Assessing unmet need among elderly Medicare Beneficiaries with Mantle cell lymphoma: an analysis of treatment patterns, survival, healthcare resource utilization, and costs

Abstract

Studies evaluating real-world outcomes and health care utilization for mantle cell lymphoma are limited. We utilized national Medicare claims (2009–2019) to examine treatment patterns, healthcare resource utilization, costs, and survival in 3664 elderly patients receiving 1 L treatment for MCL. Over a median follow-up of 2.8 years, 40.3% received at least 2 L treatment. The most common 1 L regimen was bendamustine-rituximab (50.1%), with increased use of BTKi-based regimens observed in 2 L (39.4%). Half (51.8%) of patients had an all-cause hospitalization within 12 months of initiating 1 L; hospitalization rates were higher in later lines. Healthcare costs were substantial and most costs (>80%) were MCL-related. Overall survival was poorer among later lines of treatment (median OS from initiation of 1 L: 53.5 months; 2 L: 22.0 months; 3 L: 11.8 months; 4 L: 7.8 months). These results suggest a large unmet need and future work should evaluate whether novel therapies have improved outcomes among elderly patients with MCL.

Keywords:

• Mantle cell lymphoma
• medicare
• elderly
• treatment patterns
• costs
• survival

Introduction

Mantle cell lymphoma (MCL) is a rare type of non-Hodgkin’s lymphoma (NHL) that accounts for approximately six percent of all NHL cases in the United States [1]. With a median age at diagnosis of 70 years, MCL predominantly affects older patients [2]. Due to the aggressive nature of the disease, most older adults with MCL typically do not achieve durable remissions, with relapse or progression often occurring within 2 to 3 years [3–6].

Given the high rates of relapse, significant efforts have been made to develop treatments for relapsed/refractory MCL patients. The introduction of the first Bruton’s tyrosine kinase inhibitor (BTKi) ibrutinib in November 2013 offered an important treatment option for patients who had experienced lymphoma recurrence or progression after at least one prior line of treatment. More recently, several newer treatment options have become available for relapsed/refractory patients, such as second-generation BTKis and chimeric antigen receptor (CAR) T-cell therapy [7–9]. In addition to the availability of these newer MCL treatment options, there are several ongoing investigations testing novel therapies such as B-cell lymphoma 2 (BCL-2) inhibitors, non-covalent BTKis, and receptor tyrosine kinase like orphan receptor-1 (ROR-1) targeting agents [10–13]. Further, many of these innovative therapies have shown promise for use in frontline MCL, such as BTKis in combination with bendamustine-rituximab or BTKis in place of autologous stem cell transplantation (ASCT) [14,15].

While we wait for outcomes data to accumulate for newer therapies, a real-world analysis of MCL management prior to the introduction of the most recently approved novel agents will provide a valuable benchmark of standard of care treatments. Most of the previous real-world studies in MCL have focused on privately insured patients and used older data [16–19]. Much of the real-world evidence in the elderly Medicare population is limited [20,21]. No prior real-world study in elderly patients has examined the relapsed/refractory setting, particularly later lines of treatment. Furthermore, there has been no assessment of the health care utilization and costs by lines of treatment among elderly patients. This represents a major gap in the literature for three reasons: (1) MCL is more prevalent among the elderly; (2) elderly patients often have issues of multiple comorbidities and polypharmacy; and (3) elderly patients are often under-represented in clinical trials. Consequently, treatment and healthcare utilization patterns and costs among elderly patients in the real-world setting are needed to provide a better understanding of the potential unmet need in this population, especially in later lines of MCL therapy. The objective of this study was to examine real-world treatment patterns, survival, healthcare resource use (HRU), and costs in a national sample of elderly (≥66 years) Medicare beneficiaries with MCL in the frontline and relapsed/refractory setting.

Methods

Study design & data source

This retrospective analysis utilized Chronic Conditions Warehouse (CCW) 100% Medicare Part A, B, and D claims from 2009 to 2019, the latest year of data available at the time of the analysis. The CCW 100% Medicare files are accessed via a data use agreement with the Centers for Medicare & Medicaid Services and contain deidentified data on all fee-for-service Medicare beneficiaries in the United States, including medical (inpatient, outpatient, skilled nursing facility, hospice, home health, durable medical equipment) and prescription (pharmacy) claims. The Medicare Part B file contained outpatient medical claims for all physician-administered drugs (e.g. infusions) and the Medicare Part D file contained prescription drug claims for all self-administered drugs (e.g. orals). The Medicare Parts A, B, and D claims files are linked to Medicare personal summary files that contain patient demographics, eligibility information, and date of death information.

Study sample

All patients with at least one inpatient claim or two outpatient claims ≥30 days apart with a primary diagnosis of MCL (Appendix A1) between 1 January 2010 and 31 December 2018 were identified (date of the first MCL diagnosis = index date). Patients were required to meet the following inclusion criteria: (1) continuous fee-for-service Medicare Parts A, B, and D coverage for at least 12 months before and after the index date, (2) ≥66 years old on the index date, and (3) evidence of a known, identifiable MCL-indicated treatment after initial MCL diagnosis (see Appendix A2). MCL treatments were identified using National Comprehensive Cancer Network Guidelines (NCCN) and prior published studies [16,22]. Patients were excluded if they had evidence of: (1) ≥1 claim with a diagnosis for MCL in the 12 months before the index date (to ensure inclusion of only newly diagnosed MCL patients in the study sample), (2) any evidence of any anticancer treatment in the 12-month pre-index period, (3) evidence of stem cell transplant (SCT, Appendix A5) in the pre-index period.

Classifying lines of therapy

The overall sample was followed from the index date (i.e. new MCL diagnosis) until end of study period (31 December 2019), death, or censoring (e.g. enrollment into private Medicare Advantage (MA) plans since data were unavailable for these plans), whichever occurred earlier. A claims-based algorithm was developed to classify lines of therapy based on previously published administrative claims studies and clinical expertise [16,18]. To classify a line of therapy, the service date for the first claim was identified for any MCL drug therapy occurring within an individual’s follow up period after the index date. All therapies filled or administered during or within 35 days of the dates of the first MCL drug fill or infusion identified were considered part of first-line therapy; the same principle was used for subsequent lines of therapy. Patients were assumed to have ended a previous line of therapy and started a new line of therapy if they (1) initiated a new treatment not part of the original line of therapy (i.e. switching or adding a therapy), or (2) re-initiated a therapy after treatment-free interval of >90 days from the end date of the latest drug in that regimen. Patients who died, entered hospice, or switched to a Medicare Advantage plan were assumed to have ended a line of therapy at that date. Full details on the claims-based algorithm are available in the Appendix A3.

Patients who had evidence of initiating frontline treatment (1 L+) in the post-index follow-up period were classified as frontline treatment patients. Patients who had evidence of initiating second-line (2 L+) or subsequent lines of treatments (3 L + or 4 L+) in the post-index follow-up period were classified as relapsed/refractory patients.

Outcomes

Sociodemographic and clinical characteristics were measured at the index date or during the 12-month period prior to the index date. Key study characteristics included age, gender, race/ethnicity, low-income subsidy status, region, metropolitan status, NCI-adjusted Charlson comorbidity score, all-cause hospitalization in 12-month pre-index period, all-cause total healthcare costs in 12-month pre-index period, and year of index date.

Treatment pattern outcomes were measured from initial MCL diagnosis until the end of the study period (31 December 2019), death, or censoring. We report the number of patients receiving first-line (1 L+), second-line (2 L+), third-line (3 L+), and fourth-line (4 L+) therapy as well as hematopoietic SCT. Time from initial diagnosis to initiation of each line of treatment is reported. This was defined as the number of days from the index date (i.e. date of first MCL diagnosis) to initiation of each type of treatment (among those receiving each type of treatment). We also report both duration of lines of treatment and time between lines of treatment. Duration of a line of treatment was defined as the number of days from the initiation of a line of treatment to the earliest date of discontinuation (defined as the start of a treatment-free interval of >90 days) or switch to a new line of treatment or until patient died or entered hospice (among those receiving each line of treatment). Time between treatment lines was a measure of the number of days between the end of a patient’s line of treatment to the initiation of a subsequent line of treatment. For self-administered anticancer agents identified from the prescription claims data, this was defined as the date the last treatment was filled plus the days’ supply for that therapy. For the physician-administered anticancer agents (e.g. infusions) identified from the medical claims data, this was defined as the date the last treatment was administered plus a 29 days’ supply [19]. The specific treatment a patient is receiving for MCL within each line of treatment was reported. To avoid double-counting patients (e.g. classifying a patient receiving IBR + BR in both the Bendamustine and BTK treatment groups), we followed a hierarchy so that all patients with a given regimen were identified before searching for the next regimen (See Appendix A4).

We report median overall survival as well as 1-year and 3-year overall survival rates from the date of initiation of treatment within each line of treatment. All-cause and MCL-related healthcare resource utilization (HRU) and costs during the 12-month follow up period were examined. All-cause medical service use (hospitalization, skilled nursing facility stays, hospice care, home health visits, physician visits) and costs (total, medical, prescription drug) were identified based on all medical claims, regardless of diagnosis. MCL-related HRU and costs were identified based on medical claims with a diagnosis of MCL in any position [16]. For MCL-related prescription costs, we reported costs separately for the MCL treatment prescriptions captured in the pharmacy claims files (i.e. MCL-related Part D drug costs) and the MCL infusion/injectable treatments administered by or under supervision of a physician and captured in the outpatient hospital or carrier claim files (i.e. MCL-related Part B drug costs). A complete list of MCL drugs is available in Appendix A2. We avoided double counting by excluding the MCL-related Part B drug costs from the reporting for the MCL related outpatient hospital and carrier claims costs. Costs were inflated to 2020 US dollars using the medical care component of the 2020 U.S. Consumer Price Index.

Analysis

This study consisted of solely descriptive analyses. Descriptive statistics included mean ± standard deviation (SD) for continuous data and relative frequencies for categorical data. This study was deemed exempt from review by Pearl IRB and a waiver of individual authorization under HIPAA pursuant to 45 CFR 164.512 (i)(2)(i)- (v) exempt status as specified in 45 CFR 164.512 was approved. All analyses were performed using SAS software, version 9.4 [23].

Results

The final sample contained 3,664 Medicare beneficiaries newly diagnosed with MCL and receiving treatment in the frontline setting (see Appendix A6 for sample attrition table). During a median follow up of 2.8 years, 40.3% of patients went on to receive at least second-line treatment [2L+]; 17.7% and 7.8% received at least third-line treatment [3L+] and fourth-line treatment [4L+], respectively. Very few patients received any SCT (5.6%), with the majority being autologous SCT (5.3%).

Patient sociodemographic and clinical characteristics in the frontline and relapsed/refractory setting are presented in Table 1. The median age of the sample in both the frontline and relapsed/refractory setting was 75 years, with over one-quarter ≥80 years old. In both the frontline and relapsed/refractory setting, two-thirds (>66%) of the patients were male and the majority (>92%) were White. Similarly, nearly one-quarter of patients in both settings had evidence of a pre-index all-cause hospitalization.

Table 1. Characteristics of treated elderly Medicare Beneficiaries with newly Diagnosed MCL between 1 January 2010 and 31 December 2018 by line of treatment received.

Characteristics	Frontline	Relapsed/refractory
	1L+ (N = 3664)	2L+ (N = 1476)	3L+ (N = 649)	4L+ (N = 284)
	%	%	%	%
Length of available follow-up in years from initial MCL diagnosis (median)	2.8	3.2	3.6	4.0
Length of available follow-up in years from initiation of specific line of treatment (median)	3.5	2.3	1.8	1.6
Age, median (IQR)	75.0 (71.0, 80.0)	75.0 (70.0, 80.0)	74.0 (70.0, 79.0)	73.0 (70.0, 77.0)
66–69	18.9%	19.6%	20.8%	22.9%
70–74	28.5%	29.8%	33.9%	38.0%
75–79	24.4%	25.1%	23.6%	22.2%
80+	28.2%	25.5%	21.7%	16.9%
Sex
Male	66.1%	68.6%	69.0%	67.3%
Female	33.9%	31.4%	31.0%	32.7%
Race
White	92.8%	92.3%	92.1%	93.7%
Non-white	7.2%	7.7%	7.9%	6.3%
Black	2.8%	2.8%	2.8%	2.8%
Hispanic	1.2%	1.2%	<1.7%	1.2%
Other	3.2%	3.2%	>3.9%	3.2%
Census region
Northeast	18.6%	19.4%	18.6%	18.7%
Midwest	27.3%	25.0%	25.1%	23.2%
South	36.8%	38.0%	39.1%	38.0%
West	17.3%	17.6%	17.1%	20.1%
Metropolitan status
Urban	76.8%	78.3%	80.1%	78.2%
Rural	23.2%	21.7%	19.9%	21.8%
Part D low-income subsidy (LIS) Status
LIS	14.4%	14.0%	14.0%	14.8%
Non-LIS	85.6%	86.0%	86.0%	85.2%
NCI-Adjusted Charlson comorbidity score, mean (SD)	1.1 (1.5)	1.0 (1.3)	0.9 (1.2)	0.8 (1.1)
All-cause hospitalization in 12-months prior to initial MCL diagnosis	23.9%	22.6%	23.3%	22.9%
All-cause total healthcare costs in 12-months prior to initial MCL diagnosis, mean (SD)	$18,108 ($22,091)	$17,259 ($21,684)	$17,031 ($20,179)	$17,534 ($21,261)

Per CMS policy, an exact percentage should not be reported that will allow the calculation of a cell size <11 patients.

Treatment patterns are presented in Table 2. Median time from diagnosis to treatment initiation was 35.0 days. The majority (55.5%) of frontline treated patients initiated a Bendamustine-based regimen for MCL, most commonly Bendamustine-Rituximab (50.1% - Appendix A7). Other less frequently used treatments in 1 L + setting included CVP-based regimens (3.5%), covalent BTKi-based regimens (4.2%), and bortezomib-based regimens (3.9%). Approximately 1 in 10 patients (9.3%) received rituximab monotherapy in the 1 L setting. Relative to the frontline setting, use of targeted therapies increased in the relapsed/refractory setting, most commonly covalent BTKi-based (39.4% [2L+], 31.9% [3L+], 35.6% [4L+]), bortezomib-based (10.9% [2L+], 16.2% [3L+], 12.0% [4L+]), or lenalidomide-based regimens (4.4% [2L+], 11.6% [3L+], 15.8% [4L+]). The vast majority (70%-80%) of patients receiving a covalent BTKi-based regimens across 2 L to 4 L received ibrutinib alone or in combination with other agents while the remainder received acalabrutinib alone or in combination (Appendix A7). The median time between treatment lines from the end of the previous line of treatment to the beginning of a subsequent line of treatment ranged from about 1 to 3.5 months (3.6 months from 1 L to 2 L, 0.9 months from 2 L to 3 L, and 0.4 months from 3 L to 4 L).

Table 2. Type of MCL treatment received by line of therapy among elderly Medicare Beneficiaries treated for MCL, 2010 to 2019.

	Frontline	Relapsed/Refractory
	1L+ (N = 3664)	2L+ (N = 1476)	3L+ (N = 649)	4L+ (N = 284)
Time and Duration of Treatment
Time from diagnosis to treatment, months
Median (IQR)	1.2 (0.7, 2.1)	14.9 (6.8, 31.6)	27.9 (15.4, 45.5)	36.9 (21.7, 55.5)
Duration of treatment, months
Median (IQR)	5.4 (3.2, 8.7)	3.9 (2.0, 7.9)	3.2 (1.7, 5.8)	2.7 (1.4, 5.6)
Time between treatment lines, months*
Median (IQR)	1.2 (0.7, 2.1)	3.6 (0.0, 17.7)	0.9 (0.0, 8.1)	0.4 (0.0, 4.5)
Type of Treatment Received within the Specific Line of Treatment (%)
CHOP-based Regimen (±steroid)	16.1%	5.4%	4.3%	<3.9%
CVP-based Regimen (± steroid)	3.5%	1.6%	<1.7%	<3.9%
Bendamustine-based Regimen (±steroid)	55.5%	23.8%	18.8%	14.4%
Gemcitabine-based Regimen (± steroid)	0.5%	0.9%	2.2%	<3.9%
Covalent BTKi-based regimen (± steroid)	4.2%	39.4%	31.9%	35.6%
Bortezomib-based regimen (± steroid)	3.9%	10.9%	16.2%	12.0%
Lenalidomide-based Regimen (± steroid)	1.4%	4.4%	11.6%	15.8%
CD20 Monotherapy (± steroid)	9.3%	n/a	n/a	n/a
Ifosfamide-based Regimen (± steroid)	<0.3%	<0.7%	<1.7%	0.0%
Cytarabine-based Regimen (± steroid)	0.3%	1.8%	<1.7%	<3.9%
Other Regimens (± steroid)^†	>5.1%	>11.2%	12.3%	14.8%

Per CMS policy, an exact percentage should not be reported that will allow the calculation of a cell size <11 patients. Each column may include patients who have progressed to other lines of therapy, but the results will be reported as specific to the line of treatment in the column header. In order to avoid double-counting patients (e.g. classifying a patient receiving IBR + BR in both the Bendamustine and BTK treatment groups), we followed a hierarchy so that all patients with a given regimen were identified before searching for the next regimen. The hierarchy that was applied matches the descending order of treatments listed in this table.

*Time reported under each column is the time from the end of previous treatment (or in the case of 1 L, the diagnosis date) to the start of the line of treatment.

^†Other regimens include individual agents, unspecified chemotherapy agents, or treatments not included in the categories listed in the table above (e.g. venetoclax).

Median OS from initial MCL diagnosis and from 1 L treatment initiation was 57.6 and 53.5 months, respectively. Median overall survival and the 1-year and 3-year survival rate from initiation of each line of treatment is presented in Figure 1. Survival was poorer in later lines of treatment. Median OS was 22.0, 11.8, and 7.8 months from the initiation of 2 L, 3 L, and 4 L treatment, respectively. The 1-year and 3-year survival rates were 81.3% and 60.6% from 1 L treatment initiation and 63.8% and 38.0% from 2 L treatment initiation.

Figure 1. Overall survival rates from initiation of each line of treatment among Medicare Beneficiaries with Diagnosed MCL.

All-cause and MCL-related healthcare resource utilization are presented in Table 3. HRU was substantial across all lines of therapy in the 12 months after initiating treatment. More than half of patients (51.8%) had a hospitalization due to any cause, most of which were MCL-related (42.2%). Hospice use increased in each line of therapy (10.5% [1L+], 21.7% [2L+], 33.8% [3L+], 41.0% [4L+]).

Table 3. Healthcare resource utilization among Medicare Beneficiaries with Diagnosed MCL in the 12-months after initiation of the specific line of treatment.

	Frontline*	Relapsed/Refractory*
	1L+ (N = 3649)	2L+ (N = 1460)	3L+ (N = 640)	4L+ (N = 283)
All-cause HRU
Hospitalizations
Any hospitalization (%)	51.8%	57.5%	65.0%	70.3%
Number of hospitalizations, mean (SD) (among those who had a hospitalization)	2.3	2.1	2.2	2.2
Length of stay per hospitalization, mean (SD)	7.2	7.4	7.6	8.3
Skilled nursing facility stays
Any stay (%)	11.0%	11.5%	12.3%	14.8%
Number of stays, mean (SD) (among those who had a SNF stay)	2.3	2.0	2.2	1.9
Hospice care (%)	10.5%	21.7%	33.8%	41.0%
Home health visits
Any visits (%)	25.6%	28.1%	33.8%	36.4%
Number of visits, mean (SD) (among those who had a home health visit)	1.8	1.7	1.7	1.8
Physician visits (evaluation/management visits)
Any visits (%)	99.2%	97.7%	97.2%	96.1%
Number of visits, mean (SD) (among those who had a physician visit)	21.0	18.9	17.6	16.7
MCL-related HRU^†
Hospitalizations
Any hospitalization (%)	42.2%	48.4%	56.1%	60.8%
Number of hospitalizations, mean (SD) (among those who had a hospitalization)	2.2	2.0	2.1	2.1
Length of stay per hospitalization, mean (SD)	7.5	7.7	7.8	8.8
Skilled nursing facility stays
Any stay (%)	5.1%	6.4%	6.4%	8.1%
Number of stays, mean (SD) (among those who had a SNF stay)	1.9	1.9	2.0	1.7
Hospice care (%)	5.9%	14.0%	24.7%	29.3%
Home health visits
Any visits (%)	15.4%	18.4%	24.2%	27.6%
Number of visits, mean (SD) (among those who had a home health visit)	1.6	1.5	1.5	1.7
Physician visits (evaluation/management visits)
Any visits (%)	89.8%	89.1%	88.0%	88.0%
Number of visits, mean (SD) (among those who had a physician visit)	11.9	10.9	10.8	10.5

*In order to assess healthcare resource use in the 12-months after initiation of a given line of therapy, an additional requirement of continuous eligibility for 12 months (or until death if it occurred earlier) after the initiation of a given line of therapy was applied, which led to a very small reduction in the Ns across the frontline and relapsed/refractory settings.

^†MCL-related HRU was identified based on medical claims with a diagnosis of MCL in any position.

All-cause and MCL-related healthcare costs are presented in Figures 2 and 3, respectively. Across all lines of therapy, over 80% of costs were related to services associated with an MCL diagnosis. MCL-related total healthcare costs in the 12 months after initiation of treatment were $114,369 (1 L+), $113,768 (2 L+), $113,116 (3 L+), and $115,883 (4 L+). Excluding patients who died within 12-months of treatment initiation, MCL-related total costs were higher for later lines of treatment ($127,820 [2L+], $137,761 [3L+], $155,865 [4L+]) (Appendix A8). MCL-related total prescription drug costs including Part B and Part D drugs were similar across lines of therapy ($73,398 [1L+], $75,873 [2L+], $68,871 [3L+], $61,910 [4L+]). However, in the frontline setting, the vast majority (87%, $63,749) of drug costs were for physician-administered Part B drugs rather than Part D drug (13%, $9,650). Conversely, Part D drugs costs made up a much larger share (55-65%) of MCL-related prescription drug costs in the relapsed/refractory setting ($41,925 [2L+], $41,003 [3L+], $40,381 [4L+]).

Figure 2. All-Cause and MCL-related* Total healthcare costs among Medicare Beneficiaries with Diagnosed MCL in the 12-months after initiation of the specific line of treatment. Costs were inflated to 2020 US dollars using the medical care component of the 2020 U.S. Consumer Price Index.

*MCL-related costs were identified based on claims with a diagnosis of MCL in any position and/or drug codes for one of the MCL indicated drug treatments.

Figure 3. MCL-related* medical and prescription drug costs among medicare beneficiaries with diagnosed MCL in the 12-months after initiation of the specific line of treatment.

Costs were inflated to 2020 US dollars using the medical care component of the 2020 U.S. Consumer Price Index.

* MCL-related costs were identified based on claims with a diagnosis of MCL in any position and/or drug codes for one of the MCL indicated drug treatments.

Discussion

This descriptive study of a national sample of Medicare beneficiaries being treated for MCL provides important insights into real world treatment patterns, healthcare resource utilization, costs and survival among older adults in the front-line and relapse/refractory settings.

Our study revealed that relapse is common for elderly patients with MCL: around 40% of elderly patients on first-line treatment moved to second-line therapy and nearly 20% had evidence of third-line therapy. Furthermore, for the 60% of elderly patients who had evidence of first-line treatment only it is possible that some of them may indeed have had disease progression but decided to forego treatment (for unknown reasons such as tolerability or costs) or died before they could initiate second-line treatment. Finally, both the duration of treatment and time between treatment lines decreased by line of therapy, reflecting ongoing unmet need for managing relapsed/refractory MCL in older adults.

In the frontline setting, we found that over half of our elderly sample received a bendamustine-based regimen, consistent with current clinical best practices in the elderly [22]. The vast majority of these patients were receiving bendamustine-rituximab (BR), which underscores this regimen’s status as the preferred frontline treatment for elderly MCL patients over our study period [24]. For instance, a previous Medicare study using older data (2007 to 2015) found that rituximab was the most common agent (∼40% of patients) administered in the frontline setting with administration of cyclophosphamide, doxorubicin, and vincristine subsequently occurring in 17.9%, 13.1%, and 17.2% of patients, respectively [20]. Taken together with our findings, it suggests that there has been a shift from R-CHOP as the preferred frontline regimen to wider use of BR in more recent years.

In the relapsed/refractory setting, we observed increased use of newer oral oncolytics in treatment regimens. Covalent BTKi-based regimens in particular had the highest frequency of use, with nearly 40% of second-line treated patients receiving a covalent BTKi-based regimen. The vast majority of patients receiving a covalent BTKi-based regimen were receiving ibrutinib (alone or in combination with other agents). The second-generation covalent BTKi acalabrutinib was only approved for MCL in October 2017, hence we found much lower use during our study period (2010 to 2019). As additional next generation BTKis with lower toxicity and/or better efficacy profiles become available in the future, it will be important to assess whether these agents become a preferable option for elderly patients in the relapsed/refractory setting. Furthermore, while only 5% of the frontline patients in our study received ibrutinib, we may see increased use in the future given the positive findings of the recently published phase III SHINE study of ibrutinib in combination with bendamustine-rituximab (BR) in the frontline setting [14].

Despite its potential for deepening responses and delaying MCL progression, very few patients (<6%) in our sample had evidence of autologous or allogenic SCT. Given the higher risk of complications of SCT in older adults (such as cardiovascular toxicity or gastrointestinal complication), this finding is not surprising [25]. In fact, this finding is consistent with a study using 2013-2019 claims data from a national insurer that found only 7.3% of patients diagnosed with MCL (mean age 71.1 years) received SCT [17]. Nevertheless, our finding does underscore the need for promising alternative treatment options for the elderly population. The CAR-T cell therapies first approved for MCL in July 2020 are one such option. However, logistical, reimbursement, and access barriers have been shown to have limited its uptake to date [26]. Furthermore, given the potential toxicities arising from CAR-T cell therapy, elderly patients may be considered unsuitable for receiving this treatment similar to what we observed for SCT in our study. Beyond CAR-T therapy, no clear consensus has emerged on third line treatment for elderly patients with relapsed/refractory MCL after failure of covalent BTKis. While 60% of patients received targeted treatment (covalent BTKi-, bortezomib-, or lenalidomide-based regimens) in the third line, nearly 1 in 5 patients had evidence of conventional chemotherapy (with or without anti-CD20 therapy). Current NCCN guidelines recommend pirtobrutinib or off-label treatment with venetoclax in the third-line setting [22]. While we observed some limited treatment with venetoclax among 3 L and 4 L patients (2-4%), more data will need to accumulate to see if pirtobrutinb has seen wide adoption as 3 L treatment. However, our findings underscore a need for other efficacious treatment options with greater tolerability in the relapsed/refractory setting.

In general, overall survival was poor in our study, especially in the relapsed/refractory setting. Half of patients lived less than 22 months after initiating 2 L treatment; among patients initiating 3 L treatment, half of the patients had died within a year. The findings of poor overall survival were accompanied by high healthcare resource use and costs. Of note was the high rate of hospitalization observed in both settings: over half of frontline treated patients had evidence of a hospitalization during the follow up period; the rate was even higher in the relapsed/refractory population ranging from more than half in 2 L to more than two-thirds in the 4 L. In both cases, the vast majority of these hospitalizations were MCL-related, possibly due to disease symptoms or treatment toxicity. This finding highlights the high burden that MCL places on patients as well as the health system. Healthcare costs were also substantial—in excess of $100,000 over the 12 months post-treatment initiation across all lines of therapy. For instance, 12-month all-cause costs in the 1 L + setting were $139,985 which represents a more than eight-fold increase in all-cause costs compared to the 12-months prior to MCL diagnosis. This is notable for two reasons. First, these high costs were still accompanied by poor survival among elderly patients, especially in the relapsed refractory setting. Second, despite the use of novel agents being limited to the relapsed/refractory setting, healthcare costs and drug costs in particular were still substantial for frontline patients.

Our study has several limitations. First, claims data is subject to possible coding errors and missing/incomplete data. Second, claims data do not contain clinical information such as prognostic indices and biological risk factors (e.g. MIPI score, TP53 mutation, histology) that would allow for greater nuance in the analysis and aid in the interpretation of study results. However, this limitation is offset by the advantage of having 100% Medicare claims that permits efficient and timely population-level analyses with comprehensive information on health care utilization and costs incurred by the patients regardless of setting. Third, we relied on a claims-based definition of relapsed/refractory MCL given that no specific diagnostic code exists to identify relapsed/refractory patients. However, our proxy measure (i.e. classifying patients as relapsed/refractory if they have evidence of subsequent anticancer therapy) and algorithm to identify lines of treatment has been used in prior studies [16,18]. Fourth, our study is generalizable only to the fee-for-service Medicare population rather than the Medicare Advantage population. However, the majority of the U.S. Medicare population was covered under the fee-for-service Medicare program during our study period [27]. Fifth, the proportion of patients treated might be an underestimate because drug treatments administered during hospitalization (such as chemotherapy) are not reported in the claims data. Sixth, we were unable to observe utilization of the newest therapies such as CAR-Ts given our data period ended in 2019 and thus specific Healthcare Common Procedure Coding System (HCPCS) codes for CAR-Ts were not yet available. Seventh, although we required a minimum of 12 months of follow-up for inclusion in our sample, this may not be long enough to capture evidence of disease progression and second-line treatment (particularly for patients identified later in our sample identification window). Finally, some treatments such as acalabrutinib were only approved for MCL later during our study period and hence, our utilization rates reported for such treatments may not be fully reflective of their recent utilization.

In conclusion, this real-world study of U.S. Medicare beneficiaries diagnosed with MCL found short duration of treatment therapies, high rates of hospitalization and hospice care, substantial healthcare costs, and poor overall survival, especially in the relapsed/refractory setting. These data point to a clear unmet need and the importance of novel agents and treatment modalities with higher efficacy and better tolerability to be used alone or in combination to improve outcomes in elderly patients with MCL.

Disclosure statement

SFH reports consultancy for Janssen, Pharmacyclics, AbbVie, AstraZeneca, Flatiron Health Inc., Novartis, SeaGen, Genetech, Merck, TG Therapeutics, ADC Therapeutics, Epizyme, Servier, Arvinas, and Thyme Inc.; research funding from Celgene, DTRM Biopharm, and TG Therapeutics; and honoraria form Pharmacyclics and AstraZeneca, Bayer. JAD reports consultancy for AbbVie, Acadia, Allergan, Boehringer Ingelheim, Catabasis, Ironwood Pharmaceuticals, Janssen, Kite Pharma, MeiraGTx, Merck, Otsuka, Regeneron, Sarepta, Sage Therapeutics, Sanofi, Takeda, The Medicines Company, and Vertex; research funding from AbbVie, Biogen, Humana, Janssen, Merck, Novartis, Pfizer, PhRMA, Regeneron, Sanofi, and Valeant, USA. PS, KER, and MR are employees of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA. JP and SKB are employees of COVIA Health Solutions, a consulting form with clients in the biotech/pharmaceutical industry. The study sponsor was involved in the interpretation of results and drafting of the manuscript

Additional information

Funding

This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, New Jersey.

Appendix A1.

ICD-9 and ICD-10 codes for MCL

Table

ICD-9:
200.4x	Mantle cell lymphoma
200.40	Mantle cell lymphoma, unspecified site, extranodal and solid organ sites
200.41	Mantle cell lymphoma, lymph nodes of head, face, and neck
200.42	Mantle cell lymphoma, intrathoracic lymph nodes
200.43	Mantle cell lymphoma, intra-abdominal lymph nodes
200.44	Mantle cell lymphoma, lymph nodes of axilla and upper limb
200.45	Mantle cell lymphoma, lymph nodes of inguinal region and lower limb
200.46	Mantle cell lymphoma, intrapelvic lymph nodes
200.47	Mantle cell lymphoma, spleen
200.48	Mantle cell lymphoma, lymph nodes of multiple sites
ICD-10:
C83.1x	Mantle cell lymphoma
C83.10	Mantle cell lymphoma, unspecified site
C83.11	Mantle cell lymphoma, lymph nodes of head, face, and neck
C83.12	Mantle cell lymphoma, intrathoracic lymph nodes
C83.13	Mantle cell lymphoma, intra-abdominal lymph nodes
C83.14	Mantle cell lymphoma, lymph nodes of axilla and upper limb
C83.15	Mantle cell lymphoma, lymph nodes of inguinal region and lower limb
C83.16	Mantle cell lymphoma, intrapelvic lymph nodes
C83.17	Mantle cell lymphoma, spleen
C83.18	Mantle cell lymphoma, lymph nodes of multiple sites
C83.19	Mantle cell lymphoma, extranodal and solid organ sites

Appendix A2.

List of individual agents used for MCL

Table

Chemotherapy	Biologic/immunomodulators	Targeted
Bendamustine*	Alemtuzumab	Acalabrutinib*
Bleomycin	Lenalidomide*	Bortezomib*
Busulfan	Nivolumab	Carfilzomib
Carboplatin*	Obinutuzumab*	Copanlisib
Carmustine	Ofatumumab*	Duvelisib
Chlorambucil*	Pembrolizumab	Everolimus
Cisplatin*	Pomalidomide	Ibrutinib*
Cladribine	Rituximab*	Idelalisib
Cyclophosphamide*	Tafasitamab	Temsirolimus*
Cytarabine*	Tositumomab	Umbralisib
Doxorubicin*	Tositumomab I-131	Venetoclax
Epirubicin		Zanubrutinib*
Etoposide*
Fludarabine*
Gemcitabine*
Ifosfamide*
Lomustine
Mechlorethamine
Melphalan
Mesna
Methotrexate*
Mitoxantrone*
Oxaliplatin*
Pentostatin*
Pralatrexate
Procarbazine*
Temozolomide
Thiotepa
Uracil mustard
Vinblastine
Vincristine*
Vinorelbine*

*Treatment counts toward the sample inclusion criteria of required evidence of a known, identifiable MCL-indicated treatment in the front-line setting.

Appendix A3.

Classifying lines of therapy

A claims-based algorithm was developed to identify lines of therapy based on previously published administrative claims studies (Kabadi et al. 2019; Goyal et al. 2019) and in consultation with a clinical advisor. The service dates or administration/dispensing dates of the MCL treatments or medications were instrumental in the implementation of the algorithm. The Medicare Part B outpatient medical claims for all physician-administered drugs (e.g. infusions) included the date of administration. The Medicare Part D prescription drug claims for all self-administered drugs (e.g. orals) included pharmacy fill dates; the prescriptions claims for pharmacy filled drugs also had a variable that denote the days’ supply (i.e. length of the prescription). This information was used to obtain the timing of the different treatments in the algorithm outlined below.

Note: Lines of therapy refers to the treatment regimen and not a specific drug. A line of therapy is made up of one or more drugs (i.e. a patient can be receiving a single drug or a combination regimen). To be consistent with published literature, we are referring to ‘lines of therapy’ in our algorithm, but it is important to keep in mind that this can mean several drugs as approved for combination use.

Individual and combination chemotherapies were identified by Healthcare Common Procedural Coding System codes and National Drug Codes specific to the agents considered.

To identify a line of therapy, the service date for the first claim for any MCL drug therapy occurring within an individual’s follow up period after MCL diagnosis (i.e. index date) was identified.

All therapies filled or administered during or within 35 days of the dates of the first MCL drug fill or infusion identified were considered part of first-line therapy. This 35-day period was previously used by Kabadi et al. (2019) and Goyal et al. (2019) to classify a treatment regimen. Kabadi et al. (2021) and Ghosh et al. (2021) required a slightly shorter window of 30 days and 28 days, respectively. We opted for 35 days given that it was a broader window that would include all therapies identified within 28 days as well. Typical lengths of cycle of MCL therapies are 28 days and use of a 35 day period allows for a 7-day grace period to account for delays due to clinical or logistical reasons in receiving another therapy

Note: While rituximab monotherapy is not a recommended first line treatment, it may occur in real-world clinical practice. Based on our clinical expert’s feedback, any patient who had rituximab as the first claim and no other MCL drug therapies within 90 days of the date of the first rituximab infusion will be classified as receiving rituximab monotherapy. For those patients with evidence of other MCL drug therapies within 90 days of the first rituximab infusion, the first-line treatment regimen was defined based on rituximab and all other MCL drug therapies received within 35 days of the date of the first non-rituximab MCL drug fill or infusion.

The principle is the same for subsequent lines of therapy – all other MCL therapies filled or administered during or within 35 days of the first MCL drug fill (or infusion) in the new line of therapy were considered part of that regimen.

Patients were assumed to have ended the previous line of therapy and started a new line of therapy when one of the following scenarios occurred:

the initiation of a new therapy not part of the most recent prior line of treatment

Switching medications during follow-up was considered starting a new line of therapy (Kabadi et al. 2019; Goyal et al. 2019).

Note: We did not consider removal of a treatment to be a new line of therapy, as this is expected in real-world clinical practice.

Adding a medication was considered starting a new line of therapy (Kabadi et al. 2019; Ghosh et al. 2021). The medication must have been added outside of the 35-day period used to identify a regimen for the specific line of treatment.

Note: Addition of rituximab was not considered the start of a new line of therapy, as this is expected in real-world clinical practice (Kabadi et al. 2019). Note however rituximab was used when determining the duration of a line of therapy if it was administered within the 35-day period used to define the regimen.

re-initiation of at least one or all of the agents (with the exception of rituximab) in the regimen belonging to the most recent prior line of treatment following a treatment-free interval of ≥90 days from the ‘end date’ of the latest drug in that regimen (Kabadi et al. 2019; Goyal et al. 2019; Ghosh et al. 2021).

For oral Part D medications (e.g. ibrutinib), the ‘end date’ of treatment was the date of the last prescription fill plus the days’ supply of that prescription.

For IV-administered Part B drugs including rituximab, the ‘end date’ of the treatment was the date of the last Part B drug administration plus 29 days.

In addition to a line of therapy ending when the patient meets any of the above two criteria (a) or (b), patient death, entering hospice, or reaching the end of the study period was also used to classify the end of a line of therapy.

Patients who die during follow up, enter hospice, or reached the end of the study period were considered censored at the time of those events.

This process was repeated for each subsequent line of therapy.

Hematopoietic stem cell transplantation (HSCT) was classified within an individual line of therapy if it occurred within 120 days of the ‘end date’ of the latest drug filled or administered within that line of therapy (to account for time required for other therapy, as necessary, prior to SCT). See Section 3 (b) for definition of ‘end dates’ for oral Part D medications and IV-administered Part B drugs.

Kabadi SM, Near A, Wada K, Burudpakdee C. Treatment patterns, adverse events, healthcare resource use and costs among commercially insured patients with mantle cell lymphoma in the United States. Cancer Med. 2019 Dec; 8(17): 7174–7185.

Goyal RK, Nagar SP, Kabadi SM, et al. Adverse events, resource use, and economic burden associated with mantle cell lymphoma: a real-world assessment of privately insured patients in the United States. Leuk Lymphoma. 2019 Apr;60(4):955-963.

Appendix A4.

Note on classification hierarchy

In order to avoid double-counting patients (e.g. classifying a patient receiving IBR + BR in both the Bendamustine and BTK treatment groups), we followed a hierarchy so that all patients with a given regimen were identified before searching for the next regimen. The hierarchy that was applied matches the descending order of treatments listed:

1. CHOP-based Regimen (±steroid)

2. CVP-based Regimen (±steroid)

3. Bendamustine-based Regimen (±steroid)

4. Gemcitabine-based Regimen (±steroid)

5. BTKi-based regimen (±steroid)

6. Bortezomib-based regimen (±steroid)

7. Lenalidomide-based Regimen (±steroid)

8. CD20 Monotherapy (±steroid)

9. Ifosfamide-based Regimen (±steroid)

10. Cytarabine-based Regimen (±steroid)

11. Other Regimens (±steroid)

Appendix A5.

Procedure codes for identifying stem cell transplant

Table

CPT:
38240	Allogeneic infusion
38241	Autologous transplant
38242	Allogeneic donor lymphocyte infusion
38243	Allogeneic hematopoietic cellular transplant boost
DRG:
014	Allogeneic bone marrow transplant
015	Autologous bone marrow transplant
016	Autologous bone marrow transplant with cc/mcc
017	Autologous bone marrow transplant without cc/mcc
ICD-10-PCS
30233G2	Transfusion of allogeneic related bone marrow into peripheral vein, percutaneous approach
30233G3	Transfusion of allogeneic unrelated bone marrow into peripheral vein, percutaneous approach
30233Y2	Transfusion of allogeneic related hematopoietic stem cells into peripheral vein, percutaneous approach
30233Y3	Transfusion of allogeneic unrelated hematopoietic stem cells into peripheral vein, percutaneous approach
30243G2	Transfusion of allogeneic related bone marrow into central vein, percutaneous approach
30243G3	Transfusion of allogeneic unrelated bone marrow into central vein, percutaneous approach
30243Y2	Transfusion of allogeneic related hematopoietic stem cells into central vein, percutaneous approach
30243Y3	Transfusion of allogeneic unrelated hematopoietic stem cells into central vein, percutaneous approach
30233C0	Transfusion of autologous hematopoietic stem/progenitor cells, genetically modified into peripheral vein, percutaneous approach
30233G0	Transfusion of autologous bone marrow into peripheral vein, percutaneous approach
30243C0	Transfusion of autologous hematopoietic stem/progenitor cells, genetically modified into central vein, percutaneous approach
30243G0	Transfusion of autologous bone marrow into central vein, percutaneous approach
30233Y0	Transfusion of autologous hematopoietic stem cells into peripheral vein, percutaneous approach
30243Y0	Transfusion of autologous hematopoietic stem cells into central vein, percutaneous approach

Appendix A6.

Sample attrition

Table

Sample selection criteria	N	%
≥1 inpatient claim and/or ≥2 outpatient claims with a diagnosis of MCL in the primary diagnosis position at least 30 days apart between 1 January 2010 and 31 December 2018. The date of this first claim with a diagnosis of MCL will be defined as the index date.	23,341
Continuous fee-for-service Medicare Part A and B coverage for at least 12 months before the index diagnosis date	17,056	73.1%
Continuous fee-for-service Medicare Part D coverage for at least 12 months before the index diagnosis date	9386	55.0%
Continuous fee-for-service Medicare Part A and B coverage for at least 12 months or until death after the index diagnosis date	9162	97.6%
Continuous fee-for-service Medicare Part D coverage for at least 12 months or until death after the index diagnosis date	9080	99.1%
≥66 years old on index date	8475	93.3%
No evidence of ≥1 diagnoses of MCL in the 12 months before the index diagnosis date (i.e. to ensure newly diagnosed MCL patients)	6105	72.0%
No evidence of any anticancer treatment for at least 12 months before the index diagnosis date	4787	78.4%
Evidence of MCL drug treatment (full regimen, partial regimen, single agent) at any point in the post-index follow-up. Note that patients who end continuous FFS PDP coverage before starting 1 L treatment were excluded (n < 11 patients).	3664	76.5%

Appendix A7.

Treatment regimens

Table

1L+				2L+				3L+				4L+
Treatment Group	Regimen	N	%	Treatment Group	Regimen	N	%	Treatment Group	Regimen	N	%	Treatment Group	Regimen	N	%
Bendamustine-based Regimen	bend*ritu*	1836	50.1%	BTKi	ibru*	428	29.0%	BTKi	ibru*	139	21.4%	BTKi	ibru*	72	25.4%
CHOP-based Regimen	cycl*doxo*ritu*vinc*	523	14.3%	Bendamustine-based Regimen	bend*ritu*	269	18.2%	Bendamustine-based Regimen	bend*ritu*	71	10.9%	Bendamustine-based Regimen	bend*ritu*	25	8.8%
CD20 Monotherapy	ritu*	342	9.3%	Bortezomib-based Regimen	bort*	74	5.0%	Bortezomib-based Regimen	bort*	53	8.2%	Lenalidomide-based regimen	lena*	22	7.7%
BTKi	ibru*	108	2.9%	Bortezomib-based Regimen	bort*ritu*	62	4.2%	Lenalidomide-based regimen	lena*	40	6.2%	Bortezomib-based Regimen	bort*	17	6.0%
CVP-based Regimen	cycl*ritu*vinc*	96	2.6%	BTKi	acal*	56	3.8%	Bortezomib-based Regimen	bort*ritu*	36	5.5%	Lenalidomide-based regimen	lena*ritu*	17	6.0%
Bendamustine-based Regimen	bend*	92	2.5%	BTKi	ibru*ritu*	52	3.5%	Lenalidomide-based regimen	lena*ritu*	32	4.9%	BTKi	acal*	12	4.2%
Bortezomib-based Regimen	bort*ritu*	54	1.5%	CHOP-based Regimen	cycl*doxo*ritu*vinc*	50	3.4%	BTKi	acal*	28	4.3%	Bortezomib-based Regimen	bort*ritu*	11	3.9%
Bortezomib-based Regimen	bort*cycl*doxo*ritu*	45	1.2%	Lenalidomide-based regimen	lena*	35	2.4%	Bendamustine-based Regimen	bend*	19	2.9%	Other Regimens	vene*	11	3.9%
Lenalidomide-based regimen	lena*ritu*	37	1.0%	Lenalidomide-based regimen	lena*ritu*	29	2.0%	Other Regimens	vene*	17	2.6%	CHOP-based Regimen	cycl*doxo*ritu*vinc*	n < 11	<3.9%
BTKi	ibru*ritu*	27	0.7%	Bendamustine-based Regimen	bend*	27	1.8%	CHOP-based Regimen	cycl*doxo*ritu*vinc*	16	2.5%	CHOP-based Regimen	cycl*doxo*etop*lena*vinc*	n < 11	<3.9%
Other Regimens	chem*ritu*	26	0.7%	Other Regimens	chem*	21	1.4%	BTKi	ibru*ritu*	16	2.5%	CHOP-based Regimen	cycl*doxo*meth*ritu*vinc*	n < 11	<3.9%
Bendamustine-based Regimen	bend*cyta*ritu*	25	0.7%	Other Regimens	chem*ritu*	21	1.4%	CHOP-based Regimen	cycl*doxo*vinc*	n < 11	<1.7%	CVP-based Regimen	bort*cycl*etop*vinc*	n < 11	<3.9%
Other Regimens	vinc*	25	0.7%	Other Regimens	meth*	17	1.2%	CHOP-based Regimen	acal*cycl*doxo*vinc*	n < 11	<1.7%	CVP-based Regimen	cycl*gemc*vinc*	n < 11	<3.9%
Bendamustine-based Regimen	bend*bort*ritu*	23	0.6%	Bendamustine-based Regimen	bend*bort*ritu*	13	0.9%	CHOP-based Regimen	cycl*doxo*etop*mito*ritu*vinc*	n < 11	<1.7%	CVP-based Regimen	cycl*ibru*ritu*vinc*	n < 11	<3.9%
Bortezomib-based Regimen	bort*	22	0.6%	Bendamustine-based Regimen	bend*chem*ritu*	13	0.9%	CHOP-based Regimen	cycl*doxo*etop*ritu*vinc*	n < 11	<1.7%	CVP-based Regimen	cycl*vene*vinc*	n < 11	<3.9%
Other Regimens	clad*ritu*	22	0.6%	Other Regimens	nivo*	11	0.7%	CHOP-based Regimen	cycl*doxo*lena*ritu*vinc*	n < 11	<1.7%	CVP-based Regimen	cycl*vinc*	n < 11	<3.9%
Bendamustine-based Regimen	bend*chem*ritu*	20	0.5%	CHOP-based Regimen	cycl*doxo*vinc*	n < 11	<0.7%	CHOP-based Regimen	cycl*doxo*meth*ritu*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*vinc*	17	0.5%	CHOP-based Regimen	cycl*doxo*meth*ritu*vinc*	n < 11	<0.7%	CHOP-based Regimen	cycl*doxo*ritu*vene*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*ibru*ritu*	n < 11	<3.9%
Other Regimens	chlo*	14	0.4%	CHOP-based Regimen	bort*cycl*doxo*ritu*vinc*	n < 11	<0.7%	CHOP-based Regimen	cycl*doxo*vene*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*bort*ritu*	n < 11	<3.9%
Lenalidomide-based regimen	lena*	13	0.4%	CHOP-based Regimen	cisp*cycl*doxo*ritu*vinc*	n < 11	<0.7%	CVP-based Regimen	cycl*ritu*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*chem*	n < 11	<3.9%
Other Regimens	cycl*ritu*	13	0.4%	CHOP-based Regimen	cycl*cyta*doxo*ritu*vinc*	n < 11	<0.7%	CVP-based Regimen	cycl*etop*ritu*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*cycl*flud*ibru*ritu*	n < 11	<3.9%
Other Regimens	carb*	12	0.3%	CHOP-based Regimen	cycl*doxo*ritu*vinb*vinc*	n < 11	<0.7%	CVP-based Regimen	bend*cycl*ritu*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*cyta*ritu*	n < 11	<3.9%
CHOP-based Regimen	bend*cycl*doxo*ritu*vinc*	11	0.3%	CHOP-based Regimen	bend*cycl*doxo*vinc*	n < 11	<0.7%	CVP-based Regimen	bort*cycl*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*gemc*oxal*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*meth*ritu*vinc*	n < 11	<0.3%	CHOP-based Regimen	bort*cycl*doxo*etop*ritu*vinc*	n < 11	<0.7%	CVP-based Regimen	cycl*epir*ritu*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*ibru*	n < 11	<3.9%
CHOP-based Regimen	cycl*cyta*doxo*ritu*vinc*	n < 11	<0.3%	CHOP-based Regimen	bort*cycl*doxo*vinc*	n < 11	<0.7%	CVP-based Regimen	cycl*mito*ritu*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*ibru*obin*	n < 11	<3.9%
CHOP-based Regimen	bort*cycl*doxo*ritu*vinc*	n < 11	<0.3%	CHOP-based Regimen	chem*cycl*doxo*meth*ritu*vinc*	n < 11	<0.7%	CVP-based Regimen	cycl*mito*vinc*	n < 11	<1.7%	Bendamustine-based Regimen	bend*lena*meth*ritu*	n < 11	<3.9%
CHOP-based Regimen	chem*cycl*doxo*ritu*vinc*	n < 11	<0.3%	CHOP-based Regimen	cisp*cycl*cyta*doxo*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*bort*ritu*	n < 11	<1.7%	Gemcitabine-based Regimen	gemc*	n < 11	<3.9%
CHOP-based Regimen	cisp*cycl*cyta*doxo*ritu*vinc*	n < 11	<0.3%	CHOP-based Regimen	cycl*doxo*etop*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*ibru*ritu*	n < 11	<1.7%	Gemcitabine-based Regimen	gemc*oxal*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*etop*ritu*vinc*	n < 11	<0.3%	CHOP-based Regimen	cycl*doxo*ibru*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*chem*	n < 11	<1.7%	Gemcitabine-based Regimen	bort*gemc*obin*oxal*	n < 11	<3.9%
CHOP-based Regimen	clad*cycl*doxo*ritu*vinc*	n < 11	<0.3%	CHOP-based Regimen	cycl*doxo*obin*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*cyta*meth*ritu*	n < 11	<1.7%	Gemcitabine-based Regimen	cycl*flud*gemc*oxal*ritu*	n < 11	<3.9%
CHOP-based Regimen	cycl*cyta*doxo*flud*ritu*vinc*	n < 11	<0.3%	CHOP-based Regimen	cycl*doxo*ritu*vinc*vino*	n < 11	<0.7%	Bendamustine-based Regimen	bend*bort*	n < 11	<1.7%	Gemcitabine-based Regimen	gemc*meth*	n < 11	<3.9%
CHOP-based Regimen	cycl*cyta*doxo*meth*ritu*vinc*	n < 11	<0.3%	CVP-based Regimen	cycl*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*bort*lena*ritu*	n < 11	<1.7%	Gemcitabine-based Regimen	gemc*ritu*vino*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*etop*meth*ritu*vinc*	n < 11	<0.3%	CVP-based Regimen	cycl*etop*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*bort*meth*ritu*	n < 11	<1.7%	BTKi	ibru*ritu*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*etop*vinc*	n < 11	<0.3%	CVP-based Regimen	cycl*mito*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*carb*cyta*ritu*	n < 11	<1.7%	BTKi	ibru*vene*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*gemc*ritu*vinc*	n < 11	<0.3%	CVP-based Regimen	cycl*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*chem*meth*	n < 11	<1.7%	BTKi	ibru*lena*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*ibru*lena*ritu*vinc*	n < 11	<0.3%	CVP-based Regimen	bend*cycl*obin*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*chem*ritu*	n < 11	<1.7%	BTKi	acal*bort*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*ibru*ritu*vinc*	n < 11	<0.3%	CVP-based Regimen	bort*cycl*mito*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*cyta*	n < 11	<1.7%	BTKi	acal*ibru*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*lena*ritu*vinc*	n < 11	<0.3%	CVP-based Regimen	cycl*etop*meth*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*cyta*ritu*	n < 11	<1.7%	BTKi	acal*ritu*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*oxal*ritu*vinc*	n < 11	<0.3%	CVP-based Regimen	cycl*etop*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*ibru*	n < 11	<1.7%	BTKi	bort*cyta*ibru*meth*	n < 11	<3.9%
CHOP-based Regimen	cycl*doxo*ritu*vinb*vinc*	n < 11	<0.3%	CVP-based Regimen	cycl*ibru*ritu*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*lena*	n < 11	<1.7%	BTKi	bort*ibru*	n < 11	<3.9%
CVP-based Regimen	cycl*mito*ritu*vinc*	n < 11	<0.3%	CVP-based Regimen	cycl*pemb*vinc*	n < 11	<0.7%	Bendamustine-based Regimen	bend*lena*obin*	n < 11	<1.7%	BTKi	bort*ibru*ritu*	n < 11	<3.9%
CVP-based Regimen	cycl*etop*ritu*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*chem*	n < 11	<0.7%	Bendamustine-based Regimen	bend*meth*	n < 11	<1.7%	BTKi	carb*etop*ibru*ifos*ritu*	n < 11	<3.9%
CVP-based Regimen	cycl*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*ibru*ritu*	n < 11	<0.7%	Bendamustine-based Regimen	bend*meth*ritu*	n < 11	<1.7%	BTKi	ibru*nivo*	n < 11	<3.9%
CVP-based Regimen	bend*cycl*ritu*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*bort*	n < 11	<0.7%	Bendamustine-based Regimen	bend*tems*	n < 11	<1.7%	BTKi	ibru*oxal*	n < 11	<3.9%
CVP-based Regimen	cycl*etop*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*cyta*meth*ritu*	n < 11	<0.7%	Bendamustine-based Regimen	bend*vene*	n < 11	<1.7%	Bortezomib-based Regimen	bort*lena*ritu*	n < 11	<3.9%
CVP-based Regimen	bend*cycl*mito*ritu*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*meth*ritu*	n < 11	<0.7%	Gemcitabine-based Regimen	gemc*oxal*ritu*	n < 11	<1.7%	Bortezomib-based Regimen	bort*cycl*doxo*	n < 11	<3.9%
CVP-based Regimen	bend*cycl*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*bort*gemc*oxal*ritu*	n < 11	<0.7%	Gemcitabine-based Regimen	carb*gemc*	n < 11	<1.7%	Bortezomib-based Regimen	bort*cycl*doxo*ritu*	n < 11	<3.9%
CVP-based Regimen	bort*cycl*ritu*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*carb*ritu*vinc*	n < 11	<0.7%	Gemcitabine-based Regimen	cisp*gemc*ritu*	n < 11	<1.7%	Bortezomib-based Regimen	bort*lena*	n < 11	<3.9%
CVP-based Regimen	cycl*epir*ritu*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*cycl*ritu*	n < 11	<0.7%	Gemcitabine-based Regimen	gemc*	n < 11	<1.7%	Lenalidomide-based regimen	lena*vene*	n < 11	<3.9%
CVP-based Regimen	cycl*mito*vinc*	n < 11	<0.3%	Bendamustine-based Regimen	bend*cyta*	n < 11	<0.7%	Gemcitabine-based Regimen	gemc*ibru*lena*vene*	n < 11	<1.7%	Lenalidomide-based regimen	lena*meth*ritu*	n < 11	<3.9%
Bendamustine-based Regimen	bend*meth*ritu*	n < 11	<0.3%	Bendamustine-based Regimen	bend*cyta*etop*	n < 11	<0.7%	Gemcitabine-based Regimen	gemc*oxal*	n < 11	<1.7%	Lenalidomide-based regimen	lena*obin*	n < 11	<3.9%
Bendamustine-based Regimen	bend*ibru*ritu*	n < 11	<0.3%	Bendamustine-based Regimen	bend*cyta*ofat*	n < 11	<0.7%	Gemcitabine-based Regimen	gemc*oxal*ritu*vene*	n < 11	<1.7%	Lenalidomide-based regimen	lena*obin*ritu*	n < 11	<3.9%
Bendamustine-based Regimen	bend*bort*	n < 11	<0.3%	Bendamustine-based Regimen	bend*cyta*ritu*	n < 11	<0.7%	BTKi	ibru*vene*	n < 11	<1.7%	Lenalidomide-based regimen	lena*ritu*tems*	n < 11	<3.9%
Bendamustine-based Regimen	bend*cyta*meth*ritu*	n < 11	<0.3%	Bendamustine-based Regimen	bend*gemc*ritu*	n < 11	<0.7%	BTKi	acal*vene*	n < 11	<1.7%	Cytarabine-based Regimen	cycl*cyta*ritu*	n < 11	<3.9%
Bendamustine-based Regimen	bend*cycl*ritu*	n < 11	<0.3%	Bendamustine-based Regimen	bend*ibru*	n < 11	<0.7%	BTKi	bort*ibru*	n < 11	<1.7%	Cytarabine-based Regimen	cyta*	n < 11	<3.9%
Bendamustine-based Regimen	bend*cyta*	n < 11	<0.3%	Bendamustine-based Regimen	bend*ibru*lena*ritu*	n < 11	<0.7%	BTKi	ibru*lena*	n < 11	<1.7%	Cytarabine-based Regimen	cyta*ritu*	n < 11	<3.9%
Bendamustine-based Regimen	acal*bend*ritu*	n < 11	<0.3%	Bendamustine-based Regimen	bend*lena*ritu*	n < 11	<0.7%	BTKi	ibru*lena*ritu*	n < 11	<1.7%	Other Regimens	carb*	n < 11	<3.9%
Bendamustine-based Regimen	bend*bort*cyta*ritu*	n < 11	<0.3%	Bendamustine-based Regimen	bend*obin*	n < 11	<0.7%	BTKi	ibru*meth*	n < 11	<1.7%	Other Regimens	carb*etop*	n < 11	<3.9%
Bendamustine-based Regimen	bend*chem*	n < 11	<0.3%	Bendamustine-based Regimen	bend*ritu*tems*	n < 11	<0.7%	BTKi	acal*chem*	n < 11	<1.7%	Other Regimens	cycl*flud*	n < 11	<3.9%
Bendamustine-based Regimen	bend*chem*cyta*ritu*	n < 11	<0.3%	Gemcitabine-based Regimen	gemc*	n < 11	<0.7%	BTKi	acal*ritu*vene*	n < 11	<1.7%	Other Regimens	idel*	n < 11	<3.9%
Bendamustine-based Regimen	bend*ibru*	n < 11	<0.3%	Gemcitabine-based Regimen	cisp*gemc*	n < 11	<0.7%	BTKi	bort*cycl*doxo*ibru*ritu*	n < 11	<1.7%	Other Regimens	obin*	n < 11	<3.9%
Bendamustine-based Regimen	bend*ibru*meth*ritu*	n < 11	<0.3%	Gemcitabine-based Regimen	carb*gemc*	n < 11	<0.7%	BTKi	bort*ibru*ritu*	n < 11	<1.7%	Other Regimens	obin*vene*	n < 11	<3.9%
Bendamustine-based Regimen	bend*obin*	n < 11	<0.3%	Gemcitabine-based Regimen	bort*gemc*oxal*ritu*	n < 11	<0.7%	BTKi	carb*ibru*	n < 11	<1.7%	Other Regimens	vinc*	n < 11	<3.9%
Bendamustine-based Regimen	bend*pemb*ritu*	n < 11	<0.3%	Gemcitabine-based Regimen	gemc*oxal*	n < 11	<0.7%	BTKi	cycl*flud*ibru*ritu*	n < 11	<1.7%	Other Regimens	carb*chem*	n < 11	<3.9%
Bendamustine-based Regimen	bend*ritu*vinc*	n < 11	<0.3%	Gemcitabine-based Regimen	gemc*ritu*	n < 11	<0.7%	BTKi	cyta*ibru*	n < 11	<1.7%	Other Regimens	chem*	n < 11	<3.9%
Gemcitabine-based Regimen	gemc*	n < 11	<0.3%	BTKi	ibru*vene*	n < 11	<0.7%	BTKi	ibru*ofat*	n < 11	<1.7%	Other Regimens	chem*obin*	n < 11	<3.9%
Gemcitabine-based Regimen	gemc*oxal*ritu*	n < 11	<0.3%	BTKi	bort*ibru*	n < 11	<0.7%	Bortezomib-based Regimen	bort*lena*	n < 11	<1.7%	Other Regimens	chem*ritu*	n < 11	<3.9%
Gemcitabine-based Regimen	carb*gemc*	n < 11	<0.3%	BTKi	acal*ritu*	n < 11	<0.7%	Bortezomib-based Regimen	bort*cycl*doxo*ritu*	n < 11	<1.7%	Other Regimens	clad*ritu*	n < 11	<3.9%
Gemcitabine-based Regimen	carb*gemc*ifos*ritu*	n < 11	<0.3%	BTKi	bort*ibru*ritu*	n < 11	<0.7%	Bortezomib-based Regimen	bort*cycl*ritu*	n < 11	<1.7%	Other Regimens	cycl*	n < 11	<3.9%
Gemcitabine-based Regimen	cisp*gemc*ritu*	n < 11	<0.3%	BTKi	acal*vene*	n < 11	<0.7%	Bortezomib-based Regimen	bort*cycl*	n < 11	<1.7%	Other Regimens	cycl*flud*mito*ritu*	n < 11	<3.9%
Gemcitabine-based Regimen	doxo*gemc*meth*ritu*vino*	n < 11	<0.3%	BTKi	ibru*lena*	n < 11	<0.7%	Bortezomib-based Regimen	bort*cycl*flud*mito*ritu*	n < 11	<1.7%	Other Regimens	cycl*flud*ritu*	n < 11	<3.9%
BTKi	acal*	n < 11	<0.3%	BTKi	ibru*lena*ritu*	n < 11	<0.7%	Bortezomib-based Regimen	bort*cycl*flud*ritu*	n < 11	<1.7%	Other Regimens	cycl*ritu*	n < 11	<3.9%
BTKi	acal*ritu*	n < 11	<0.3%	BTKi	acal*ibru*	n < 11	<0.7%	Bortezomib-based Regimen	bort*lena*ritu*	n < 11	<1.7%	Other Regimens	doxo*vinb*	n < 11	<3.9%
BTKi	ibru*lena*ritu*	n < 11	<0.3%	BTKi	cyta*ibru*meth*	n < 11	<0.7%	Bortezomib-based Regimen	bort*ritu*vene*	n < 11	<1.7%	Other Regimens	idel*ritu*	n < 11	<3.9%
BTKi	acal*cycl*doxo*	n < 11	<0.3%	BTKi	acal*bort*	n < 11	<0.7%	Lenalidomide-based regimen	cyta*lena*ritu*	n < 11	<1.7%	Other Regimens	meth*	n < 11	<3.9%
BTKi	acal*ibru*	n < 11	<0.3%	BTKi	acal*obin*	n < 11	<0.7%	Lenalidomide-based regimen	lena*obin*	n < 11	<1.7%	Other Regimens	meth*nivo*	n < 11	<3.9%
BTKi	acal*lena*ritu*	n < 11	<0.3%	BTKi	chem*ibru*	n < 11	<0.7%	Lenalidomide-based regimen	lena*vene*	n < 11	<1.7%	Other Regimens	pemb*	n < 11	<3.9%
BTKi	bort*ibru*ritu*	n < 11	<0.3%	BTKi	chlo*ibru*ritu*	n < 11	<0.7%	Ifosfamide-based Regimen	carb*etop*ifos*	n < 11	<1.7%	Other Regimens	proc*ritu*	n < 11	<3.9%
BTKi	chem*ibru*ritu*	n < 11	<0.3%	BTKi	cycl*doxo*ibru*ritu*	n < 11	<0.7%	Cytarabine-based Regimen	carm*cyta*etop*	n < 11	<1.7%	Other Regimens	ritu*vene*	n < 11	<3.9%
BTKi	chlo*ibru*	n < 11	<0.3%	BTKi	cyta*ibru*ritu*	n < 11	<0.7%	Cytarabine-based Regimen	cisp*cyta*etop*ritu*	n < 11	<1.7%
BTKi	ibru*meth*	n < 11	<0.3%	BTKi	ibru*idel*	n < 11	<0.7%	Cytarabine-based Regimen	cycl*cyta*	n < 11	<1.7%
Bortezomib-based Regimen	bort*cycl*ritu*	n < 11	<0.3%	BTKi	ibru*lena*obin*	n < 11	<0.7%	Cytarabine-based Regimen	cyta*	n < 11	<1.7%
Bortezomib-based Regimen	bort*clad*ritu*	n < 11	<0.3%	BTKi	ibru*meth*	n < 11	<0.7%	Cytarabine-based Regimen	cyta*etop*meth*ritu*	n < 11	<1.7%
Bortezomib-based Regimen	bort*cycl*doxo*meth*ritu*	n < 11	<0.3%	BTKi	ibru*meth*ritu*	n < 11	<0.7%	Cytarabine-based Regimen	cyta*meth*	n < 11	<1.7%
Bortezomib-based Regimen	bort*cycl*doxo*	n < 11	<0.3%	BTKi	ibru*obin*	n < 11	<0.7%	Cytarabine-based Regimen	cyta*oxal*ritu*	n < 11	<1.7%
Bortezomib-based Regimen	bort*lena*	n < 11	<0.3%	Bortezomib-based Regimen	bort*cycl*doxo*ritu*	n < 11	<0.7%	Other Regimens	carb*etop*	n < 11	<1.7%
Bortezomib-based Regimen	bort*carb*ritu*	n < 11	<0.3%	Bortezomib-based Regimen	bort*cycl*ritu*	n < 11	<0.7%	Other Regimens	cycl*	n < 11	<1.7%
Bortezomib-based Regimen	bort*cycl*	n < 11	<0.3%	Bortezomib-based Regimen	bort*lena*	n < 11	<0.7%	Other Regimens	meth*	n < 11	<1.7%
Bortezomib-based Regimen	bort*cyta*meth*ritu*	n < 11	<0.3%	Bortezomib-based Regimen	bort*lena*ritu*	n < 11	<0.7%	Other Regimens	nivo*	n < 11	<1.7%
Bortezomib-based Regimen	bort*doxo*	n < 11	<0.3%	Bortezomib-based Regimen	bort*chem*ritu*	n < 11	<0.7%	Other Regimens	chem*	n < 11	<1.7%
Ifosfamide-based Regimen	carb*etop*ifos*ritu*	n < 11	<0.3%	Bortezomib-based Regimen	bort*cyta*	n < 11	<0.7%	Other Regimens	cycl*flud*	n < 11	<1.7%
Cytarabine-based Regimen	cyta*	n < 11	<0.3%	Bortezomib-based Regimen	bort*doxo*	n < 11	<0.7%	Other Regimens	oxal*	n < 11	<1.7%
Cytarabine-based Regimen	cycl*cyta*	n < 11	<0.3%	Bortezomib-based Regimen	bort*doxo*lena*	n < 11	<0.7%	Other Regimens	pemb*	n < 11	<1.7%
Cytarabine-based Regimen	cyta*etop*meth*ritu*	n < 11	<0.3%	Bortezomib-based Regimen	bort*meth*	n < 11	<0.7%	Other Regimens	chem*ritu*	n < 11	<1.7%
Cytarabine-based Regimen	cyta*meth*	n < 11	<0.3%	Bortezomib-based Regimen	bort*vinb*	n < 11	<0.7%	Other Regimens	chlo*	n < 11	<1.7%
Cytarabine-based Regimen	cyta*meth*ritu*	n < 11	<0.3%	Lenalidomide-based regimen	cyta*lena*meth*	n < 11	<0.7%	Other Regimens	cisp*	n < 11	<1.7%
Cytarabine-based Regimen	cyta*oxal*ritu*	n < 11	<0.3%	Ifosfamide-based Regimen	carb*etop*ifos*ritu*	n < 11	<0.7%	Other Regimens	cycl*flud*mito*ritu*	n < 11	<1.7%
Cytarabine-based Regimen	cyta*ritu*	n < 11	<0.3%	Ifosfamide-based Regimen	carb*etop*ifos*	n < 11	<0.7%	Other Regimens	cycl*flud*ritu*	n < 11	<1.7%
Cytarabine-based Regimen	cyta*vinc*	n < 11	<0.3%	Cytarabine-based Regimen	cyta*	n < 11	<0.7%	Other Regimens	doxo*	n < 11	<1.7%
Other Regimens	cycl*doxo*ritu*	n < 11	<0.3%	Cytarabine-based Regimen	carm*cyta*etop*	n < 11	<0.7%	Other Regimens	idel*	n < 11	<1.7%
Other Regimens	chlo*ritu*	n < 11	<0.3%	Cytarabine-based Regimen	cyta*ritu*	n < 11	<0.7%	Other Regimens	obin*vene*	n < 11	<1.7%
Other Regimens	ritu*vinc*	n < 11	<0.3%	Cytarabine-based Regimen	carb*cyta*ritu*	n < 11	<0.7%	Other Regimens	cisp*etop*	n < 11	<1.7%
Other Regimens	meth*	n < 11	<0.3%	Cytarabine-based Regimen	carm*cyta*etop*melp*	n < 11	<0.7%	Other Regimens	clad*	n < 11	<1.7%
Other Regimens	cycl*flud*ritu*	n < 11	<0.3%	Cytarabine-based Regimen	carm*cyta*etop*melp*meth*	n < 11	<0.7%	Other Regimens	clad*ritu*	n < 11	<1.7%
Other Regimens	doxo*ritu*vinc*	n < 11	<0.3%	Cytarabine-based Regimen	carm*cyta*etop*melp*ritu*	n < 11	<0.7%	Other Regimens	cycl*doxo*ritu*	n < 11	<1.7%
Other Regimens	meth*ritu*	n < 11	<0.3%	Cytarabine-based Regimen	clad*cyta*etop*meth*ritu*	n < 11	<0.7%	Other Regimens	cycl*doxo*ritu*vino*	n < 11	<1.7%
Other Regimens	carb*etop*	n < 11	<0.3%	Cytarabine-based Regimen	cycl*cyta*	n < 11	<0.7%	Other Regimens	cycl*ritu*	n < 11	<1.7%
Other Regimens	cycl*	n < 11	<0.3%	Cytarabine-based Regimen	cycl*cyta*doxo*etop*meth*ritu*	n < 11	<0.7%	Other Regimens	duve*	n < 11	<1.7%
Other Regimens	doxo*	n < 11	<0.3%	Cytarabine-based Regimen	cycl*cyta*ritu*	n < 11	<0.7%	Other Regimens	flud*mito*	n < 11	<1.7%
Other Regimens	flud*ritu*	n < 11	<0.3%	Cytarabine-based Regimen	cyta*meth*	n < 11	<0.7%	Other Regimens	mech*	n < 11	<1.7%
Other Regimens	oxal*	n < 11	<0.3%	Cytarabine-based Regimen	cyta*oxal*ritu*	n < 11	<0.7%	Other Regimens	meth*vinc*	n < 11	<1.7%
Other Regimens	pemb*	n < 11	<0.3%	Other Regimens	carb*	n < 11	<0.7%	Other Regimens	obin*	n < 11	<1.7%
Other Regimens	carb*pemb*	n < 11	<0.3%	Other Regimens	oxal*	n < 11	<0.7%	Other Regimens	ofat*	n < 11	<1.7%
Other Regimens	chlo*obin*	n < 11	<0.3%	Other Regimens	vene*	n < 11	<0.7%	Other Regimens	ritu*vinc*	n < 11	<1.7%
Other Regimens	bleo*doxo*vinb*	n < 11	<0.3%	Other Regimens	cycl*	n < 11	<0.7%	Other Regimens	tems*	n < 11	<1.7%
Other Regimens	carb*ritu*	n < 11	<0.3%	Other Regimens	carb*etop*	n < 11	<0.7%	Other Regimens	vinc*	n < 11	<1.7%
Other Regimens	cisp*	n < 11	<0.3%	Other Regimens	chlo*	n < 11	<0.7%
Other Regimens	cisp*epir*	n < 11	<0.3%	Other Regimens	cisp*etop*	n < 11	<0.7%
Other Regimens	clad*ofat*	n < 11	<0.3%	Other Regimens	cycl*ritu*	n < 11	<0.7%
Other Regimens	cycl*doxo*	n < 11	<0.3%	Other Regimens	cisp*	n < 11	<0.7%
Other Regimens	cycl*doxo*ritu*vinb*	n < 11	<0.3%	Other Regimens	flud*	n < 11	<0.7%
Other Regimens	cycl*etop*proc*ritu*	n < 11	<0.3%	Other Regimens	pemb*	n < 11	<0.7%
Other Regimens	cycl*etop*ritu*	n < 11	<0.3%	Other Regimens	cycl*etop*	n < 11	<0.7%
Other Regimens	doxo*ritu*	n < 11	<0.3%	Other Regimens	cycl*flud*mito*ritu*	n < 11	<0.7%
Other Regimens	doxo*ritu*vinb*	n < 11	<0.3%	Other Regimens	etop*	n < 11	<0.7%
Other Regimens	flud*	n < 11	<0.3%	Other Regimens	ofat*	n < 11	<0.7%
Other Regimens	obin*	n < 11	<0.3%	Other Regimens	proc*	n < 11	<0.7%
Other Regimens	pent*	n < 11	<0.3%	Other Regimens	bleo*doxo*vinb*	n < 11	<0.7%
Other Regimens	thio*	n < 11	<0.3%	Other Regimens	busu*	n < 11	<0.7%
				Other Regimens	carb*pemb*	n < 11	<0.7%
				Other Regimens	carb*ritu*	n < 11	<0.7%
				Other Regimens	carm*cycl*etop*	n < 11	<0.7%
				Other Regimens	carm*ritu*	n < 11	<0.7%
				Other Regimens	chem*nivo*	n < 11	<0.7%
				Other Regimens	chlo*ritu*	n < 11	<0.7%
				Other Regimens	cycl*etop*ritu*	n < 11	<0.7%
				Other Regimens	cycl*flud*	n < 11	<0.7%
				Other Regimens	cycl*meth*	n < 11	<0.7%
				Other Regimens	cycl*pent*ritu*	n < 11	<0.7%
				Other Regimens	etop*ritu*	n < 11	<0.7%
				Other Regimens	idel*	n < 11	<0.7%
				Other Regimens	meth*ritu*	n < 11	<0.7%
				Other Regimens	meth*ritu*vinc*	n < 11	<0.7%
				Other Regimens	obin*ritu*vene*	n < 11	<0.7%
				Other Regimens	pent*	n < 11	<0.7%
				Other Regimens	tems*	n < 11	<0.7%
				Other Regimens	vinc*	n < 11	<0.7%

Appendix A8.

Healthcare costs among medicare beneficiaries with diagnosed MCL in the 12-months after initiation of the specific line of treatment (alive in the 12-month post-index period)

Table

	Front-line			Relapsed/refractory
	1L+			2L+			3L+			4L+
	N	%	Std Dev	N	%	Std Dev	N	%	Std Dev	N	%	Std Dev
All-cause costs
Total costs	2881	$143,786	$68,389	821	$151,936	$82,168	270	$164,623	$96,281	92	$184,813	$129,061
Medical costs	2881	$129,990	$64,069	821	$95,703	$73,100	270	$97,121	$81,956	92	$110,162	$117,211
Inpatient costs	2881	$17,573	$38,478	821	$17,352	$34,008	270	$20,519	$40,938	92	$36,616	$91,201
Inpatient costs among those with >1 hospitalization	1269	$39,895	$49,715	368	$38,712	$41,892	132	$41,971	$50,340	56	$60,155	$110,996
Skilled nursing facility (SNF) costs	2881	$1,402	$7,090	821	$1,246	$6,498	270	$1,695	$7,073	92	$1,836	$6,929
SNF costs among those with >1 SNF stay	202	$20,002	$18,613	54	$18,947	$17,654	25	$18,307	$15,620	13	$12,993	$14,390
Outpatient hospital costs	2881	$54,148	$56,621	821	$33,549	$47,046	270	$32,638	$50,219	92	$27,449	$33,324
Carrier claims costs	2881	$55,015	$56,364	821	$41,377	$50,398	270	$39,094	$46,392	92	$39,037	$49,239
Hospice costs	2881	$197	$2,712	821	$261	$2,821	270	$599	$4,978	92	$1,489	$6,887
Home health costs	2881	$1,215	$3,359	821	$1,406	$4,042	270	$1,938	$4,573	92	$3,175	$6,589
Durable medical equipment costs	2881	$441	$1,521	821	$512	$1,768	270	$637	$2,217	92	$560	$1,974
Pharmacy costs	2881	$13,796	$34,219	821	$56,233	$70,540	270	$67,502	$73,041	92	$74,650	$74,246
MCL-related costs
Total costs	2881	$119,662	$62,354	821	$127,820	$76,484	270	$137,761	$81,336	92	$155,865	$108,560
Medical costs	2881	$39,099	$47,426	821	$32,841	$43,618	270	$39,400	$55,688	92	$56,455	$100,529
Inpatient costs	2881	$14,133	$33,775	821	$13,629	$30,407	270	$17,092	$39,130	92	$33,725	$90,401
Inpatient costs among those with >1 hospitalization	1003	$40,595	$46,941	295	$37,931	$40,667	110	$41,952	$52,214	49	$63,320	$116,535
Skilled nursing facility (SNF) costs	2881	$550	$4,452	821	$627	$4,622	270	$938	$5,142	92	$797	$3,021
SNF costs among those with >1 SNF stay	94	$16,865	$18,325	32	$16,096	$17,550	14	$18,082	$14,606	<11	$9,166	$5,566
Outpatient hospital costs	2881	$13,285	$18,649	821	$9,270	$14,798	270	$10,450	$30,023	92	$8,781	$11,945
Carrier claims costs	2881	$10,442	$14,660	821	$8,399	$13,296	270	$9,205	$14,199	92	$10,104	$14,929
Hospice costs	2881	$77	$1,673	821	$163	$2,355	270	$583	$4,976	92	$753	$5,291
Home health costs	2881	$591	$2,056	821	$707	$2,801	270	$1,036	$2,914	92	$2,167	$5,521
Durable medical equipment costs	2881	$21	$197	821	$45	$431	270	$99	$1,206	92	$128	$828
Prescription drug costs	2881	$80,563	$43,173	821	$94,979	$64,615	270	$98,360	$64,138	92	$99,411	$59,141
Pharmacy costs (Part D drugs)	2881	$9,800	$32,529	821	$51,767	$69,123	270	$61,363	$68,177	92	$67,969	$63,147
Physician-administered drug costs (Part B drugs)	2881	$70,763	$37,319	821	$43,213	$44,376	270	$36,997	$39,813	92	$31,441	$37,691