Introduction
Multiple myeloma (MM) is a hematological malignancy resulting from the neoplastic proliferation of plasma cells [ 1 ]. Globally, MM comprises approximately 10% of hematological malignancies [ 2 ]. In 2020, approximately 8200 new cases of MM and 4500 mm-related deaths were recorded in Japan [ 3 ].
Patients with MM typically have poor health-related quality of life (HRQoL) due to the high burden of disease and tend to experience more disease-related symptoms than that observed in other hematological cancers, including painful bone destruction, bone marrow failure, renal failure, immunodeficiency, and body shape changes [ 4 ]. Furthermore, therapeutic interventions may themselves have adverse effects and lead to functional impairments [ 4 ]. While novel treatments have improved prognosis, there is limited evidence of their impact on HRQoL. Some novel treatments are also associated with adverse events, impacting the gastrointestinal system as well as causing fatigue, sleep disturbances, and allergic reactions. The treatment burden, from the infusion duration to the treatment interfering with daily activities, also impacts HRQoL [ 5–7 ]. There is therefore a need for regular assessment of HRQoL and functional status in the clinical management of patients, which may improve treatment outcomes and compliance [ 8 , 9 ].
The approval of proteasome inhibitors, immunomodulatory drugs, and anti-CD38 monoclonal antibodies (mAbs), such as daratumumab and isatuximab, has transformed the treatment landscape for MM in Japan [ 10 , 11 ]. Such agents have been defined as “key drugs” by the Japanese Society of Hematology (JSH) [ 1 ]. Initial treatment with key drugs, such as lenalidomide, daratumumab, or bortezomib, has led to higher overall response rate (ORR) and prolonged median time to subsequent treatment in patients with RRMM in Japan [ 12 ]. Furthermore, treatment with these key agents significantly improved overall survival (OS) compared with conventional chemotherapy (median OS 66.8 months vs. 39.4 months, p < 0.00001) [ 13 ].
Highly effective triplet and quadruplet regimens incorporating a mAb, lenalidomide, and/or bortezomib are now recommended frontline treatments for some patients by American, European, and Japanese guidelines [ 1 , 2 , 14 ]. Bortezomib + lenalidomide + dexamethasone (VRd) and daratumumab + bortezomib + melphalan + prednisolone (DVMp) are recommended as first-line treatment in American Society of Clinical Oncology and Cancer Care Ontario joint clinical practice guideline, for treatment of transplant-ineligible patients [ 14 ]. In the latest edition of the Japan Practical Guideline for Hematological Malignancies from the JSH, DVMp, and daratumumab + lenalidomide + dexamethasone (DRd) are recommended as first-line (1 L) treatments for transplant-ineligible patients [ 15 ], while first-line use of VRd and bortezomib + thalidomide + dexamethasone (VTd) is common for transplant-eligible patients [ 1 , 2 ].
Despite the increasing number of available treatment options, new therapies with novel mechanisms of action are needed because MM frequently becomes refractory to available treatments due to the nature of triplet and quadruplet therapies in the frontline setting incorporating multiple agents [ 16 ]. Patients with relapsed MM who are refractory to a proteasome inhibitor and an immunomodulatory agent have particularly poor survival outcomes [ 6 ]. Strategies to overcome resistance/refractoriness include introducing a second generation proteasome inhibitor, such as carfilzomib or ixazomib, or switching to a different class of drugs with a novel mechanism, or introducing mAbs [ 17 ]. Some patients, such as those with late relapses ≥9–12 months after their last treatment, can be retreated with previously used treatments [ 1 ]. Retreatment includes sequencing different therapeutic regimens in patients with multiple relapses primarily with the drugs to which patients have become refractory [ 18 ]. Retreatment with daratumumab in combination with immunomodulatory drugs and proteasome inhibitors has been shown to overcome refractoriness to bortezomib and lenalidomide in the frontline setting [ 17 , 19 ]. However, there is a need to better understand the rationale for retreatment in the real-world setting in Japan, as there is no consensus for the optimal treatment sequence. Furthermore, the number of available options across different treatment lines has increased the complexity of decision-making for physicians when optimizing treatment choices for their patients, particularly as drugs are listed for reimbursement immediately after approval in Japan, increasing the number of available regimens available for consideration.
Real-world data can capture a more representative and wider patient population than randomized controlled trials, providing access to clinical evidence on patient outcomes and routine delivery of healthcare associated with the use of a medical product [ 20 ]. Since there is a lack of consensus for managing RRMM in Japan, real-world evidence can help to improve understanding of current treatment patterns and factors associated with treatment selection, as well as the effect of treatments on patients in routine clinical practice. However, there is a paucity of real-world evidence on treatment patterns in Japan. The Adelphi Real World (ARW) MM III Disease Specific Programme (DSP™) is a cross-sectional survey of hematologists and hemato-oncologists, with elements of retrospective data collection used for evidence generation related to MM treatment patterns, treatment sequencing, burden of illness from an integrated patient and clinician perspective. Data from the ARW MM III DSP were evaluated in this analysis to better understand the burden of disease (including symptom burden and quality of life [QoL]), treatment patterns, and treatment selection factors for patients with MM in Japan.
The primary objective of this analysis was to describe MM symptoms, patient-perceived health status, and QoL using patient reported outcome (PRO) tools in Japanese patients with MM. Secondary objectives included describing demographic and clinical characteristics of patients with MM at each line of treatment (LOT), treatment patterns in MM patients by LOT, and MM treatment sequencing and retreatment across LOTs.
Methods
Study design
In this study, data were drawn from the ARW MM III DSP, a cross-sectional, real-world survey. DSPs are large, independent, multi-subscriber data collection surveys of physicians and their patients presenting in a real-world clinical setting. The data analyzed for this study were collected from treating physicians in Japan between September 2022 and May 2023. A complete description of the DSP methodology has been previously published and validated [ 20–23 ].
The data source comprised data collected via three interlinking surveys: I) physician survey, an attitudinal survey on physician perceptions and treatment decisions; II) detailed patient record forms (PRFs), physician-completed immediately after a consultation, including patient history and treatment; III) patient self-completion forms (PSCs), a questionnaire voluntarily completed by patients whose information was recorded in the PRF. The PSC form was completed independently from the physician. Data from the physician surveys and PRFs were collected online; data from PSCs were collected via a pen and paper methodology.
Data collected included health status and QoL, patient demographics, clinical characteristics, treatment patterns and sequencing, and reasons for prescribing treatments.
Study population
DSPs comprise data from a geographically diverse sample of physicians recruited to participate in the survey by local fieldwork agents [ 20 ]. Physicians were eligible to participate in the ARW MM III DSP if they were a hematologist or hemato-oncologist based in Japan, who was personally responsible for treatment decisions and management of patients with RRMM, had seen a minimum of six patients with MM per month, and accepted all survey rules and responsibilities. A geographically representative sample of hematologists and hemato-oncologists was recruited by local fieldwork teams in Japan, who used publicly available lists. Invited physicians were not limited to large cities or practice centers.
Recruited physicians were invited to complete online PRFs for their next eight consecutive patients with MM, comprising two patients at each of the 1 L, second-line (2 L), third-line (3 L), and fourth-line (4 L) cohorts.
Patients were eligible to participate in the ARW MM III DSP if, at data collection, they were over the age of 18 years, had a physician-confirmed diagnosis of MM, and receiving active treatment for MM; patients were excluded if they were participating in a clinical trial at the time of data collection, or if they were receiving palliative care.
The data for this analysis was retrieved from the DSP database, a secondary data source; as the DSP is an independent research programme conducted with no pre-specified hypotheses, formal sample size calculations were not applicable for this study.
Data analysis
Treatment landscape was analyzed by descriptive statistics, through the analysis of treatment and retreatment patterns. Analyses were performed on the full Adelphi DSP database available. Descriptive analyses of key measures, including demographics and clinical characteristics, treatment and retreatment patterns, reasons for prescribing treatments, and patient-reported symptoms were undertaken. Categorical variables and ordinal variables were reported as frequencies and percentages and/or medians and interquartile ranges (IQRs) as appropriate for the individual variables. Continuous variables were presented as means and standard deviations, medians and IQRs, and score ranges. Variables were analyzed as observed, with no imputation of missing data or aggregation across questions. Patients with missing values were removed from all pieces of analysis where that variable was used; however, patients removed from one piece of analysis were still eligible for inclusion in other analyses. Thus, the base of patients for analysis could vary from variable to variable, and is reported separately for each analysis.
Data from several validated PRO instruments were analyzed to assess HRQoL. The EuroQol 5-dimension 3-level questionnaire (EQ-5D-3 L) utility score and Visual Analogue Scale (VAS) were used to evaluate overall HRQoL, including mobility, self-care, usual activities, pain/discomfort, and anxiety/depression [ 24 ]. The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC-QLQ) Core-30 (C30) evaluated cancer-specific HRQoL across five functional scales, three symptom scales (fatigue, pain, and nausea and vomiting), as well as a collection of single items assessing global health status and perceived financial impact of the disease [ 25 ]. The EORTC-QLQ of Myeloma Patients 20-item questionnaire (MY20) evaluated HRQoL in MM patients, including three multi-item scales to assess disease symptoms, side effects of treatment, future perspective, and one single item to assess body image [ 26 , 27 ]. The impact of MM on patients’ daily work and activity impairment was evaluated using the Work Productivity and Activity Impairment (WPAI) questionnaire [ 28 ].
All analyses were performed using Stata 17 software, version 17.0 (StataCorp, College Station, Texas, US) or IBM SPSS Data Collection Survey Reporter v7.5, or the latest available version at the time of analysis.
Ethics
The DSP survey was submitted to the Pearl Institutional Review Board (study protocol number AG8847) and received ethics exemption. Data collection was undertaken in line with the European Pharmaceutical Marketing Research Association guidelines. Each survey was performed in full accordance with relevant legislation at the time of data collection. All patients provided informed consent prior to completing the PSC. Physician and patient data were pseudo-anonymized by assigning a code to them when data were collected. Upon receipt by Adelphi Real World, data were pseudo-anonymized again to mitigate against tracing them back to the individual. Data were aggregated before being shared with GSK and/or for publication. Complete descriptions of the DSP methodology and their generalizability have been published [ 20–23 ].
Results
Physician and patient characteristics
Fifty-one physicians completed the survey, of whom 38 (75%) were hematologists and 13 (25%) were hem-oncologists. Thirty-six (71%) of these physicians were primarily practicing in a non-academic-based hospital and 14 (27%) were in an academic hospital or cancer center.
Overall, 309 patients with completed PRFs were analyzed in the study ( Table 1 ). The median age (IQR) in the total patient population was 75.0 (69.5–80.0) years; median (IQR) age was 75.0 (70.0–79.2), 73.5 (70.0–78.0), 74.5 (68.2–81.8), and 76.0 (69.0–81.0) years in the 1 L, 2 L, 3 L, and 4 L cohorts, respectively. Gender distribution was equal in the total population (50%; 155/309). In the 1 L and 2 L cohorts, the most common biological sex was male (57% and 53%, respectively); in the 3 L and 4 L cohorts, it was the least common (44% and 45%, respectively). Table 1. Baseline characteristics. Cohort Total 1 L 2 L 3 L 4 L+ N 309 90 76 72 71 Age (years) Median (IQR) 75.0 (70–80) 75.0 (70–79) 73.5 (70–80) 74.5 (68–82) 76.0 (69–81) Sex, n (%) Male 155 (50) 51 (57) 40 (53) 32 (44) 32 (45) Female 154 (50) 39 (43) 36 (47) 40 (56) 39 (55) Smoking status at the point of data collection, n (%) Never smoked 149 (48) 43 (48) 35 (46) 34 (47) 37 (52) Ex-smoker 86 (28) 32 (36) 24 (32) 14 (19) 16 (23) Current smoker 6 (2) 2 (2) 1 (1) 2 (3) 1 (1) Unknown 68 (22) 13 (14) 16 (21) 22 (31) 17 (24) Employment status at the point of data collection, n (%) Not working due to retirement 89 (29) 24 (27) 22 (29) 24 (33) 19 (27) Unemployed 86 (28) 27 (30) 22 (29) 16 (22) 21 (30) Homemaker 61 (20) 15 (17) 16 (21) 15 (21) 15 (21) Working full time 36 (12) 13 (14) 10 (13) 9 (12) 4 (6) Working part time 22 (7) 8 (9) 4 (5) 4 (6) 6 (8) Student 1 (<1) 0 (0) 0 (0) 0 (0) 1 (1) Unknown 14 (5) 3 (3) 2 (3) 4 (6) 5 (7) Time since initial diagnosis of MM (days) Median (IQR) 804 (213–1752) 148 (54–466) 780 (285–1716) 1123 (611–1960) 1644 (1107–2395) ECOG score performance status at diagnosis of MM, n (%) 0 97 (31) 33 (37) 24 (32) 18 (25) 22 (31) 1 114 (37) 41 (46) 23 (30) 27 (38) 23 (32) 2 66 (21) 9 (10) 15 (20) 20 (28) 22 (31) 3 18 (6) 4 (4) 8 (11) 3 (4) 3 (4) 4 8 (3) 2 (2) 2 (3) 3 (4) 1 (1) Unknown/not assessed 6 (2) 1 (1) 4 (5) 1 (1) 0 (0) MM stage at diagnosis, n (%) SMM 5 (2) 2 (2) 2 (3) 1 (1) 0 (0) Stage I 40 (13) 16 (18) 6 (8) 7 (10) 11 (15) Stage II 147 (48) 42 (47) 36 (47) 36 (50) 33 (46) Stage III 110 (36) 29 (32) 28 (37) 26 (36) 27 (38) Unknown/not assessed 7 (2) 1 (1) 4 (5) 2 (3) 0 (0) MM stage at data collection, n (%) Stage I 32 (10) 12 (13) 6 (8) 8 (11) 6 (8) Stage II 140 (45) 46 (51) 32 (42) 31 (43) 31 (44) Stage III 105 (34) 26 (29) 27 (36) 26 (36) 26 (37) Unknown/not assessed 32 (10) 6 (7) 11 (14) 7 (10) 8 (11) Cytogenetic risk (detection of del(17p), t(4;14), t(14;16) or gain 1q), n (%) High risk 46 (15) 11 (12) 10 (13) 13 (18) 12 (17) Standard risk 84 (27) 28 (31) 16 (21) 19 (26) 21 (30) Unknown 179 (58) 51 (57) 50 (66) 40 (56) 38 (54) 1 L: first line; 2 L: second line; 3 L: third line; 4 L: fourth line; ECOG: Eastern Cooperative Oncology Group; IQR: interquartile range; MM: multiple myeloma; SMM: smoldering multiple myeloma.
Table 1.
Baseline characteristics.
1 L: first line; 2 L: second line; 3 L: third line; 4 L: fourth line; ECOG: Eastern Cooperative Oncology Group; IQR: interquartile range; MM: multiple myeloma; SMM: smoldering multiple myeloma.
| Cohort | |||||
| Total | 1 L | 2 L | 3 L | 4 L+ | |
| N | 309 | 90 | 76 | 72 | 71 |
| Age (years) | |||||
| Median (IQR) | 75.0 (70–80) | 75.0 (70–79) | 73.5 (70–80) | 74.5 (68–82) | 76.0 (69–81) |
| Sex, n (%) | |||||
| Male | 155 (50) | 51 (57) | 40 (53) | 32 (44) | 32 (45) |
| Female | 154 (50) | 39 (43) | 36 (47) | 40 (56) | 39 (55) |
| Smoking status at the point of data collection, n (%) | |||||
| Never smoked | 149 (48) | 43 (48) | 35 (46) | 34 (47) | 37 (52) |
| Ex-smoker | 86 (28) | 32 (36) | 24 (32) | 14 (19) | 16 (23) |
| Current smoker | 6 (2) | 2 (2) | 1 (1) | 2 (3) | 1 (1) |
| Unknown | 68 (22) | 13 (14) | 16 (21) | 22 (31) | 17 (24) |
| Employment status at the point of data collection, n (%) | |||||
| Not working due to retirement | 89 (29) | 24 (27) | 22 (29) | 24 (33) | 19 (27) |
| Unemployed | 86 (28) | 27 (30) | 22 (29) | 16 (22) | 21 (30) |
| Homemaker | 61 (20) | 15 (17) | 16 (21) | 15 (21) | 15 (21) |
| Working full time | 36 (12) | 13 (14) | 10 (13) | 9 (12) | 4 (6) |
| Working part time | 22 (7) | 8 (9) | 4 (5) | 4 (6) | 6 (8) |
| Student | 1 (<1) | 0 (0) | 0 (0) | 0 (0) | 1 (1) |
| Unknown | 14 (5) | 3 (3) | 2 (3) | 4 (6) | 5 (7) |
| Time since initial diagnosis of MM (days) | |||||
| Median (IQR) | 804 (213–1752) | 148 (54–466) | 780 (285–1716) | 1123 (611–1960) | 1644 (1107–2395) |
| ECOG score performance status at diagnosis of MM, n (%) | |||||
| 0 | 97 (31) | 33 (37) | 24 (32) | 18 (25) | 22 (31) |
| 1 | 114 (37) | 41 (46) | 23 (30) | 27 (38) | 23 (32) |
| 2 | 66 (21) | 9 (10) | 15 (20) | 20 (28) | 22 (31) |
| 3 | 18 (6) | 4 (4) | 8 (11) | 3 (4) | 3 (4) |
| 4 | 8 (3) | 2 (2) | 2 (3) | 3 (4) | 1 (1) |
| Unknown/not assessed | 6 (2) | 1 (1) | 4 (5) | 1 (1) | 0 (0) |
| MM stage at diagnosis, n (%) | |||||
| SMM | 5 (2) | 2 (2) | 2 (3) | 1 (1) | 0 (0) |
| Stage I | 40 (13) | 16 (18) | 6 (8) | 7 (10) | 11 (15) |
| Stage II | 147 (48) | 42 (47) | 36 (47) | 36 (50) | 33 (46) |
| Stage III | 110 (36) | 29 (32) | 28 (37) | 26 (36) | 27 (38) |
| Unknown/not assessed | 7 (2) | 1 (1) | 4 (5) | 2 (3) | 0 (0) |
| MM stage at data collection, n (%) | |||||
| Stage I | 32 (10) | 12 (13) | 6 (8) | 8 (11) | 6 (8) |
| Stage II | 140 (45) | 46 (51) | 32 (42) | 31 (43) | 31 (44) |
| Stage III | 105 (34) | 26 (29) | 27 (36) | 26 (36) | 26 (37) |
| Unknown/not assessed | 32 (10) | 6 (7) | 11 (14) | 7 (10) | 8 (11) |
| Cytogenetic risk (detection of del(17p), t(4;14), t(14;16) or gain 1q), n (%) | |||||
| High risk | 46 (15) | 11 (12) | 10 (13) | 13 (18) | 12 (17) |
| Standard risk | 84 (27) | 28 (31) | 16 (21) | 19 (26) | 21 (30) |
| Unknown | 179 (58) | 51 (57) | 50 (66) | 40 (56) | 38 (54) |
At the time of data collection, approximately half of all patients (48%; 149/309) did not have any smoking history (46–52% across the cohorts); 28% (86/309) were ex-smokers, 2% (6/309) were current smokers, and smoking status was unknown for 22% (68/309) of patients. Most patients were either retired (29%; 89/309), unemployed (28%; 86/309) or a homemaker (20%; 61/309).
Patient clinical characteristics
The median (IQR) time since initial diagnosis was 804.0 (212.5–1751.8) days in the total population; 148.0 (53.8–465.8), 780.5 (284.8–1715.5), 1123.0 (610.5–1960.0), and 1644.0 (1107.0–2395.0) days in the 1 L, 2 L, 3 L, and 4 L cohorts, respectively. Eastern Cooperative Oncology Group (ECOG) score categories of 0, 1, 2, and ≥3 at diagnosis were observed in 31% (97/309), 37% (114/309), 21% (66/309), and 8% (26/309) of the overall population, respectively. MM stage at diagnosis comprised 13% (40/309) stage I, 48% (147/309) stage II, and 36% (110/309) stage III. Of the total population, 2% (5/309) of patients had smoldering MM and 2% (7/309) had unknown stage at diagnosis. MM stage at data collection comprised 10% (32/309) stage I, 45% (140/309) stage II, 34% (105/309) stage III, and 10% (32/309) with unknown stage. Cytogenetic risk (defined as the detection of del(17p), t(4;14), t(14;16) or gain 1q [ 29 ]) was high in 15% (46/309), standard in 27% (84/309), and unknown in 58% (179/309) of the total population.
Patient reported symptoms, health status, and quality of life
Of the 309 patients with completed PRFs, 52 had matched PSC data (Table S1). The most commonly reported symptoms by patients at diagnosis were bone pain (46%; 24/52), lower back pain (44%; 23/52), shortness of breath (27%; 14/52), and fatigue (27%; 14/52). The most common reported symptoms by patients at data collection were lower back pain (29%; 15/52), fatigue (23%; 12/52), and bone pain (21%; 11/52).
The median (IQR) overall health status scores as assessed by EQ-5D-3L and EQ-5D-VAS were 0.7 (0.6–1.0) and 70 (56.2–70.0), respectively (Table S2). As assessed by EORTC QLQ-C30, the median (IQR) global health status score was 66.7 (50.0–75.0) and for financial difficulties 0 (0–33.3). The median (IQR) scores for the EORTC QLQ-C30 symptom domains were: fatigue, 33.3 (11.1–44.4); nausea and vomiting, 0 (0–16.7); pain, 16.7 (0–33.3); dyspnea, 33.3 (0–33.3); insomnia, 0 (0–33.3); appetite loss, 0 (0–33.3); constipation, 33.3 (0–33.3); diarrhea, 0 (0–33.3). As assessed using the EORTC-QLQ-MY20, the median (IQR) scores for the MY20 domains were: body image, 66.7 (66.7–100.0); future perspective 66.7 (44.4–77.8); disease symptoms, 12.2 (0–27.8); side effects of treatment, 12.2 (6.7–26.5). PRO scores were similar across lines of therapy (Table S2).
Treatment regimens
Overall, VRd was the most common first LOT (32%; 99/309), followed by bortezomib + dexamethasone (Vd) (17%; 54/309), lenalidomide + dexamethasone (Rd) (16%; 49/309), and DRd (16%; 49/309) ( Figure 1(a) ). In the 1 L cohort, the majority of patients received lenalidomide-based regimens (77%) as their first LOT and the most commonly used regimens were DRd (30%), VRd (29%), and Rd (14%) ( Figure 2(a) ). DRd was the first LOT in 30%, 20%, 7%, and 3% of patients in the 1 L, 2 L, 3 L, and 4 L cohorts, respectively. The use of VRd and Vd as the first LOT was highest in the 4 L cohort (31% and 25%, respectively). Other prescribed regimens are detailed in Table S3. Figure 1. The five most common treatment regimens in the total population across the first to fourth LOTs. Treatment regimens included in “other” are presented in Table S3. *6% for each regimen. 1 L: first line; 2 L: second line; 3 L: third line; 4 L: fourth line; DPd: daratumumab, pomalidomide, dexamethasone; DRd: daratumumab, lenalidomide, dexamethasone; DVd: daratumumab, bortezomib, dexamethasone; DVMp: daratumumab, bortezomib, melphalan, prednisone; EloPd: elotuzumab, pomalidomide, dexamethasone; Isa-Pd: isatuximab, pomalidomide, dexamethasone; IxaPd: ixazomib, pomalidomide, dexamethasone; Kd: carfilzomib, dexamethasone; KRd: carfilzomib, lenalidomide, dexamethasone; LOT: line of therapy; Pd: pomalidomide, low-dose dexamethasone; Rd: lenalidomide, dexamethasone; Vd: bortezomib, dexamethasone; VRd: bortezomib, lenalidomide, dexamethasone. Figure 2. The five most common treatment regimens across the 1 L to 4 L treatment cohorts across the first to fourth LOTs. The remaining regimens have been combined in “other” and are listed in Table S3. 1 L: first line; 2 L: second line; 3 L: third line; 4 L: fourth line; DPd: daratumumab, pomalidomide, dexamethasone; DRd: daratumumab, lenalidomide, dexamethasone; DVd: daratumumab, bortezomib, dexamethasone; DVMp: daratumumab, bortezomib, melphalan, prednisone; EloPd: elotuzumab, pomalidomide, dexamethasone; Isa-Pd: isatuximab, pomalidomide, dexamethasone; Kd: carfilzomib, dexamethasone; LOT: line of therapy; Pd: pomalidomide, low dose dexamethasone; Rd: lenalidomide, dexamethasone; Vd: bortezomib, dexamethasone; VRd: bortezomib, lenalidomide, dexamethasone.
Excluding “Other,” DRd was the most frequently used second LOT (19%; 41/219), followed by Rd (14%; 31/219), and carfilzomib + dexamethasone (Kd) (9%; 20/219) ( Figure 1(b) ). Rd was the second LOT in 8%, 15%, and 20% of patients in the 2 L, 3 L, and 4 L cohorts, respectively ( Figure 2(b) ).
Excluding “Other,” Kd was the most common third LOT (15%; 21/143), followed by elotuzumab + pomalidomide + dexamethasone (EloPd) (10%; 15/143), DVd (9%; 13/143), and pomalidomide + dexamethasone (Pd) (8%; 11/143) ( Figure 1(c) ). Treatment regimens used as the third LOT were similar between the 3 L and 4 L cohorts ( Figure 2(c) , Table S3).
Excluding “Other,” isatuximab + Pd (isa-Pd) was the most common fourth LOT (20%; 14/71), followed by Kd (14%; 10/71) and daratumumab + Pd (DPd) (11%; 8/71) ( Figures 1(d) and 2(d) ). Additional details of treatment regimens in each LOT and across treatment cohorts are presented in the appendix (Table S3).
Treatment sequencing
Of patients in the 2 L, 3 L, and 4 L cohorts ( n = 219), 35% (77/219) were treated with lenalidomide beyond their first LOT, and 13% (29/219) with an anti-CD38 mAb, including 9% (20/219) who were treated with daratumumab. Overall, 80% (175/219) of these patients had previous exposure to lenalidomide and 38% (84/219) had previous exposure to an anti-CD38 mAb, which was daratumumab in 38% (84/219) ( Figure 3 ). Of patients with prior exposure to lenalidomide, 44% (77/175) were retreated with lenalidomide. Of patients with prior exposure to anti-CD38 therapy, 35% (29/84) were retreated with an anti-CD38 mAb, including 24% (20/84) who were retreated with daratumumab. Figure 3. Retreatment status at 2 L and beyond. 2 L: second line; mAb: monoclonal antibody.
The most common treatment sequences from first LOT to second LOT were Vd to Rd (10%; 21/219) and VRd to DRd (8%; 17/219). Treatment sequencing patterns are presented in Figure S1.
Reason for treatment selection
The most common reasons for prescribing treatments at the time of data collection and across the LOTs were better efficacy overall (49%; 151/309), rapid onset of action (47%; 144/309), good clinical data regarding OS (38%; 118/309), manageable side effect profiles (29%; 89/309), and duration of response (25%; 76/309) ( Figure 4 ). Figure 4. Reason for current prescribing treatment. 1 L: first line; 2 L: second line; 3 L: third line; 4 L: fourth line.
Discussion
This real-world analysis used the ARW MM III DSP database to investigate patient burden of disease, HRQoL, treatment patterns, treatment sequencing, and reasons for treatment selection in patients with MM in Japan. Findings from this study reflect those of previous studies in offering insights and highlighting areas for improvement.
There is limited real-world evidence in Japan which uses patient-reported symptoms in MM. The patient reported symptoms at diagnosis in the current study are aligned with the previously reported symptoms in patients with MM in Japan [ 30 ]. PRO scores also showed a high level of disease burden in patients with MM regardless of LOT. The utility value scores assessed by EQ-5D-3L in this study were lower than those of the Japanese population norms [ 31 ]. The results of the current study indicate that patients with MM experience a lower QoL compared with the general Japanese population [ 31 ]. In a real-world study in the US, using similar PRO tools as in this analysis, including EQ-5D-3L, EQ-5D VAS, EORTC QLQ-C30, and EORTC QLQ-MY20, the HRQoL of patients with MM was generally similar across patients receiving different LOTs and worsened in patients with higher levels of side effect bother [ 32 ]. Real-world evidence in patients in the US showed that HRQoL is a key factor for treatment selection from the patient perspective; while patients with MM favored prolonged life expectancy and reduced disease burden, they equally expressed concern for the impact of treatment burden as it related to pain and general function [ 33 ]. Effective alignment between physician and patient preferences is associated with improved treatment adherence and outcomes [ 33 ]. Our analysis suggests a need for regular assessment of HRQoL by physicians in the clinical management of patients in the real-world setting.
Of the 309 patients with completed PRFs, 90 patients were receiving a first LOT at time of data collection, among whom 77% (69/90) received a lenalidomide-based therapy, with the most common 1 L lenalidomide-based regimen in the current analysis being VRd (32%). In a real-world study in Japan conducted between 2003 and 2020, Rd was the most commonly used regimen in the non-transplant group as well as in post-transplant [ 34 ], while a subsequent real-world study in Japan demonstrated an increase in the 1 L use of VRd from 2016 to 2020, reflecting the pattern observed in our analysis [ 35 ].
First-line use of DRd was greatest in patients who received 1 L treatment at the time of data collection and decreased across the cohorts, suggesting an increased use of DRd in 1 L over time. This may be related to the 2019 approval of daratumumab as 1 L treatment for MM in Japan and the subsequent recommendation of DRd in the 1 L setting in the JSH guidelines [ 15 , 36 ]. Furthermore, the findings of the MAIA study, in which 1 L treatment with DRd increased OS and progression-free survival in patients with MM, may have influenced the increased use of DRd in the 1 L setting [ 37 ]. The extensive use of lenalidomide and a trend toward using DRd in the first LOT may increase the proportion of patients in the second LOT who are refractory to both lenalidomide and daratumumab, as evidenced by Hartley-Brown et al. [ 38 ].
Of lenalidomide-exposed patients, 44% were retreated with lenalidomide at second LOT and beyond, which is slightly lower than reported in previous real-world studies in the US that found retreatment rates of 53% and 71% with lenalidomide-therapy [ 39 , 40 ]. Patients who are refractory to lenalidomide tend to experience less favorable outcomes, with treatment options often restricted to doublet regimens [ 41 , 42 ]. In a prospective study of 41 patients retreated with lenalidomide-containing regimens, ORR (partial response or better) was significantly higher in 1 L lenalidomide treatment than in 2 L (68.6% vs. 14.2%, p < 0.001) [ 41 ]. In addition, median progression-free survival was significantly longer with 1 L versus 2 L lenalidomide treatment (15.2 vs. 4.8 months, p < 0.001) [ 41 ].
Among patients with prior exposure to anti-CD38 therapy, 35% were retreated with an anti-CD38 mAb. Similarly, a retrospective study in the US reported retreatment with an anti-CD38 mAb in 39% of patients [ 40 ]. In another retrospective study in the US, the ORR among patients retreated with daratumumab was 67% [ 43 ]; however, other real-world studies in the US have reported poor prognoses with daratumumab retreatment [ 44 , 45 ]. Although findings are mixed, this evidence suggests that retreatment may not be an effective treatment strategy in some patients with prior exposure to the same treatment.
The most common reasons for treatment selection were better efficacy and good clinical data regarding OS. These reasons may be attributed to the findings of the MAIA study in relation to the improved OS and progression-free survival with the 1 L use of DRd [ 37 ]. However, these reasons for treatment selection may lead to an increased use of lenalidomide and/or daratumumab in earlier LOTs, and the subsequent increase in retreatment rates leads to more patients experiencing poor treatment outcomes. Together, this indicates a high unmet need for novel therapeutic modalities with good efficacy for patients who have had prior exposure to anti-CD38 mAb or lenalidomide-based regimens. Data collected regarding current and past treatment regimens, response rates, and treatment duration were not included here as they are beyond the scope of this paper.
Limitations
The DSP is a cross-sectional survey, thereby reflecting the MM population at a single point in time. As a result, it cannot be used to demonstrate cause and effect, although this was not one of the objectives of the survey.
The survey could have been prone to selection bias as the sample of patients in our study reflected the consulting population rather than the broader MM population. Patients who consulted more frequently were more likely to be included within the final survey population. Furthermore, the survey could not include patient data from those who have died. However, the systematic approach of DSP in recruitment (next presenting patients meeting the criteria) reduces selection bias. While minimal inclusion criteria governed the selection of physicians, the inclusion of a physician was influenced by their willingness to take part in the survey. Potential bias may have occurred as physicians surveyed represent a pragmatic sample and may not be representative of the overall population of physicians treating MM. Data regarding the survey duration and completion rate were not available.
While physicians were requested to collect data on a series of consecutive patients to mitigate selection bias, in the absence of randomization this was contingent on the integrity of the participating physician rather than formalized source verification procedures. Because the background of patients and physicians were not aligned for each group, analyses were conducted in a descriptive manner. As such, comparative analyses exploring subgroup differences, or correlation analyses to assess relationships between treatment, disease burden, and QoL were not conducted.
Diagnosis in the target patient group was based primarily on the judgment and diagnostic skills of the respondent physician, and a formalized diagnostic checklist was not mandated as part of the DSP methodology. However, this is reflective of real-world practice. The quality of data depended largely on the accurate reporting of information by physicians and patients, which may be subject to recall bias.
Despite these limitations, the Adelphi MM III DSP dataset utilized for this study was generally consistent with the overall MM population in Japan in terms of age and gender distribution [ 11 , 13 , 34 ]. This analysis reports on a relatively large sample of unselected patients; therefore, this can be considered reflective of real-world clinical practice at the time of data collection. Our data were collected at the time of each patient’s appointment, which was expected to reduce the likelihood of recall bias (physicians also had access to patients’ medical records for data extraction). Finally, the findings of this real-world analysis play an important part in highlighting areas of concern that are not addressed in clinical trials.
Conclusion
This analysis provided a contemporary overview of the current treatment practices in patients with MM across Japan. Patients with MM experience varying symptoms and HRQoL remains poor across the LOTs, highlighting a significant treatment burden. The extensive use of lenalidomide-based regimens at first LOT could be associated with higher rate of lenalidomide refractoriness and poor outcomes in patients in later LOTs. The high use of lenalidomide at first LOT, increasing prevalence of DRd at first LOT, and high retreatment rates with lenalidomide and anti-CD38 agents indicate a substantial need for new therapeutic options with novel mechanisms of action, particularly in the second LOT and beyond in lenalidomide- and anti–CD38-experienced patients.
Acknowledgments
Data collection was undertaken by Adelphi Real World as part of an independent survey, entitled the Adelphi Real World Multiple Myeloma III DSP TM . GSK did not influence the original survey through either contribution to the design of questionnaires or data collection. The analysis described here used data from the Adelphi Real World Multiple Myeloma III DSP. The DSP is a wholly owned Adelphi Real World product and GSK is one of multiple subscribers to the DSP. We would like to thank Toshihiko Kaise and Peter Wang, both former employees of GSK, for their contribution to concept creation.
Article highlights
This real-world analysis aimed to evaluate the disease burden, treatment patterns, treatment sequencing and retreatment, and reasons for treatment selection in patients with multiple myeloma (MM) in Japan. The median (interquartile range; IQR) age in the population of 309 patients included in this study was 75.0 (69.5–80.0) years; gender distribution was equal in the total population (male, 50.0%; 155/309). The most commonly reported symptoms by patients with a matched PRF/PSC ( n = 52) at data collection were lower back pain (29%; 15/52), fatigue (23%; 12/52), bone pain (21%; 11/52). The median (IQR) EuroQol-5 dimensions-3 level (EQ-5D-3L) and EQ-5D visual analog scale were 0.7 (0.6–1.0) and 70 (56.2–70.0), respectively. Lenalidomide-based treatments were commonly used in the first line of therapy (LOT) and bortezomib + lenalidomide + dexamethasone was the most common regimen (32%). Also, first-line (1 L) use of daratumumab + lenalidomide + dexamethasone (DRd) was greatest in patients who received 1 L treatment at the time of data collection and decreased across the cohorts to fourth-line, suggesting an increased use of DRd in 1 L over time. Of patients with prior exposure to lenalidomide and anti-CD38 monoclonal antibodies (mAbs), 44% (77/175) and 35% (29/84) were retreated with lenalidomide and an anti-CD38 mAb, respectively, at second LOT and beyond. The high use of lenalidomide and increasing prevalence of DRd at first LOT, and high retreatment rates with lenalidomide and anti-CD38 agents indicate a substantial need for new therapeutic options with novel modes of action, particularly in the second LOT and beyond in patients with prior exposure to lenalidomide- and anti-CD38 mAbs.
Authors contributions
Developed the concept: YY, AR, EL, and SM. Conceived, designed, and performed the experiment: AR and EL. Analysed data: EL. Contributed reagents/materials/analysis tools: AR and EL. Validated data: EL. Wrote the paper: YY. Edited the manuscript: YY, AR, EL, and SM. Contributed to data visualization: YY, AR, and EL. Supervised the project: YY, AR, EL, and SM. Contributed to project administration: YY, AR, EL, and SM. Contributed to funding acquisition: SM.
Disclosure statement
YY and SM report employment at GSK and own shares in GSK. AR and EL report no conflicts of interest. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Medical writing and editorial support were provided by Ehsan Kachooei, PhD, and Joyce Lee, PhD, CMPP TM of Nucleus Global, an Inizio Company, and were funded by GSK.
Ethical statement
The DSP survey was submitted to the Pearl Institutional Review Board (study protocol number AG8847) and received ethics exemption. Data collection was undertaken in line with the European Pharmaceutical Marketing Research Association guidelines. Each survey was performed in full accordance with relevant legislation at the time of data collection. All patients provided informed consent prior to completing the PSC. Physician and patient data were pseudo-anonymized by assigning a code to them when data were collected. Upon receipt by Adelphi Real World, data were pseudo-anonymized again to mitigate against tracing them back to the individual. Data were aggregated before being shared with GSK and/or for publication.
Data availability statement
All data, i.e., methodology, materials, data, and data analysis, that support the findings of this survey are the intellectual property of Adelphi Real World. All requests for access should be addressed directly to Amanda Ribbands (employee of Adelphi Real World) at Amanda.ribbands@adelphigroup.com .
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