In heterogeneous multiple myeloma (MM) patients treatment decisions are challenging. The hypothesis was that adaptation of treatment intensity (dose reduction [DR] vs. none) according to an objective risk score (revised-myeloma comorbidity index [R-MCI]) rather than physician judgement alone may improve therapy efficacy and avoid toxicities. We performed this study in 250 consecutive MM patients who underwent a prospective fitness assessment at our center, after having received induction protocols based on physicians’ judgement. DR, serious adverse events (SAE), response, progression-free survival (PFS) and overall survival (OS) were compared in fitness (fit, intermediate-fit, frail), age (<60, ≥70 years [y]) and therapy intensity subgroups at baseline and follow-up. Fit and <60 y patients were mostly treated with full intensity, whereas frail and ≥70 y patients usually received DR. Hematological and non-hematological SAE were more frequently seen in frail versus ≥70 y patients. Dose adaptations were mainly necessary in frail patients. OS and PFS were similar in fit and intermediate-fit but significantly worse in frail patients (P=0.0245/P<0.0001), whereas in age-based subgroups, OS and PFS differences did not reach significance (P=0.1362/P=0.0569). Non-hematological SAE were another negative predictor for impaired OS and PFS (P=0.0054/P=0.0021). In the follow-up performed at a median of 11 months after the first fitness assessment, the R-MCI improved or remained stable in 90% versus deteriorated in only 10% of patients. In conclusion, separation by R-MCI/frailty-defined subgroups was superior to age-based subgroups and can be used to improve tailored treatment. Fitter patients benefit from intensive therapies, whereas frail patients bear a need for initial DR.
Multiple myeloma (MM) is a hematological disease, which typically affects elderly patients.1 In the past decade, treatment options have substantially evolved and with inclusion of proteasome inhibitors (PI), immunomodulatory drugs (IMiDs) and antibodies (Ab)/immunotherapies into induction and relapse protocols, response rates, progression-free survival (PFS) and overall survival (OS) have improved impressively.2 Standard treatment in newly diagnosed MM (NDMM) includes triplets or quadruplets, plus - if patients are deemed fit enough - autologous stem cell transplantation (ASCT), followed by maintenance therapy.3,4 Even though patient assessment, via age, Karnofsky Performance Status (KPS), comorbidities, patient history and examination, is performed, MM patients’ actual constitution and fitness can be over- or underestimated.5-8 Additionally, inclusion in clinical trials is especially rare in elderly patients over the age of 70 years.5,6 This suggests that more objective tools may assist physicians to find most suitable treatment regimens and to adapt dose intensity for elderly and/or MM-stricken patients.
In line, the long continuing COVID-19 pandemic imposes the need to prevent any unnecessary serious adverse events (SAE), hospitalization, time-consuming dose adjustments and therapy cessations.9-13 However, therapy decisions are often made without an objective fitness assessment.7,14 ,1 5 In recent years, MM-specific risk scores (e.g., International Myeloma Working Group (IMWG)-frailty score, revised-myeloma comorbidity index (R-MCI), Mayorisk score, UK Myeloma Research Alliance Risk Profile) were developed to assist in this unsolved matter.16-19
The hypothesis of this study was that adaptation of therapy intensity according to an objective risk score (via R-MCI; Online Supplementary Figure S1), rather than via physician judgement alone, may improve therapy efficacy and avoid therapy toxicities and discontinuation. More studies are now testing the feasibility of MM-specific risk-scores for treatment assistance in MM patients, albeit additional studies should further be performed.14,20-22 We used the R-MCI, because this constitutes a repeatedly validated MM-specific risk tool, used routinely for MM patients at our institution, that has been integrated into our electronic tumor board (TB) online system.17,23 The R-MCI contains of five weighted risk factors, namely renal and lung function, KPS, frailty and age, plus allows to include cytogenetics.17,23 The R-MCI web tool allows the immediate calculation of the weighted R-MCI, which can be likewise performed by physicians or research assistants (www.myelomacomorbidityindex.org). We here investigated the applicability of the R-MCI for future therapy decision support by performing an analysis of patients receiving first-line treatment. Main aspects of this study were to track patients’ induction treatment, comparing R-MCI- versus age subgroups in terms of therapy adaptations, SAE, response, OS and PFS.
Since patients’ constitution and disease burden may change during treatment, we also re-evaluated constitution, fitness and R-MCI changes in a follow-up analysis (Online Supplementary Figure S2).
Data sources and study design
We performed this exploratory study in 250 consecutive MM patients, who received induction treatment ideally continuing until intolerance or progression at our Comprehensive Cancer Center Freiburg (CCCF) and catchment area of the Black Forest from 2000 to 2018. All patients were fully documented at our CCCF. The cohort with prospectively assessed R-MCI was pooled from two prior conducted analyses,8,17 retrieving patient- and therapy-relevant data through our electronic documentation system ‘Medoc’. Of the initial 359 patients, 109 had to be excluded either due to ongoing induction at the time of assessment (n=50) or incomplete data (n=59; Online Supplementary Figure S2). Patients’ characteristics included the International Staging System (ISS), R-MCI and IMWG-frailty scores at baseline. Via R-MCI and IMWG-frailty scores, all patients were grouped as fit, intermediate-fit or frail (Table 1; Online Supplementary Figure S1). Decisions of induction regimen, treatment intensity and dose reductions (DR) were based on physicians’ choice. Comparisons were performed for frailty- (R-MCI-fit, -intermediate-fit, vs. -frail), age- (<60, 60-69 vs. ≥70 years) and therapy-intensity (with vs. without initial DR) subgroups.
The study was performed according to the guidelines of the Declaration of Helsinki and Good Clinical Practice. All patients gave their written informed consent for institutionally initiated research studies and analyses of clinical outcome studies conforming to the institutional review board guidelines. The trial protocol was approved by the ethics committee of the University of Freiburg (EV 81/10).
Induction, dose reductions, serious adverse events, response and follow-up analysis
For each induction regimen, one lead agent was determined. Albeit any DR of any drug in combination first-line treatment could have been counted as a dose modification, this would not have allowed a less complex intensity calculation. Thus, any decrease in the lead agents’ standard dose intensity or change from triplets to doublets was defined as DR. Lead agents were defined, with regard to the severity of adverse events (AE) in the following order of priority: alkylating agents, subsequently IMiDs and PI/Ab, last anthracyclines or glucocorticoids. The standard dose was consistent with NCCN/EMN-guidelines and CCCF-chemotherapy manual.24
We used the Common Terminology Criteria (CTC) for AE version 5.0 to assess grade 3-4 hematological SAE (anemia, leukocytopenia, thrombocytopenia) and non-hematological SAE grades 3-5 (infections, renal, pulmonary, cardiac impairment).
Quality of response was assessed via IMWG-remission criteria until the end of induction.25,26
Six to 24 months after the first fitness assessment, we analyzed, if changes in remission status and patients’ constitution via a follow-up R-MCI analysis had occurred.
OS was defined as the time from start of induction to death from any cause and PFS as the time from start of induction to cancer recurrence or death from any cause. Data for patients alive at the time of the analysis were censored at the last follow-up. Probabilities of PFS and OS were estimated using Kaplan-Meier method and compared with log-rank tests. In order to avoid an immortal time bias, plots of OS and PFS of patients showing no or at least one SAE, included only those who survived at least 6 months after the start of induction treatment. Chi-square-and Mantel-Haenszel tests were utilized as appropriate in the comparisons of therapy protocols, DR, SAE, response rates in R-MCI and age groups. A P value of <0.05 was considered as statistically significant. Data were analyzed with SAS 9.2 (SAS Institute, Inc, Cary, North Carolina).
Patient characteristics’ and induction regimen
Among the 250 analyzed patients the median age at baseline was 62 years and 61% were males. For a cohort, where first-line treatment was mainly applied at a tertiary and referral center, patient characteristics were typical (Table 1), likewise myeloma subtypes, ISS, median bone marrow infiltration (45%) underlying AL-amyloidosis rate (6%), and cytogenetics. ISS stage 2 and 3 were most common with 66% (Table 1).
Median IMWG-frailty score and R-MCI were 1 and 4, respectively, in line with prior test and validation analyses.15,17,23,27,28 Impaired constitution via KPS ≤70% was present in 42% and moderate or severe frailty was reported in 36%.8,17,23 Renal impairment with eGFR <60 mL/min/1.73m2 was present in 40% and moderate or severe lung dysfunction as defined via R-MCI website in 12%, in line with previous analyses.8,17,23,29,30
VCD, VRd/RAD or other induction (Online Supplementary Figure S3) was predominantly performed with VCD and whenever possible in German study group (DSMM)-protocols (DSMM XI, XII, XIV).31,32 Induction was often initiated at the CCCF, but also at regional hospitals and private practices. Stem cell transplantation (SCT) was performed in 72% (Table 1).
Comparisons of patient- and therapy-relevant parameters in entire cohort, and in revised myeloma comorbidity index, age and dose intensity subgroups
Patient and therapy parameters are summarized in Table 2 in entire cohort and in fit versus frail, younger versus older and full-dosed (no DR) and dose-reduced (initial DR) subgroups. No initial DR versus DR were performed in 59% and 41%, respectively, reflecting the complexity to treat an even fairly young MM cohort, and that initial DR was frequently performed. The number of hematological and non-hematological SAE were frequent with 157 and 123, which accounted for SAE per patient in 0.63 and 0.49, respectively. The median induction duration was 62 days. Best IMWG response (≥ partial response [PR]) after induction was observed in 75% in line with prior data (Table 2).31 Results of R-MCI fit and younger (<60 years) patients were similar and merely differed in used protocols and SAE (Table 2). Intermediate-fit and frail patients (combined: 71%) showed increased median ages of 66 and 74 years, respectively. VRd/RAD first-line treatment in fit, intermediate-fit and frail patients were performed in 37%, 15% and 6%, thus decreased with frailty, whereas DR substantially increased (19%, 45% and 72%, respectively; Table 2). In line, SAE per patient increased from 0.23 in fit, to 0.72 in intermediate-fit and 1.1 in frail patients for hematological SAE, and 0.23, 0.48 and 1.13 for non-hematological SAE. Median induction duration in intermediate-fit patients was similar to fit patients, basically because SCT in intermediate-fit patients remained considerable with 72%. As expected, in frail patients longer induction (6-9 cycles plus maintenance) was performed and less SCT (Table 2). Divided into fit, intermediate-fit and frail patients, the response rates were 73%, 77% and 69%, respectively, thus higher in both former than latter subgroup (Table 2).
According to age subgroups, DR increased in <60, 60-69 and ≥70-year-old patients from 21% to 43% and 66%, respectively. SAE per patients increased less and seemed less predictable than in R-MCI subgroups (Table 2). Transplant-eligible patients aged 60-69 years did hardly show any differences in non-hematological SAE compared to patients ≥70 years (0.58/patient vs. 0.62/patient). SCT frequencies were typical in young in 93%, 81% in 60-69 and 32% in ≥70-year-old patients. Transplant-ineligible patients (≥70 years) were at treatment initiation in 29 of 73 (40%) >75 years old. Best responses (≥PR) in age cohorts ranged from 66-79% (Table 2).
In dose intensity subgroups, for patients without versus with DR, median age differences and R-MCI-differences were notable with 58 versus 69 years and 4 versus 5, respectively. This was in line with lesser performed SCT in the latter group (Table 2). Whereas hematological SAE/patient were similar in patients without versus with DR, these almost doubled for non-hematological SAE (0.36 versus 0.69/patient, respectively). Median induction duration was similar in both groups, while as expected, response rates (≥PR) were increased in patients without DR (Table 2).
Serious adverse events: subgroup distribution for hematological and non-hematological serious adverse events
Distinct differences in types of hematological and non-hematological SAE are shown in Figure 1A and B, being more prevalent in frail than intermediate-fit or fit patients.
Anemia, leukocytopenia and thrombocytopenia SAE (CTC grade 3-4) occurred in 76, 56 and 25 patients, respectively. Leukocytopenia was equally prevalent in frail and intermediate-fit patients (44% and 45%, respectively), while anemia and thrombocytopenia were predominantly observed in frail patients (59% and 68%, respectively; Figure 1A). Hematological SAE appeared in patients treated with alkylating agents (VCD) in 47% and with IMiD-based protocols (VRd/RAD) in 20% (‘others’ were too few for meaningful conclusions).
Of 123 registered non-hematological SAE, 69 were attributed to infectious, 23 to renal, 18 to pulmonary and 13 to cardiac causes (Figure 1B). Comparison of different R-MCI subgroups with infectious, renal, pulmonary and cardiac SAE showed significant increases in frail as compared to fit or intermediate-fit patients, reaching significance in all except pulmonary SAE (Figure 1B).
Therapy adaptations in frailty and age subgroups
Our assessment of SAE in patients without or with initial DR is depicted in the Online Supplementary Table S1A and B. The occurrence of hematological SAE without or with DR did not show distinct differences in frailty subgroups. Therapy adaptations (DR, therapy pauses or discontinuation) after hematological SAE occurred in intermediatefit or frail patients only, both without and with performed initial DR (Online Supplementary Table S1A).
Non-hematological SAE occurred in R-MCI subgroups likewise more often in patients without DR than if performed, whereas this was less strikingly found for age subgroups (Online Supplementary Table S1B). Therapy adjustments or therapy discontinuations after non-hematological SAE increased both with frailty and age, and were more prevalent with full doses than if DR had been performed (Online Supplementary Table S1B).
Therapy discontinuation occurred in only eight of 250 (3%) patients (Online Supplementary Table S2), showing mostly advanced age and impaired R-MCI scores. Initial dose reductions had been performed in seven of eight patients. Patient constitution complications, myelomaand/or therapy-induced complications occurred in already the 1st (n=3) to 4th (n=3) induction cycle (range, 1-4). Retrospectively assessed therapy intensity by us (MH, ME) suggested in seven of eight patients, that these were overdosed. Thus, if on top of clinical judgement, R-MCI-tailored therapy had been performed - SAE and therapy cessation might have been avoided in seven of eight patients.
Serious adverse events leading to early death (<60 days after induction) or concomitant serious adverse events
SAE during induction which led to death were seen in three patients (3/250 i.e., in 1.2%). These showed cardiac complications, two of three associated with underlying AL-amyloidosis. Sixteen patients with concomitant AL-amyloidosis contributed to 28% of cardiac (n=13) and renal (n=23) SAE (CTC 3-5) of the entire cohort, thus showed a 5.6 higher risk for these complications. As expected, patients with CTC grade 3-4 leukocytopenia were more likely to acquire severe infections (Online Supplementary Table S3).
Overall survival and progression-free survival of entire cohort and in various subgroups
The median follow-up from start of induction was 65 months (range, 1-246). Detailed response in fit, intermediate-fit and frail patients are summarized in the Online Supplementary Table S4. The Kaplan-Meier curves for OS and PFS analyses are displayed in Figures 2 and 3. The estimated 3-year OS and PFS in the entire cohort were 85% (95% confidence interval [CI]: 79-89) and 53% (95% CI: 47-60), respectively (Figure 2A and B). The three R-MCI subgroups in Figure 2C and D showed similar results for both fit and intermediate-fit patients. The 3-year OS in R-MCI fit, intermediate-fit and frail was 89% (95% CI: 79-94), 85% (95% CI: 77-90) and 70% (95% CI: 47-85), respectively (P=0.0688; Figure 2C). The 3-year PFS was 60% (95% CI: 47-70), 55% (95% CI: 47-63) and 21% (95% CI: 6-41), respectively (P<0.0001; Figure 2D).
Since results of fit and intermediate-fit patients were comparable and very different to frail patients, these were combined as displayed in Figure 2E and F. Of note, 3-year OS and PFS in fit/intermediate versus frail patients showed highly significant differences of 86% (95% CI: 81-90) versus 70% (95% CI: 47-85) and 57% (95% CI: 50-63) versus 21% (95% CI: 6-41), respectively (P=0.0245/P<0.0001).
Age subgroups revealed differences with best OS/PFS for <60-year-old patients, whereas 60-69 and ≥70-year subgroups revealed similar Kaplan Meier curves. Notably, 3-year OS/PFS results via age displayed lesser and insignificant group distinctions (P=0.1362/P=0.0569; Figure 2G and H).
Serious adverse events and dose reduction impact on overall survival and progression-free survival
Any SAE led to impaired OS and PFS (Figure 3A and B), both for hematological (Figure 3C and D) and non-hematological SAE (Figure 3E and F), with more striking OS and PFS differences for the latter SAE. In line, DR versus no DR led to both OS and PFS differences in favor of patients with no DR, reflecting standard doses to be easier applied in fitter and younger patients and therefore accounting for these differences (Figure 3G and H).
Revised myeloma comorbidity index follow-up analysis
The follow-up analysis (T0 → T1) was possible in 180 patients (72%; Figure 4; Online Supplementary Figure S2). The median follow-up was 11 months (range, 6-24), in line with our previous study.8 Median R-MCI scores at T0 and T1 were both 4, reflecting intermediate-fit patients. Assessing also mean T0 versus T1 differences, the R-MCI improved from 4.3 to 3.7, respectively and accounted for a mean improvement of 0.6 points over an 11 month period (P<0.0001). Among all follow-up patients, 77 (43%) achieved a better, 84 (47%) a stable and only 19 (10%) a worse R-MCI (Figure 4). Maximum R-MCI changes were improvements by four points (R-MCI 6 → 2) and deteriorations by three points (R-MCI 4 → 7), impressively illustrating that R-MCI changes within a ~1 year follow-up can be more drastic than age shifts within this period.
One crucial aspect for MM patients and physicians is the individual selection of the initial (and subsequent) treatment and its intensity.7,33 Our message is that we apply evidence generated from clinical trials that rarely include old or frail patients to treat such patients without knowledge of the need for modifications of the drugs used, the dosing or schedule. Although our study population received first-line MM treatment with dose modifications according to best clinical judgment and a prospective frailty assessment was performed, the frailty assessment was not used for decision making. Thus, clinicians were blinded to this information. This allowed to assign patients into those with versus without DR as compared to comorbidity and age subgroups (Table 2). Hence, we investigated physicians’ therapy decisions in a NDMM population during induction by analyzing the course of therapy and patient outcome. Our data demonstrates that the use of the R-MCI can assist to anticipate the likelihood of SAE. Those predictions can subsequently be used to optimize induction dose intensity (Table 2; Figure 1A and B) and identify patients at risk of treatment discontinuation with the associated risk of a more unfavorable outcome (Figure 2 and 3). We found compelling differences between R-MCI subgroups regarding protocols and doses prescribed. Overall, alkylating agents were the most used leading agent, in line with VCD being a commonly applied DSMM/GMMG and EMN-study regimen.31,34 With a median start of induction in 2016, most frail patients in our cohort were treated in compliance with guidelines for dose-adjusted VCD or Vd as first-line treatment.34 The EHA-ESMO guideline from 2021 propagates nowadays to add a CD38-antibody (e.g., Dara-VMP, DaraRd) in NDMM patients ineligible for ASCT, reiterating that R-MCI- or other risk-tool-adapted triplets and quadruplets may profit from our approach described here to avoid SAE and therapy cessations.33
We were also able to show that patients at risk were mostly correctly identified and reasonable dose adaptations were made, but potential improvements remain. Moreover, we determined that fit patients had few initial DR (19%) and a low incidence of hematological or non-hematological SAE (both 0.14 per patient). In contrary, full-dosed frail patients (28%) were nine times more likely to suffer from hematological SAE and had a four times higher rate of non-hematological SAE (Online Supplementary Table S1A and B). The conjecture that some frail patients were possibly overtreated and dose-reduced fit patients undertreated is therefore plausible and has been described previously.6,17,23,34 Besides, the 3-year OS was superior in frail patients with initial DR versus in frail patients without initial DR (73% vs. 63%). Although numbers of this subgroup comparison were limited, other studies confirm this observation.11,22,35,36 Moreover, we observed that two of three events of death were associated with subclinically underlying AL-amyloidosis. These findings confirmed the need to critically evaluate induction protocols and protocol doses to avoid therapy complications and to reliably detect AL-amyloidosis.37,38 Indeed, the occurrence of any SAE was associated with a worse outcome (Figure 3A), validating prior studies.11,36,37
Since various treatment pathways continue to suggest age cut-offs (i.e., >60 or >70-years), we divided our cohort into patients aged <60, 60-69 and ≥70 years and compared them to R-MCI and therapy intensity (DR vs. no DR performed) subgroups (Table 2). In line with our and other prior analyses, R-MCI subgroups differed much more than age or dose intensity subgroups, supporting the paradigm of an objective, functional GA and risk score.5,8,39-42 Notably, ≥70 and 60-69-year-old (transplant-eligible) patients showed similar results regarding SAE, OS and PFS and thereby differed from <60-year-old patients, whereas for fitness groups, fit and intermediate-fit patients were comparable and very distinct from frail patients. Since patients ≥70 years are often excluded from clinical trials and more intensive therapies, we assessed this age group more thoroughly.6,34,42,43 Of interest, 32% of our ≥70-year-old patients received a SCT after physicians’ appraisal (Table 2). Nevertheless, 56% (n=28) of patients ≥70 years, who did not receive a SCT, were classified as either intermediate-fit or fit. Thus, those patients could have received intensive treatment. Larocca et al. elegantly demonstrated the benefit of a therapy-decision approach (dose-adjusted Rd-R vs. continuous Rd) via IMWG-frailty index in intermediate-fit patients in a prospective study.20 An ongoing, equally important Medical Research Councils study randomizes unaltered to adjusted treatment according to fitness results (FiTNEss study, clinicaltrails gov. Identifier: NCT03720041, PI: G.Cook), both supporting our findings.
R-MCI: revised-myeloma comorbidity index; Interm.-fit: intermediate-fit.
Concerning patients’ outcome, we observed a substantial OS and PFS advantage in fit and intermediate-fit versus frail patients (Figure 2A and B). In line, Facon et al. propagated a simpler approach of the IMWG-frailty score, dividing patients likewise into non-frail versus frail patients, which seems straightforward for clinical routine and clinical trials.21 An understandable request is that these risk-assessments should not be time-consuming and prospectively performed.7 Both, the R-MCI and IMWG-frailty index offer online tools for their automatic calculation.
Strengths of our analysis were the meticulous examination of a NDMM patient cohort, of performed treatment, doses, SAE, PFS and OS in R-MCI, age and therapy intensity subgroups. Moreover, 72% of the initially assessed patients could be included in our follow-up analysis and their constitution and fitness, as measured via R-MCI, revealed improvement or stabilization in 90% and deterioration in only 10%. We have previously described in an even more detailed functional analysis using 12 different comorbidity scores and functional tests: the assessments more frequently and significantly changed in younger patients (<70 years) and those with good response (≥PR), suggesting a better functional reconstitution in younger and responsive than in older and less responsive myeloma patients.8 These 10% of patients, whose R-MCI deteriorated in our follow-up analysis, should be reliably identified, because fittingly chosen therapies are relevant to perform and will ideally improve patients’ quality of life (QoL).8 This was also reflected in frequencies of non-hematological SAE, which were much lower in patients with improved or stable R-MCI (both 0.39/per patient) than in those with deteriorated R-MCI (0.53/per patient). No difference in infections was observed, most likely, due to the use of detailed chemotherapy treatment plans, including strict antibiotic prophylaxis schedules therein.24 Although OS differences between these subgroups were hampered by limited patient numbers with deteriorated R-MCI, our follow-up analysis confirmed that MM treatment may indeed improve patients’ constitution. Renal impairment and/or frailty (including KPS) may recover under MM therapies, whereas in those not improving and deteriorating with QoL domains, therapeutic adjustments are important to consider.8 Additionally, our median observation period of 5.4 years was substantial, therefore our Kaplan-Meier results in all patients, in R-MCI, age, SAE-experiencing and dose-reduced patients were robust and mature. So far, there are few studies on prospective MM-specific risk tools for therapy-decision-support for NDMM and even lesser for relapsed/refractory (RR)MM patients, albeit these data and currently ongoing GIMEMA, MRC, HOVON and other studies support these endeavors.44-46 Lastly, these data impressively confirm that the R-MCI seems superior to age-based treatment pathways. Limitations of our study were the single institution approach, yet due to strict inclusion criteria regarding patients’ and therapy data, all patients included provided infinitely detailed information. Another criticism could be the heterogeneity in patients (age range, 27-92 years), with a considerable number of patients <70 years (71%), as is typical for tertiary centers in Germany (vs. more centralized institutions and countries). Since our university and catchment area-treated patient population was relatively young and the majority received ASCT, we refrained from non-ASCT versus ASCT-based subgroup analyses, but considered all patients as one group. Besides, one could criticize the use of other than VCD-induction protocols in rare subgroups. Underlying AL-amyloidosis could also been argued to be possibly excluded, which we decided against, because all patients were initially diagnosed with MM only, but determined with AL amyloidosis by us, thus initially remaining undetected.38 Lastly, we did not analyze the event-free survival as shown in prior studies,20 as we focused on OS/PFS. The former will be part of another upcoming study at our institution.
In conclusion, our results demonstrate the higher frequency of SAE, higher discontinuation rates and early mortality in frail patients, supporting MM patients' need for individualized induction and relapse protocols.17,23,46,47 Full-dose intensity for fit and reduced doses for frail patients appears pertinent, whereas intermediate-fit patients need continuing consideration. The precise fitness assessment in MM, similar to other hematological malignancies, seems relevant to achieve favorable treatment results, less DR, SAE, as few unscheduled re-hospitalizations and preserved QoL.7,12,42,48,49 The latter demonstrated itself to be possible even after intensive regimens, after allogeneic transplantation or quadruplet RRMM treatment.8,17,23,27,28 The implementation of functional assessments in myeloma TB may also support physicians in treatment decisions, since this adds an objective assessment of patients’ individual constitution and possible treatment endurance. Future studies are needed to evaluate the benefits of a functionally adapted treatment approach versus ´treatment as usual´. Prospective studies using the R-MCI in TB for therapeutic decision support are in process at our CCCF.
- Received May 30, 2022
- Accepted October 27, 2022
No conflicts of interest to disclose.
MH, ME, and all other authors performed the analysis. MH, GI, HR and ME analyzed results. MH, GI and ME prepared tables and figures. ME, RW, GI designed the research and MH and ME wrote the paper. All authors approved and carefully revised the paper.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
This work was supported by the Deutsche Krebshilfe (grants 1095969 and 111424 to ME and RW).
The authors thank DSMM, GMMG, EMN and IMWG experts for their support and prior recommendations on this study. We thank all MM patients who participated in this study.
- Rajkumar SV. Multiple myeloma: 2020 update on diagnosis, risk-stratification and management. Am J Hematol. 2020; 95(5):548-567. Google Scholar
- Kumar SK, Rajkumar V, Kyle RA. Multiple myeloma. Nat Rev Dis Primer. 2017; 3:17046. Google Scholar
- Mateos M-V, Dimopoulos MA, Cavo M. Daratumumab plus bortezomib, melphalan, and prednisone for untreated myeloma. N Engl J Med. 2018; 378(6):518-528. Google Scholar
- Dimopoulos MA, Jakubowiak AJ, McCarthy PL. Developments in continuous therapy and maintenance treatment approaches for patients with newly diagnosed multiple myeloma. Blood Cancer J. 2020; 10(2):17. Google Scholar
- Soto-Perez-de-Celis E, Li D, Yuan Y, Lau YM, Hurria A.. Functional versus chronological age: geriatric assessments to guide decision making in older patients with cancer. Lancet Oncol. 2018; 19(6):e305-e316. Google Scholar
- Fakhri B, Fiala MA, Tuchman SA, Wildes TM. Undertreatment of older patients with newly diagnosed multiple myeloma in the era of novel therapies. Clin Lymphoma Myeloma Leuk. 2018; 18(3):219-224. Google Scholar
- Goede V, Neuendorff NR, Schulz R-J, Hormigo A-I, Martinez-Peromingo FJ, Cordoba R.. Frailty assessment in the care of older people with haematological malignancies. Lancet Healthy Longev. 2021; 2(11):e736-e745. Google Scholar
- Scheubeck S, Ihorst G, Schoeller K. Comparison of the prognostic significance of 5 comorbidity scores and 12 functional tests in a prospective multiple myeloma patient cohort. Cancer. 2021; 127(18):3422-3436. Google Scholar
- Voorhees PM, Kaufman JL, Laubach J. Daratumumab, lenalidomide, bortezomib, and dexamethasone for transplant-eligible newly diagnosed multiple myeloma: the GRIFFIN trial. Blood. 2020; 136(8):936-945. Google Scholar
- Facon T, Kumar S, Plesner T. Daratumumab plus lenalidomide and dexamethasone for untreated myeloma. N Engl J Med. 2019; 380(22):2104-2115. Google Scholar
- Bringhen S, Mateos MV, Zweegman S. Age and organ damage correlate with poor survival in myeloma patients: meta-analysis of 1435 individual patient data from 4 randomized trials. Haematologica. 2013; 98(6):980-987. Google Scholar
- Klepin HD, Sun C-L, Smith DD. Predictors of unplanned hospitalizations among older adults receiving cancer chemotherapy. JCO Oncol Pract. 2021; 17(6):e740-e752. Google Scholar
- Terpos E, Engelhardt M, Cook G. Management of patients with multiple myeloma in the era of COVID-19 pandemic: a consensus paper from the European Myeloma Network (EMN). Leukemia. 2020; 34(8):2000-2011. Google Scholar
- Isaacs A, Fiala M, Tuchman S, Wildes TM. A comparison of three different approaches to defining frailty in older patients with multiple myeloma. J Geriatr Oncol. 2020; 11(2):311-315. Google Scholar
- Engelhardt M, Ihorst G, Duque-Afonso J. Structured assessment of frailty in multiple myeloma as a paradigm of individualized treatment algorithms in cancer patients at advanced age. Haematologica. 2020; 105(5):1183-1188. Google Scholar
- Palumbo A, Bringhen S, Mateos M-V. Geriatric assessment predicts survival and toxicities in elderly myeloma patients: an International Myeloma Working Group report. Blood. 2015; 125(13):2068-2074. Google Scholar
- Engelhardt M, Domm A-S, Dold SM. A concise revised Myeloma Comorbidity Index as a valid prognostic instrument in a large cohort of 801 multiple myeloma patients. Haematologica. 2017; 102(5):910-921. Google Scholar
- Milani P, Vincent Rajkumar S, Merlini G. N-terminal fragment of the type-B natriuretic peptide (NT-proBNP) contributes to a simple new frailty score in patients with newly diagnosed multiple myeloma. Am J Hematol. 2016; 91(11):1129-1134. Google Scholar
- Cook G, Royle K-L, Pawlyn C. A clinical prediction model for outcome and therapy delivery in transplant-ineligible patients with myeloma (UK Myeloma Research Alliance Risk Profile): a development and validation study. Lancet Haematol. 2019; 6(3):e154-e166. Google Scholar
- Larocca A, Bonello F, Gaidano G. Dose/schedule-adjusted Rd-R vs continuous Rd for elderly, intermediate-fit patients with newly diagnosed multiple myeloma. Blood. 2021; 137(22):3027-3036. Google Scholar
- Facon T, Dimopoulos MA, Meuleman N. A simplified frailty scale predicts outcomes in transplant-ineligible patients with newly diagnosed multiple myeloma treated in the FIRST (MM-020) trial. Leukemia. 2020; 34(1):224-233. Google Scholar
- Brioli A, Manz K, Pfirrmann M. Frailty impairs the feasibility of induction therapy but not of maintenance therapy in elderly myeloma patients: final results of the German Maintenance Study (GERMAIN). J Cancer Res Clin Oncol. 2020; 146(3):749-759. Google Scholar
- Engelhardt M, Dold SM, Ihorst G. Geriatric assessment in multiple myeloma patients: validation of the International Myeloma Working Group (IMWG) score and comparison with other common comorbidity scores. Haematologica. 2016; 101(9):1110-1119. Google Scholar
- Engelhardt M, Mertelsmann R, Duyster J.. Das Blaue Buch Chemotherapie-Manual Hämatologie und Onkologie. 7. Auflage. Springer Verlag. 2020. Google Scholar
- Kumar S, Paiva B, Anderson KC. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016; 17(8):e328-e346. Google Scholar
- Durie BGM, Harousseau J-L, Miguel JS. International uniform response criteria for multiple myeloma. Leukemia. 2006; 20(9):1467-1473. Google Scholar
- Greil C, Engelhardt M, Ihorst G. Allogeneic transplantation of multiple myeloma patients may allow longterm survival in carefully selected patients with acceptable toxicity and preserved quality of life. Haematologica. 2019; 104(2):370-379. Google Scholar
- Waldschmidt JM, Keller A, Ihorst G. Safety and efficacy of vorinostat, bortezomib, doxorubicin and dexamethasone in a phase I/II study for relapsed or refractory multiple myeloma (VERUMM study: vorinostat in elderly, relapsed and unfit multiple myeloma). Haematologica. 2018; 103(10):e473-e479. Google Scholar
- Dold SM, Möller M-D, Ihorst G. Validation of the revised myeloma comorbidity index and other comorbidity scores in a multicenter German study group multiple myeloma trial. Haematologica. 2021; 106(3):875-880. Google Scholar
- Möller M-D, Ihorst G, Pahl A. Physical activity is associated with less comorbidity, better treatment tolerance and improved response in patients with multiple myeloma undergoing stem cell transplantation. J Geriatr Oncol. 2021; 12(4):521-530. Google Scholar
- Einsele H, Engelhardt M, Tapprich C. Phase II study of bortezomib, cyclophosphamide and dexamethasone as induction therapy in multiple myeloma: DSMM XI trial. Br J Haematol. 2017; 179(4):586-597. Google Scholar
- Bachmann F, Schreder M, Engelhardt M. Kinetics of renal function during induction in newly diagnosed multiple myeloma: results of two prospective studies by the German Myeloma Study Group DSMM. Cancers. 2021; 13(6):1322. Google Scholar
- Dimopoulos MA, Moreau P, Terpos E. Multiple myeloma: EHA-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2021; 32(3):309-322. Google Scholar
- Tuchman SA, Moore JO, DeCastro CD. Phase II study of dose-attenuated bortezomib, cyclophosphamide and dexamethasone (“VCD-Lite”) in very old or otherwise toxicity-vulnerable adults with newly diagnosed multiple myeloma. J Geriatr Oncol. 2017; 8(3):165-169. Google Scholar
- Mai EK, Miah K, Bertsch U. Bortezomib-based induction, high-dose melphalan and lenalidomide maintenance in myeloma up to 70 years of age. Leukemia. 2021; 35(3):809-822. Google Scholar
- Ludwig H, Delforge M, Facon T. Prevention and management of adverse events of novel agents in multiple myeloma: a consensus of the European Myeloma Network. Leukemia. 2018; 32(7):1542-1560. Google Scholar
- Bringhen S, Milan A, Ferri C. Cardiovascular adverse events in modern myeloma therapy - incidence and risks. A review from the European Myeloma Network (EMN) and Italian Society of Arterial Hypertension (SIIA). Haematologica. 2018; 103(9):1422-1432. Google Scholar
- Ihne S, Morbach C, Sommer C, Geier A, Knop S, Störk S.. Amyloidosis - the diagnosis and treatment of an underdiagnosed disease. Dtsch Arzteblatt Int. 2020; 117(10):159-166. Google Scholar
- Möller M-D, Gengenbach L, Graziani G, Greil C, Wäsch R, Engelhardt M.. Geriatric assessments and frailty scores in multiple myeloma patients: a needed tool for individualized treatment?. Curr Opin Oncol. 2021; 33(6):648-657. Google Scholar
- Antoine-Pepeljugoski C, Braunstein MJ. Management of newly diagnosed elderly multiple myeloma patients. Curr Oncol Rep. 2019; 21(7):64. Google Scholar
- Cordoba R, Eyre TA, Klepin HD, Wildes TM, Goede V.. A comprehensive approach to therapy of haematological malignancies in older patients. Lancet Haematol. 2021; 8(11):e840-e852. Google Scholar
- Rosko AE, Huang Y, Benson DM. Use of a comprehensive frailty assessment to predict morbidity in patients with multiple myeloma undergoing transplant. J Geriatr Oncol. 2019; 10(3):479. Google Scholar
- Soekojo CY, Kumar SK. Stem-cell transplantation in multiple myeloma: how far have we come?. Ther Adv Hematol. 2019; 10:2040620719888111. Google Scholar
- D’Agostino M, De Paoli L, Conticello C. Continuous therapy in standard- and high-risk newly-diagnosed multiple myeloma: a pooled analysis of 2 phase III trials. Crit Rev Oncol Hematol. 2018; 132:9-16. Google Scholar
- Stege CAM, Nasserinejad K, Klein SK. Improving the identification of frail elderly newly diagnosed multiple myeloma patients. Leukemia. 2021; 35(9):2715-2719. Google Scholar
- Cook G, Larocca A, Facon T, Zweegman S, Engelhardt M.. Defining the vulnerable patient with myeloma-a frailty position paper of the European Myeloma Network. Leukemia. 2020; 34(9):2285-2294. Google Scholar
- Larocca A, Dold SM, Zweegman S. Patient-centered practice in elderly myeloma patients: an overview and consensus from the European Myeloma Network (EMN). Leukemia. 2018; 32(8):1697-1712. Google Scholar
- Stauder R, Eichhorst B, Hamaker ME. Management of chronic lymphocytic leukemia (CLL) in the elderly: a position paper from an international Society of Geriatric Oncology (SIOG) Task Force. Ann Oncol. 2017; 28(2):218-227. Google Scholar
- Abel GA, Klepin HD. Frailty and the management of hematologic malignancies. Blood. 2018; 131(5):515-524. Google Scholar
Figures & Tables
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.