outpatient care are obtained from the database. The inpatient data include the date of admission, the date of discharge, diagnostic and therapeutic information (OPS-Level) with exact dates and diagnoses. The outpatient data also contain diagnostic and therapeutic information. Outpatient prescription data contain information (type and quantities) on the ATC-Level. All diagnoses are coded according to the German Modification of the International Classification of Diseases, 10th Revision (ICD-10-GM).\u003c/p\u003e \u003cp\u003eSince all insured individual data in the InGef database are made anonymized and are no longer social data in the sense of \u0026sect;\u0026nbsp;67 para. 2 SGB X in combination with Art. 4 No. 1 DSGVO, their use for scientific research purposes is compliant with German laws, accordingly no further permission from an ethics committee is required. The analysis follows the recommendations of Good Epidemiological Practice (GEP) and Good Practice Secondary Data Analysis (GPS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCase definition\u003c/h2\u003e \u003cp\u003eFollicular lymphoma (ICD-10: C82.0 \u0026ndash; C82.3) and DLBCL (ICD-10: C83.3) diagnosis as an inpatient main or secondary diagnosis and/or two outpatient diagnoses in different quarters of the respective analysis year (M2Q criterion).\u003c/p\u003e \u003cp\u003eFor incidence patients, the index date was defined according to the setting of the initial diagnosis: For inpatient main or secondary diagnoses, the date of hospital discharge; for confirmed outpatient diagnoses (with corresponding fulfilment of the M2Q criterion), the first physician contact with the diagnosing physician representing the index date. If an initial outpatient diagnosis was followed by an inpatient diagnosis in another quarter, the outpatient diagnosis defines the index date. If there was an outpatient and an inpatient diagnosis in the same quarter, the date of the inpatient diagnosis was defined as the index date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables\u003c/h2\u003e \u003cp\u003ePatient characteristics were assessed according to the individual index year during the observation period. Sociodemographic variables included age and gender. The Elixhauser- and Charlson Comorbidity Scores were used to describe the general comorbidity burden. Entity-relevant ICD-codings (e.g. Lymphoma) were taken into account for the comorbidity-scoring. In addition, the specific comorbidity burden was assessed through another data-driven approach, identifying and describing the five most common comorbidities. Medical variables and measures: diagnostics (ICD-10-Code), outpatient medication (ATC-L-Code), relevant inpatient and outpatient procedures (OPS-code). Side effects (ICD-10-Code; e.g. sepsis, thrombosis, etc.). Economic variables; Inpatient stay, costs. Outcomes in terms of mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis A and B on patients with stem-cell-transplant\u003c/h2\u003e \u003cp\u003eIdentification via OPS-coding (auto-SCT: 5-411.0, 8-805.0; allo-SCT: 5-411.2\u0026ndash;5-411.5, 8-805.2-8-805.5). The subgroups of patients with respective therapy were analysed for both entities in 6, 12 or 24 months after the treatment-coding. The following measures were assessed within the available follow-up period: 1.) Patient characteristics and comorbidities at the time of therapy-coding. 2.) Number and duration of hospitalisations in the 6, 12 and 24 months after therapy. 3) Costs of transplant in the 6, 12 or 24 months after SCT-coding. 4) Mortality incl. Kaplan-Meier curve in the 6, 12 or 24 months after therapy.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence and Incidence\u003c/h2\u003e \u003cp\u003eOut of total 8.8\u0026nbsp;million individuals in the database, an annual subsample from the years 2015\u0026ndash;2020 of ~\u0026thinsp;3.25\u0026nbsp;million individuals were included in this analysis, being representative for the German population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. With, a mean of 956 prevalent patients with follicular lymphoma and 1.362 patients with diffuse large B-cell lymphoma, corresponding to a 1-year FL prevalence rate of 29,4/ 100.000 [median 30, range 25,6\u0026ndash;31,9] and 41,9 / 100.000 [median 42,6, range 36,8\u0026ndash;44,6] for the DLBCL-cohort, respectively. The mean incidence rate was 7,3 per 100.000 [median 7,2, range 6,7\u0026ndash;8,1] for FL and 14,4 [median 14,3, range 13,1\u0026ndash;15,3] for DLBCL-pts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics and comorbidities\u003c/h2\u003e \u003cp\u003eDetails are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The annual distribution of the TOP-5 comorbidities did not change during the observation period (Supp_Table 1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics of prevalent FL/ DLBCL cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eDLBCL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of individuals per year;\u003c/p\u003e \u003cp\u003en absolute (n per 100.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eper 100.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eper 100.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e18\u0026ndash;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e18\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group in respective year; n absolute\u0026nbsp;(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18-39y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40-59y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60-80y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex; n absolute (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities; mean score (standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e4,1 +/- 2,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e4,8 +/- 2,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElixhauser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e5,2 +/- 3,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e6,1 +/- 3,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Description Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e] For comorbidity scoring all variables (incl. entity-specific) were included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic, treatment and procedures\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eDiagnostic\u003c/h2\u003e \u003cp\u003eIn the inpatient-setting 33% of the FL-patients received a minimum of one CT-diagnostic, 9% an MRT and 7% an ultrasound per year. In the outpatient setting: 42% of the FL-patients received minimum one CT-diagnostic, 19% a MRT and 86% an ultrasound. Similarly, in the inpatient-setting 46% of the DLBCL-patients received a minimum of one CT-diagnostic, 18% an MRT and 9% an ultrasound. In the outpatient setting, 36% of the DLBCL-patients received a minimum of one CT-diagnostic, 16% a MRT and 74% an ultrasound.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTreatment\u003c/h2\u003e \u003cp\u003eFor outpatient FL medication on ATC-level the annual distribution was comparable between 2015 and 2017, including Rituximab, Bendamustin, Vincristin, Doxorubicin and Cyclophosphamide with descending shares. The percentage of Obinutuzumab increased between 2018 and 2020. For DLBCL patients, the distribution was comparable including Rituximab, Vincristin, Cyclophosphamid, Doxorubicin and Filgrastim/ Pegfilgrastim with descending shares. The percentage of Rituximab decreased slightly over the years. For the inpatient medication on OPS-Level, the annual distribution does not change between 2015 and 2020 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For more detailed information, see Supp_Tables 2 \u0026amp; 3 in the supplements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview mean inpatient treatment procedures between 2015 and 2020 per year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDLBCL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon complex chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately complex and intensive block chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh complex and intensive block chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighly active antiretroviral therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR-T-cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem cell transplant autogeneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem cell transplant allogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSide effects:\u003c/h2\u003e \u003cp\u003eFor the prevalent FL-patients following top-5 side effects (ICD-Codings) were documented: Reaction to severe stress and anxiety (12,8%), agranulocytosis and neutropenia (7,2%), immunodeficiency with predominant antibody deficiency (5,3%), thrombosis, phlebitis and thrombophlebitis (4,9%), and acute renal failure (4,3%). For the DLBCL-pts: agranulocytosis and neutropenia (16%), Reaction to severe stress and anxiety (11,7%), acute renal failure (8,9%), Aplastic anemia due to cytostatic therapy (7,8%), other secondary thrombocytopenia not designated as transfusion refractory (7,2%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHealthcare resource utilization (HCRU) and costs\u003c/h2\u003e \u003cp\u003eMean HCRU and associated costs are presented in the Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysician visit and hospitalization, mean annual numbers per patient\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003etotal pat. n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePat. n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of physician consultations (n, ppt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of hospitalizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInpatient stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDLBCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of physician consultations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of hospitalizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInpatient stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the FL study cohort, 64% of the patients were observed with at least one hospitalization. A rate of 78% were documented for the DLBCL patients.\u003c/p\u003e \u003cp\u003eAssociated mean inpatient costs per patient amounted to \u0026euro; 6.740 per year for a FL patient and \u0026euro; 14.188 for the DLBCL-cohort. In the outpatient setting mean annual costs per FL patient of \u0026euro; 2.012 and \u0026euro;1.906 for DLBCL were documented. The total mean costs were \u0026euro;15.258 per patient (ppt) in the FL-cohort (SD 20.367) and \u0026euro;23.455 (SD 32.892) among patients with DLBCL. More details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual total costs for FL and DLBCL patients in \u0026euro;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e255.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e239.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e166.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDLBCL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e244.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e337.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e287.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e602.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e572.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e591.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of costs for the mean costs per FL-patient was: 13% outpatient care, 44 inpatient care, 39% drugs and 3% remedies and aids. For the DLBCL-patients: 8% outpatient care, 60% inpatient care, 29% drugs and 3% remedies and aids.\u003c/p\u003e \u003cp\u003eIn total, mean costs for the FL-cohort (mean n\u0026thinsp;=\u0026thinsp;956pts) of \u0026euro;14.566.390 are documented per year, for the DLBCL patients (mean n\u0026thinsp;=\u0026thinsp;1.361pts) \u0026euro;31.960.242, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eDocumented mortality\u003c/h2\u003e \u003cp\u003eThe annual mortality rate for the prevalent FL-patients was mean n\u0026thinsp;=\u0026thinsp;47,8 patients (5%). In the DLBCL cohort n\u0026thinsp;=\u0026thinsp;182 patients died (13,4%).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis A: FL Patients with stem cell transplant\u003c/h2\u003e \u003cp\u003eIn the FL-cohort n\u0026thinsp;=\u0026thinsp;38 patients (2,01%) received a stem cell transplant per year. An auto-SCT received 27 patients (1,27%) and 8 patients received allo-SCT (0,42%). Because of data protection (n\u0026thinsp;\u0026gt;\u0026thinsp;5 patients) no analysis was feasible for patients with auto- and allo-SCT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics and comorbidities\u003c/h2\u003e \u003cp\u003eFor patients with auto-SCT the mean age was 59,5y (SD: 11,68y; median: 63y; range 34-74y). The gender distribution for this cohort was n\u0026thinsp;=\u0026thinsp;7 (18,4%) female and n\u0026thinsp;=\u0026thinsp;20 (52,6%) male. Because of data protection, no detailed information for allo-patients can be shown. In terms of comorbidities, the mean Charlson Comorbidity Index (CCI) for autologous transplanted patients was: 4,11 (SD: 2,33; median: 3,0; range 2\u0026ndash;5). The Elixhauser Comorbidity Score for auto-SCT patients was: 6,78 (SD: 2,65; median: 7; range 3\u0026ndash;14).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eHospitalization during 12M after transplant\u003c/h2\u003e \u003cp\u003eThe mean number of inpatient stays for the auto-SCT cohort was 4,05 (SD: 2,52; median: 3,5; range 1\u0026ndash;10) with a mean number of n\u0026thinsp;=\u0026thinsp;67 inpatient days (SD: 69,86; median: 35; range 23\u0026ndash;258).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTotal costs transplant and 12M after\u003c/h2\u003e \u003cp\u003eThe mean total costs for patients with auto-SCT were \u0026euro;46.270 (SD: 21.936; median: 41.882; range 12.596\u0026ndash;106.842). Due to 4% for outpatient care, 79% for inpatient care, 16% for medicines and 1% for remedies and aids.\u003c/p\u003e \u003cp\u003eBecause of limited FL patients, overall survival probability cannot be shown.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis C: DLBCL Patients with stem cell transplant\u003c/h2\u003e \u003cp\u003eOver the years 2015 until 2019 n\u0026thinsp;=\u0026thinsp;146 prevalent DLBCL patients (4,67%) received a stem cell transplant. A number of 117 patients received an auto-SCT (3,75%) and 23 patients an allo-SCT (0,74%). Because of data protection (n\u0026thinsp;\u0026gt;\u0026thinsp;5 patients) no results were shown for patients with auto- and allo-SCT together.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCharacteristics and comorbidities\u003c/h2\u003e \u003cp\u003eFor patients with auto-SCT the mean age at SCT-procedure was 61y (SD: 10,2y; median: 63y; range 33-77y). The sex distribution for the auto-SCT cohort was n\u0026thinsp;=\u0026thinsp;42 (36%) female and n\u0026thinsp;=\u0026thinsp;75 (64%) male. For the n\u0026thinsp;=\u0026thinsp;23 allo-SCT patients a mean age of 52,7y (SD: 12,06y; median: 55y; range 24-67y) was documented. The sex distribution was: n\u0026thinsp;=\u0026thinsp;6 pts (26%) were female and n\u0026thinsp;=\u0026thinsp;17 (74%) male. In terms of comorbidities, the mean CCI-score for autologous transplanted patients were: 6,09 (SD: 2,99; median: 6,0; range 2\u0026ndash;13). In the allo-SCT cohort a score of 4,22 (SD: 1,91; median: 4,0; range 2\u0026ndash;9) were documented. The Elixhauser comorbidity score for auto-SCT patients was: 8,38 (SD: 2,9; median: 8; range 3\u0026ndash;17) and 6,17 (SD: 2,46; median: 6; range 4\u0026ndash;14) for the allo-SCT cohort.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eHospitalization in 12M after transplant\u003c/h2\u003e \u003cp\u003eThe mean number of inpatient stays in the 12M after transplant, was n\u0026thinsp;=\u0026thinsp;3,9 per auto-SCT patient (SD: 2,29; median: 4; range 1\u0026ndash;10) with a mean number of inpatient days of 79d (SD: 75,6; median: 44; range 1\u0026ndash;318). The mean number of inpatient stays for patients with allo-SCT were 6,1 (SD: 3,38; median: 5,5; range 1\u0026ndash;12) with a mean 183 inpatient days (SD: 116,9; median: 144; range 40\u0026ndash;345).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eTotal costs during and 12M after transplant\u003c/h2\u003e \u003cp\u003eThe total costs in the 12M during and after transplant for all patients with auto-SCT (n\u0026thinsp;=\u0026thinsp;95) were \u0026euro;5.373.020 and for the allo-SCT cohort n\u0026thinsp;=\u0026thinsp;14 \u0026euro;2.263.269. The mean total costs were \u0026euro;56.558 per auto-SCT patient (SD: 45.926; median: 42.064; range 12.596\u0026ndash;338.009). Due to 3% for outpatient care, 82% for inpatient care, 11% for medicines and 4% for remedies and aids. The mean total costs for allo-SCT patients were \u0026euro;161.662 ppt (SD: 75.266; median: 148.105; range 86.128\u0026ndash;294.691) in the 12 months after coding. Due to 2% for outpatient care, 79% for inpatient care, 18% for medicines and 0% for remedies and aids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eOutcomes in terms of documented mortality after transplant\u003c/h2\u003e \u003cp\u003eThe mortality rate of the auto-SCT cohort (n\u0026thinsp;=\u0026thinsp;95 pts) was 5,23% (n\u0026thinsp;=\u0026thinsp;5pts) in the 12M follow-up after transplant. In the allo-SCT cohort n\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;5 patients had a documented death. For more details, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe findings of this study demonstrate the high burden of patients with malignant lymphomas (FL, DLBCL). Nearly one-thousand prevalent patients of both entities per year, the maximal documented annual inpatient stay of 335 days, given intensive therapy options like stem cell transplants, and maximal total costs of \u0026euro;620.000\u0026euro; per patient in one year. These results indicating a challenging setting for all stakeholders in the care of these patients. Annually \u0026euro;87.4m for approx. n\u0026thinsp;=\u0026thinsp;956 FL-patients and \u0026euro;191.8m for n\u0026thinsp;=\u0026thinsp;1.361 DLBCL-patients were spent by third party payers between the years 2015\u0026ndash;2020. This study generates valuable insights on FL \u0026amp; DLBCL-care in Germany. To our knowledge, this is likely to be one of the first comprehensive analyses of German claims data for malignant lymphomas such as FL and DLBCL.\u003c/p\u003e \u003cp\u003eOn European level, annual prevalence of 37/100.000 for FL [ICD:C82.0, C82.1, C82.2, C82.3, C82.4, C82.5, C82.6, C82.7, C82.9] and 43/100.000 for DLBCL-patients [ICD-10 C83.3] were recently published. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] In our German study cohort, the FL prevalence extrapolated for Germany was slightly lower (30,3/100.000). The extrapolated prevalence for the DLBCL cohort was 42,4/100.000 what is comparable to aforementioned European data. One reason for these lower FL-rates can be the methodologic difference by the including ICD-codes. Also, there is a trend of rising prevalence over the observation period. Between 2015 and 2020 the prevalence increased by 23% for FL and 22% for DLBCL. This trend of increasing patient numbers is comparable with German data, provided by the \u0026ldquo;Krebs in Deutschland f\u0026uuml;r 2017/2018\u0026rdquo;-report published by the governmental Robert-Koch institute [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of the age-distribution, the majority of the patients had a documented age above 60 years. Up to 75% (FL) and 76% (DLBCL) of the respective patients were in this age group. This observation must be interpreted in a broader view. The number of incident NHL-patients with an age of more than 80years are highest. Discussed in context of a higher mortality-risk for patients\u0026thinsp;\u0026gt;\u0026thinsp;75y, the low numbers in the eldest age-groups in our prevalent cohort are plausible. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] In terms of gender distribution, the InGef-cohort is almost in line with D\u0026uuml;rig J et al. gender is balanced in the FL group (men: 49% men), in the DLBCL group a the proportion of men (57.7%)was slightly higher [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This observation is also in line with previous publications. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNo German publication on comorbidities has been found, therefore no national comparisons are possible. Yang X. et al showed in a US claims data analysis on n\u0026thinsp;=\u0026thinsp;2.500 DLBCL patients a mean CCI-Index number for prevalent patients of 2.3 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our study cohort CCI-score for the prevalent DLBCL cohort was 4,8 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7). The CCI-score is clearly higher, because of e.g. different methodological approaches (entity not excluded). For FL-patients no public available information has been found. Beside the scores, the individual underlying variables indicate a huge patient burden (Supplement Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For example, about 30% of all prevalent patients in both entities had a depression in our study cohort. In a previous publication for Germany a depression-rate for NHL-pts of 22,3% within 10-years after index date was communicated [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Also the documented side effects indicate the high burden for these patients. Here, a clear difference between the two entities was documented. The shares of agranulocytosis and neutropenia, reaction to severe stress and anxiety, and acute renal failure are more than doubled for DLBCL patients. This observation as well as the more intense treatment regimens might be responsible for the documented longer inpatient-days in the DLBCL-cohort. In addition, the mean numbers of diagnostic procedures shown underscore the high resource use, as all patients underwent at least one procedure. However, we assume that this number is underestimated and that patients have significantly more procedures.\u003c/p\u003e \u003cp\u003eOther results on treatment-details, are generally consistent with the expectations of clinical experts and previous international research. Thus, a substantial percentage of chemotherapy-based treatment approaches for the DLBCL cohort (e.g. R-CHOP) is documented. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] In contrast, in the FL-cohort 25% of the prevalent patients received Rituximab in the ambulant setting only. In addition, the increasing usage of innovative treatment after market-approval were documented. After its approval in September 2017, the Obinutuzumab-use for FL-patients continuously increased between 2018 and 2020 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen discussing patient-burden, hospitalization-rates are an additional indicator of previously discussed aspects. The results identified a mean of 20,7 inpatient days in the FL cohort and 29,3 days for the DLBCL patients. The number of hospitalizations were n\u0026thinsp;=\u0026thinsp;2,0 and n\u0026thinsp;=\u0026thinsp;2,9 admissions, in respective. The maximum number of n\u0026thinsp;=\u0026thinsp;323 (FL) and n\u0026thinsp;=\u0026thinsp;336 (DLBCL) inpatient days indicate major expenditures of resources, and a huge burden of disease for individual patients. This observation of huge efforts for a small number of high risk patients, is consistent with a German DLBCL study [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this retrospective analysis of relapsed and refractory DLBCL-patients, a mean number of 63 inpatient days (median 66; range 17\u0026ndash;123; SD\u0026thinsp;\u0026plusmn;\u0026thinsp;36) and 5 admissions (3; 1\u0026ndash;12; \u0026plusmn;3) were documented in DLBCL patients with \u0026gt;\u0026thinsp;3 lines of therapy.\u003c/p\u003e \u003cp\u003eHowever, in our study the annual costs for inpatient care contributed to highest shares of total cost for both entities and over the total observation period. A high proportion of inpatient cost was also noted in several international real-world cost analysis on DLBCL and FL. [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] A recent German claims data analysis on DLBCL-patients documented hospitalization as the main cost driver with 71%. The time-unadjusted absolute costs sum up to \u0026euro;59,868 (43,331), \u0026euro;35,870 (37,387), and \u0026euro;28,832 (40,540) during first-line, second-line, and third-line treatments, respectively. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Due to methodological differences (e.g. results classified in lines of therapy only) no detailed comparison were performed. International publications also show comparable results: In a Canadian study of DLBCL patients, the inpatient stay for second-line treatment was the largest cost driver (62%)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Tsutsu\u0026eacute; et al. showed in a Japanese claims data analysis, that the majority of the overall costs as well as per-treatment line were due to inpatient costs (n\u0026thinsp;=\u0026thinsp;6,821) of 47,903.08 USD (SD, 47,497.30; range, 247.43\u0026ndash;488,296.86) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A major factor in this context is the choice of therapy. One example of a treatment approach that has a huge economic impact is the stem cell transplantation. In a Canadian study, autologous SCT and hospitalization contributed the most to direct costs for DLBCL patients in more than three lines of therapy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We discussed this in context of the subgroup analysis on SCTs, in the following text.\u003c/p\u003e \u003cp\u003eIn general, the mean and median numbers of all cost-data differ significantly because of the wide range of min and max numbers, with a maximal annual cost of \u0026euro;255.353 per patient in FL and \u0026euro;602.243 for the DLBCL. A small number of patients with very high total costs were documented. From 2018 to 2019/20 the maximal costs almost doubled in the DLBCL-cohort. This significant increase may be caused by a higher number of treatment-approaches with cost-intensive innovative / personalized medicines. In total, mean costs of \u0026euro;15.4m are documented per year in the FL-cohort, in comparison to \u0026euro;34.6m in the DLBCL patients. The mean annual costs in years 2017 to 2020 amounted to 0.3% (FL) and 0,7% (DLBCL) of the German statutory health insurance expenses in these years on antineoplastic agents (approximately \u0026euro;5\u0026nbsp;billion) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of outcomes, we focussed on mortality and survival. The mean annual mortality rate for the prevalent FL-patients was 5% and 13,4% for the DLBCL-cohort. Since these cohorts are a cross-section of all patients in the corresponding year, the outcome-results of the SCT subgroup were discussed only. Regarding the outcomes in the subgroup analysis on stem cell transplanted patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), a recent Canadian study on FL-patients demonstrated also an potential long-term benefit of SCT in Canada [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Because of the insufficient evidence level on German data for both entities, no further comparison with national data was possible. However, when discussing on treatment and respective outcome, innovative approaches must be set into perspective e.g. CAR-T cell therapies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs an addition, in the subgroup analysis, the mean 12M-total cost after autologous SCT were \u0026euro;46.270 for FL and \u0026euro;56.558 for the DLBCL patients. The mean 12M\u0026ndash;costs for DLBCL-patients with allo-SCT were \u0026euro;161.662. Mayerhoff et al. reported direct costs of \u0026euro;230,399/patient (DLBCL/FL allogeneic) and \u0026euro;107,457/patient (DLBCL/FL autologous) for Germany. These reported costs are substantially higher, because they were summed up in the period of two quarters before and eight quarters after SCT. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Moertl et al. documented mean treatment costs per DLBCL-patient with auto-SCT of \u0026euro;55,468 and \u0026euro;131,264 for allo-SCT in the clinical setting[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Further research and detailed information are needed to put conventional treatments in the context of innovative treatment approaches, such as CAR T-cell therapy, which ranged between 276 086 EUR and 328 727 EUR [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSummarizing, in terms of rising prevalence of haematological malignancies in Germany, comprehensive care for patients can lead to high costs for health systems and places pressure on public budgets. Our results can be used as a baseline for future economic studies, in the context of innovative lymphoma therapies in Germany.\u003c/p\u003e \u003cp\u003eCertain factors limit our findings. The analyses were based on health claims records, which are collected for billing purposes and not primary for research reasons. Therefore, all results depend on quality of coding. Because of the high level of data protection in Germany, individual case validation was not possible and no results for a patient count of n\u0026thinsp;\u0026lt;\u0026thinsp;5 could be displayed. Because of general limitations in health claims datasets, classification of socioeconomic status, clinical details (e.g. tumor status, lines of therapy) or quality of life (QoL) criteria is impossible. Despite this lack of data, the impact of high-dose chemotherapies on the quality of life of e.g. FL patients should not be neglected [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Another limitation of the health claims data is the missing of comprehensive information on specific treatments. For instance, singular medicines dispensed in the inpatient setting cannot be assessed separately as these costs are usually included in the compensation schemes for diagnosis related groups (DRG). On the other hand, this also reflects a major strength of this study as it evaluates overall costs and resource utilization based on the perspective of health insurances with a high rate of data completeness. Similarly, the large and representative sample size is a huge strength.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn summary, in the underlying German health insurance claims database, the number of FL and DLBCL continuously increased between 2015 and 2020. The number and length of inpatient stays indicates a high burden for the patients and their families. Especially for patients with relapsed or refractory (r/r) course of disease, it can be associated with an uncertain prognosis. In addition, the costs for r/r patients who require intensive treatments such as SCTs are significant. For a more holistic view future effort should include linkage to additional data sources, e.g. prospective registries, regional cancer registry to enable comprehensive information on patient journeys, treatment patterns and outcome analysis. However, our results from health insurance claims data provide complementary information for initial discussions with payers, administrators and politician in the context of access to innovative treatments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest:\u003c/h2\u003e\n\u003cp\u003eKarin Berger is employed at the LMU Klinikum, Department Haematology / Oncology. D. Beier and D. Pawlowska-Phelan are employed by InGef-Institute for Applied Health Research Berlin GmbH, which received funding from LMU Klinikum. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eEthical considerations\u003c/h2\u003e\n\u003cp\u003eThis study was approved by a Scientific Committee, consisting of clinical experts from LMU Klinikum and InGef-Insitute for Applied Health Research Berlin. Due to the non-interventional nature of this research, which used a retrospective anonymized claims dataset, no ethical approval was required for this study. The study followed all the dictates of the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eKarin Berger is employed at the LMU-Clinic, Department Haematology / Oncology. D. Beier and D. Pawlowska-Phelan are employed by InGef-Institute for Applied Health Research Berlin GmbH, which received funding from LMU. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding Statement:\u003c/h2\u003e\n\u003cp\u003eThe conduct of this study has been supported by a research grant from Novartis Pharma GmbH; InGef received research funding by LMU Klinikum. Novartis Pharma GmbH provided financial support, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eKB and BM contributed half/half to the development of the conceptional study design, critical interpretation of results, and writing the draft as well as the final version of the article. MD and MvB, supported as clinical experts on lymphoma and have been involved in critical interpretation of results. DB and DPP conducted the data collection and data analysis in consultation with KB and BM. All authors were involved in critically reviewing the article and gave approval of the version to be published. The manuscript was prepared according to the ICMJE Recommendations.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eFunding Statement:The conduct of this study has been supported by a research grant from Novartis Pharma GmbH; InGef received research funding by LMU Klinikum. Novartis Pharma GmbH provided financial support, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data used in this study cannot be made available in the article, the supplemental files, or in a public repository due to German data protection laws (Bundesdatenschutzgesetz). To facilitate the replication of results, anonymized data used for this study are stored on a secure drive at the InGef - Institute for Applied Health Research Berlin. Access to the raw data used in this study can only be provided to external parties under the conditions of a cooperation contract and can be accessed upon request, after written approval ([email protected]), if required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSwerdlow, S.H., et al., \u003cem\u003eThe 2016 revision of the World Health Organization classification of lymphoid neoplasms.\u003c/em\u003e Blood, 2016. \u003cstrong\u003e127\u003c/strong\u003e(20): p. 2375-90.\u003c/li\u003e\n\u003cli\u003eDreyling, M., et al., \u003cem\u003eNewly diagnosed and relapsed follicular lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up\u0026lt;sup\u0026gt;\u0026amp;#x2020;\u0026lt;/sup\u0026gt;\u0026lt;sup\u0026gt;\u0026amp;#x2606;\u0026lt;/sup\u0026gt;.\u003c/em\u003e Annals of Oncology, 2021. \u003cstrong\u003e32\u003c/strong\u003e(3): p. 298-308.\u003c/li\u003e\n\u003cli\u003eTilly, H., et al., \u003cem\u003eDiffuse large B-cell lymphoma (DLBCL): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up\u0026lt;sup\u0026gt;\u0026amp;#x2020;\u0026lt;/sup\u0026gt;.\u003c/em\u003e Annals of Oncology, 2015. \u003cstrong\u003e26\u003c/strong\u003e: p. v116-v125.\u003c/li\u003e\n\u003cli\u003eErdmann, F.e.a., \u003cem\u003eKrebs in Deutschland f\u0026uuml;r 2017/2018.\u003c/em\u003e Robert Koch-Institut und die Gesellschaft der epidemiologischen Krebsregister in Deutschland, 2021. \u003cstrong\u003e13.Ausgabe\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eMorton, L.M., et al., \u003cem\u003eLymphoma incidence patterns by WHO subtype in the United States, 1992-2001.\u003c/em\u003e Blood, 2006. \u003cstrong\u003e107\u003c/strong\u003e(1): p. 265-76.\u003c/li\u003e\n\u003cli\u003eTa, R., et al., \u003cem\u003eMolecular Diagnostic Review of Diffuse Large B-Cell Lymphoma and Its Tumor Microenvironment.\u003c/em\u003e Diagnostics (Basel), 2022. \u003cstrong\u003e12\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003eGisselbrecht, C. and E. 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Available from: https://www.arzneimittel-atlas.de/arzneimittel/l01-antineoplastische-mittel/ausgaben/\u003c/li\u003e\n\u003cli\u003ePuckrin, R., et al., \u003cem\u003eLong-term follow-up demonstrates curative potential of autologous stem cell transplantation for relapsed follicular lymphoma.\u003c/em\u003e Br J Haematol, 2023.\u003c/li\u003e\n\u003cli\u003eSalles, G., et al., \u003cem\u003eEfficacy comparison of tisagenlecleucel vs usual care in patients with relapsed or refractory follicular lymphoma.\u003c/em\u003e Blood Adv, 2022. \u003cstrong\u003e6\u003c/strong\u003e(22): p. 5835-5843.\u003c/li\u003e\n\u003cli\u003eMayerhoff, L., et al., \u003cem\u003eCost associated with hematopoietic stem cell transplantation: a retrospective claims data analysis in Germany.\u003c/em\u003e J Comp Eff Res, 2019. \u003cstrong\u003e8\u003c/strong\u003e(2): p. 121-131.\u003c/li\u003e\n\u003cli\u003eHeine, R., et al., \u003cem\u003eHealth Economic Aspects of Chimeric Antigen Receptor T-cell Therapies for Hematological Cancers: Present and Future.\u003c/em\u003e Hemasphere, 2021. \u003cstrong\u003e5\u003c/strong\u003e(2): p. e524.\u003c/li\u003e\n\u003cli\u003eAndresen, S., et al., \u003cem\u003eThe impact of high-dose chemotherapy, autologous stem cell transplant and conventional chemotherapy on quality of life of long-term survivors with follicular lymphoma.\u003c/em\u003e Leuk Lymphoma, 2012. \u003cstrong\u003e53\u003c/strong\u003e(3): p. 386-93.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4830530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4830530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpidemiological data and information on resource consumption, costs and clinical outcomes of the care of patients (pts) with follicular lymphoma (FL) or diffuse large b-cell lymphoma (DLBCL) in Germany are rare. Objective of this study was to generate information filling these evidence gaps. This retrospective cohort study (2015\u0026ndash;2020) is based on anonymized, longitudinal health claims data. Subgroup analyses on pts with stem-cell transplant (SCT) were performed. About n\u0026thinsp;=\u0026thinsp;950 annual prevalent FL-pts and n\u0026thinsp;=\u0026thinsp;1.360 DLBCL-pts were analysed per year. Mean age of FL-pts was 67 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;13), 50,7%-females. In the DLBCL-cohort mean age was 68,6 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;13,6), 44,4%-females. The share of \u0026ldquo;agranulocytosis and neutropenia\u0026rdquo; as an example of the analyzed side effects was: FL 7,2% and DLBCL 16%. Of the FL-pts 64% had min. one hospital admission, with mean 2 admissions (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;2,3) and a mean duration of 21 days (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;44,7) per year. In the DLBCL-cohort 78% had a hospitalization with 2,9 admissions (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;3,1) and 29 inpatient days (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;47,5). Mean annual costs: FL \u0026euro;15.258 per-patient (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;20.367) and DLBCL \u0026euro;23.455 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;32.892) per-patient. Mean 12-month costs after autologous-SCT were: FL \u0026euro;46.270 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;21.936) and DLBCL \u0026euro;56.558 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;45.926); for allogeneic-SCT (only DLBCL-cohort): \u0026euro;161.662 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;75.266). This study demonstrate a high burden associated with malignant lymphomas. A considerable number or side effects is documented, indicating a difference between the entities. Length of inpatient stay is stressful for patients and associated with significant costs. Total spending for r/r-pts who require intensive treatments like SCTs are significant. Future efforts including linkage to additional data sources with complementary clinical-information are needed.\u003c/p\u003e","manuscriptTitle":"Follicular lymphoma or diffuse large B-cell lymphoma: a population based analysis of epidemiological and health economic aspects in Germany","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 17:06:58","doi":"10.21203/rs.3.rs-4830530/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-05T04:11:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-01T21:35:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-27T23:19:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49065734323098658505714968256012442390","date":"2024-08-25T20:39:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196973990646724268543614019064065015113","date":"2024-08-19T05:20:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210427887235322135002092229864547314172","date":"2024-08-18T11:06:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238450216262938423611604391388138395606","date":"2024-08-14T10:09:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193910159576483022316473783549146728433","date":"2024-08-14T06:05:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-13T10:59:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-31T13:18:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-31T13:17:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2024-07-30T17:39:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"edfe5a57-790d-4065-bfa8-46a1cd053519","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T15:59:02+00:00","versionOfRecord":{"articleIdentity":"rs-4830530","link":"https://doi.org/10.1007/s00277-025-06592-8","journal":{"identity":"annals-of-hematology","isVorOnly":false,"title":"Annals of Hematology"},"publishedOn":"2025-09-02 15:57:08","publishedOnDateReadable":"September 2nd, 2025"},"versionCreatedAt":"2024-09-03 17:06:58","video":"","vorDoi":"10.1007/s00277-025-06592-8","vorDoiUrl":"https://doi.org/10.1007/s00277-025-06592-8","workflowStages":[]},"version":"v1","identity":"rs-4830530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4830530","identity":"rs-4830530","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}