Оценка взаимосвязи между показателями конечных результатов пациента и семьи и множественными клиническими параметрами миэломы.

Introduction

Multiple Myeloma (MM) constitutes 17% of hematologic cancers and 1–2% of all cancers, with global incidence ranging from 0.54 to 5.3 per 100,000. In 2020, MM accounted for approximately 176,404 cases and 117,077 deaths worldwide 1 , 2 . In 2018, the age-standardized incidence of MM in Israel was 4.2/100,000 incidents 1 . MM primarily affects older adults, with a median age at diagnosis of 65 to 74, and is slightly more common in men than in women 3 . MM involves manifestations including hypercalcemia, renal failure, anemia, and bone lesions (CRAB criteria), along with symptoms like pain and fatigue 4 – 6 . Treatment options include immunomodulatory drugs (IMiDs), proteasome inhibitors (PIs), antibody-drug conjugates, autologous stem cell transplants, and monoclonal antibodies, in addition to Bispecific molecules, including bispecific antibodies (BisAbs) and bispecific T-cell engagers (BiTEs) 7 – 9 . For every MM patient, the physicians assess the patient response using clinical parameters, and the response to treatment is classified based on established criteria according to the International Myeloma Working Group (IMWG) 10 .

Numerous studies highlight that compared to other hematologic cancers, MM exhibits the highest incidence of symptoms, including pain, fatigue, constipation, and peripheral neuropathy. These symptoms can lead to psychological and social dysfunction 11 – 13 . Primary amyloidosis and MM are both clonal plasma cell proliferative disorders 14 . The most frequent type of association between MM and amyloidosis is the synchronous occurrence of both diseases 15 . Amyloidosis patients suffer from fatigue, change in taste, dry mouth, and weight loss 16 .

This disease profoundly affects patients’ physical, psychological, and social well-being, directly impacting their quality of life (QoL). Consequently, medical decisions increasingly consider patient QoL. Monitoring changes in patients’ QoL is crucial in routine medical care 17 . To address this, many studies utilize questionnaires to assess symptoms and treatment effects. In a study that was performed by Bennink et al. 18 , and aimed to evaluate the implementation of Patient Reported Outcome Measures (PROMs) in MM over a 2-year period, it was found that patients with MM seemed willing to complete PROMs; however, the integration of PROMs in individual patient care and shared decision making, which matches the expectations of both patients and healthcare professionals, is complex and requires team effort. However, most focus solely on the patient, overlooking the caregiver’s significant impact. The first Family Reported Outcome Measure (FROM-16) was introduced in 2014 19 . Some research has explored the impact of prostate cancer on partners 20 , 21 , while others have examined disability’s influence on family QoL 22 . In a review that was performed by Shah et al. 23 , and mainly aimed to assess the impact of patients’ disease on family members with various illnesses, it was found that caregivers experience a considerable impact on their physical, social, and psychological wellbeing. Besides, FROM-16 was identified as a generic, user-friendly tool that can be applied across all disease disciplines to measure the effect on the family caregiver.

Therefore, this study aimed to identify domains within both PROMs and FROM-16 19 , that correlate significantly with disease evaluation and clinical measurements, as well as examine their temporal change.

Materials and methods

Ethical statement

Prior to starting the data collection, this study received ethical approval from the Carmel Medical Center, Israel (approval number 0083-19-CMC). The recruitment of patients considered MM hematologic patients of the Institute of Hematology at Carmel Medical Center, Israel, only. All patients and caregivers participating in the study signed informed consent, and all methods were performed in accordance with the relevant Helsinki guidelines and regulations.

Charlson comorbidity index

In this study we summarized the main illness according to the Charlson comorbidity index. The Charlson comorbidity index is an assessment tool designed specifically to predict long-term mortality, and it includes: Age (0–4), myocardial infraction (0–1), Congestive heart failure (0–1), peripheral vascular disease (0–1), stroke (0–1), dementia (0–1), chronic pulmonary disease (0–1), connective tissue disease (0–1), peptic ulcer disease (0–1), liver disease (0–3), diabetes mellitus (0–2), hemiplegia (0–2), moderate to severe chronic kidney disease (0–2), solid tumor (0–6), leukopenia (0–2), and AIDS (0–6). The patients were categorized according to their final score: 1–2 mild, 3–4 moderate, and 5 + as severe score 24 – 26 .

PROMs & FROM-16 questionnaires

In this study, we used the EORTC-QLQ-C30 (European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire) questionnaires and the specific MM model questionnaire of EORTC-QLQ-MY20, which has been recommended in oncology studies and widely used in other fields of research 27 – 29 . To assess the family’s coping with the disease of the family member, we used a questionnaire FROM-16 questionnaire 19 . This questionnaire was filled out by the main family member caring for the patient.

Questionnaires scoring

ORTC-QLQ-C30 scoring.

The QLQ-C30 is composed of both multi-item scales and single-item measures (1–4 and 1–7 Likert scales). These include five functional scales, three symptom scales, a global health status, and six single items. All the scales and single-item measures converted to scores range from 0 to 100. A high scale score represents a higher level (e.g., higher disease symptoms, higher functional status, etc.) 30 . QLQ-C30 summary score- is calculated from the mean of 13 of the 15 QLQ-C30 scales (except the global quality of life scale and the financial impact scale) 31 .

EORTC-QLQ-MY20.

The QLQ-MY20 questionnaire incorporates three multi-item scales to assess disease symptoms, side effects of treatment, and future perspective. In addition, one single item assesses body image (1–4 Likert scales). The scoring approach for the QLQ-MY20 is identical in principle to that for the function and symptom scales / single-items of the QLQ-C30 (i.e., all domains converted to 0-100 scores) 32 .

FROM-16.

The FROM-16 scale has two domains- Emotional domain (the first 6 questions, maximum score of 12), and Personal and Social Life domain (the last 10 questions, maximum score of 20). Each question has three response options- from 0 (not at all) to 2 (a lot). The total score range (FROM-16-total) is 0–32. The higher the total score, the greater the effect on the family member’s quality of life.

All PROM questionnaires have undergone validation and are available in English, Hebrew, Russian and Arabic. Regarding the FROM-16 questionnaire that was not available in Hebrew, Russian or Arabic. For each language, the following process was followed: two professional translators translated the original questionnaire, the questionnaire was translated back into English, and the translation was approved by the author of the FROM-16 questionnaire 19 .

The reliability of the questionnaires

In order to examine the reliability of the tools on our populations, we calculated Cronbach’s alpha for each questionnaire. Full results are presented in Supplementary Appendix – Table S1 .

Inclusion-criteria

MM patients who receive treatment or are being monitored by the Hematology Institute at Carmel Medical Center, Israel.

Ability to be interviewed and complete questionnaires in: English, Hebrew, Arabic, or Russian.

Exclusion-criteria

Patients who meet one of the following criteria were excluded from the study :

Patients who are not interested in completing research questionnaires.

Patients who suffered from any other clinically significant medical disease, a cognitive or psychiatric condition that prevented the patient from completing the questionnaires.

In this study and in order to ensure high reliability, most of the patients and the family members were demanded to complete the questionnaires independently in their preferred language, when they came to the clinic for physical examination or treatment. However, in minor cases, when the patient/caregiver was not able to complete the questionnaires independently, the questionnaire was completed by interview with a Clinical Nurse Coordinator during the visit, or if the visit was not feasible, then via phone call. Clinical measures and demographic details of patients were monitored throughout, using the computerized coded data, and included mainly: Disease evaluation (Plasma Cells at Bone Marrow, Monoclonal Protein Level, Free Light Chain Level, CRAB Criteria, R-ISS Stage, Treatment Line, disease evaluation according to the International Myeloma Work Group), in addition to the covariates- age, gender, and comorbidity status.

Sample size considerations

The required sample size was calculated using Win-Pepi software, using 5% significance and 80% power. For a coefficient of 0.4 between PROMs and FROMs, and MM clinical evaluations, the minimum sample size required is 47 patients, for a coefficient of 0.35, the minimum sample size required is 62 patients. For a coefficient of 0.50, the minimum sample size required is 37 patients. In this study, we aimed to enroll the maximum number of cases available within the enrollment period. Initially, 75 patients enrolled in the study; however, not all of them were able to fill out the questionnaires as needed, due to loss to follow-up at the different time points of the study, or technical issues that prevented them from filling the questionnaires. In the current study, the sample size at each point was as described in Table 1 .

Statistical analysis

This study enrolled the maximum available cases within the enrollment period. In this study, we performed all statistical analyses using IBM SPSS software, version 25 33 . Due to the limited sample size, in addition to the fact that many of the variables used in this study were ordinal scale (e.g., the R-ISS, bone lesions, disease evaluation, etc.), we performed non-parametric statistical analysis tests that included Spearman correlation for associations between PROMs/FROM-16 and clinical variables. In addition, we used Friedman and Wilcoxon tests to compare scores over time.

Results

Patient characteristics

The research sample comprised 75 patients undergoing treatment for active MM (MM) (93.3%) or MM with amyloidosis (6.7%) at Carmel Medical Center, Israel (Table 2 ). Among them, 43 patients were newly diagnosed (57.3%), 29 were relapsed/refractory patients (38.7%), and 2 were on treatment holiday (2.7%). The disease stage was categorized according to the revised international staging system (R-ISS). The mean age of the participants was 72 years (M = 72, SD = 8.8), with an age range of 47–94. At the time of enrollment, each patient had the option to involve a caregiver in the study, with 54 patients opting for this (72%). The distribution of different treatment modalities at various time points is presented in Fig. 1 .

The research sample included patients with various comorbidities. Table 3 summarizes the distribution of Charlson comorbidity index components at the time of study recruitment 26 .

Correlation between disease evaluation and PROMs, FROM-16 (Supplementary Appendix Tables S2, S3)

QLQ-C30 & QLQ-MY20 Measurements : Better disease evaluation correlated with improved physical, role, emotional, cognitive, and social functions, as well as higher quality of life, better body image, and positive future perspectives. Conversely, it correlated with lower levels of fatigue, pain, insomnia, appetite loss, financial difficulties, disease symptoms, and treatment side effects.

FROM-16 questionnaire measurement : After 12 months, a significant correlation was found between FROM-16 and disease evaluation, indicating that better disease evaluation was associated with less severe emotional, personal, and social impacts on caregivers.

Correlation between calcium levels, renal function, hemoglobin, bone lesions (CRAB), and PROMs, FROM-16 (Supplementary Appendix Tables S4–S9)

The results indicated that better measures in the organ systems affected by MM (CRAB) were associated with better PROMs scores and less impact on family caregivers.

Worse renal function (manifested as higher creatinine) was linked to lower PROMs for treatment effectiveness and convenience, as well as worse emotional FROM-16 scores. Higher hemoglobin levels were associated with better global satisfaction and treatment effectiveness PROMs score ( p < 0.05) as well as lower emotional FROM-16 score ( P < 0.01). More severe bone lesions correlated with decreased patient function. ( P < 0.05).

PROMs correlation with FROM-16 (Supplementary Appendix – Tables S10, S11)

Most PROMs domains correlated with FROM-16 domains at least once during follow-up, with better patient functioning corresponding to less severe impacts on caregivers.

The results demonstrated that improvement in patient well-being (e.g., physical, role, emotional, etc.) correlated with a smaller effect on family caregiver. On the other hand, worse levels of fatigue, nausea & vomiting, pain, dyspnea, insomnia, appetite loss, financial difficulties, and side effects due to treatment correlated with a higher effect on family caring member.

Time as an independent variable

The mean values of the actual scores of the measures are presented in Table 4 . The results demonstrated that in general there was improvement in the different domains of PROMs or FROM-16 that were reported. In domains such as quality of life, disease symptoms, and future perspective there was significant ( P < 0.05) improvement from baseline to at least one follow-up point. Besides, there was an almost significant improvement in the domains body image scale ( p = 0.096), and FROM-16 emotional domain ( p = 0.075) (Table 5 ).

Discussion

PROMs/FROM-16 vs. disease evaluation

QLQ-C30 & QLQ-MY20 questionnaires and disease evaluation : Although we did not find a significant correlation between PROMs/FROM-16 and disease evaluation at time zero, most PROMs variables were correlated with disease evaluation at least at one point during follow-up. This finding might be due to the fact the study included both new patients, who had not yet received treatment and may have been asymptomatic, like patients who found incidental findings in blood tests, and patients who had already started treatment. Our study identified significant correlations between disease evaluation and various PROMs outcomes. Notably, better disease evaluation was associated with improved physical, emotional, and social functioning, as well as higher quality of life and reduced symptom burden. These findings align with previous research by Dubois et al. (2006), which observed improvements in PROMs outcomes among MM patients responding to treatment 34 . Moreover, a systematic review emphasized the complementary role of health-related quality-of-life measures in assessing treatment response and overall patient well-being, among MM patients 35 .

FROM-16 questionnaire and disease evaluation : After 12 months, we found a significant correlation between FROM-16 and disease evaluation, suggesting that better disease evaluation was associated with less severe emotional, personal, and social impacts on caregivers. This aligns with previous research validating the FROM-16 tool and underscores the importance of considering caregiver perspectives in evaluating patient well-being. In addition, the results of this study showed that the mean scores of FROM-16 were 14.26 (M = 14.26, SD = 7.78), and during the follow up the scores decreased. Our results were slightly lower, in both domains and in the total FROM-16, compared to Shah et al. 36 , who examined the mean FROM-16 scores of 99 family members of people with MM and found that the mean scores were 15.8 (mean = 15.8, SD = 6.3, n = 99).

PROMs/FROM-16 changes over time

MM patients experience a variety of disease-related events and symptoms such as pain, fatigue, renal failure, anemia, and psychological burden. These many aspects might have different importance across different disease periods 37 . A statistically significant change doesn’t mean clinically significant change. In order to assess whether this change is clinically significant, we should consider the minimal important difference (MID) 37 , 38 or minimal important change (MIC) as demonstrated by Shah et al. 39 .

QLQ-C30/ QLQ-MY20 questionnaires over time

In the questionnaires QLQ-C30 and QLQ-MY20, Kvam et al., (2010) proposed that ranges between 6 and 17 points (0–100 scale) are MID among MM patients. Mean score changes of less than 6 points are unlikely to be important to the patients 40 . According to our results, most of the domains in those two questionnaires demonstrated improvement between at least two points of follow-up. Among these domains were quality of life, disease symptoms, body image scale, and future perspective. In all these domains, we observed a mean change of 6 points or more. Therefore, there is a significant clinical change that was reported by the patients.

FROM-16 questionnaire over time

A study that was recently performed by Shah et al. 39 , and aimed to assess the responsiveness of FROM-16 to change and determine Minimal Important Change (MIC), among outpatient different clinics (including hematology), demonstrated that there was a significant positive correlation between the FROM-16 change scores and the patient’s disease severity change scores ( p < 0.001), and finally suggested that 4 will be the MIC value for FROM-16. In our study, Both FROM-16 domains improved over time. The results of our study showed that the total FROM-16 score improved from a mean score of 14.95 to 10.86 between the third month of the study, compared to the sixth month of the study (i.e., greater than the MIC = 4). The decrease in the effect on the caregiver over time is also supported by other studies, like Gottlieb and Wolfe 41 , that examined the family caregiving to persons with dementia found that caregivers’ coping resources consist of a complex and dynamic set of cognitive, affective, and behavioral responses that are aimed to regulate their emotions, solve, or improve the practical problems they face, and maintain the psychological resistance and fortitude needed to stay productively engaged in caregiving for a protracted period of time 41 .

Conclusion

An improvement in the disease evaluation according to the IMW criteria was correlated with improvement in the disease symptoms and disease manifestations as reported by the patient. Besides, the improvement was also associated with a reduced burden and a better ability to cope with the patient’s disease as reported by the caregiver (FROM-16). Most of the domains in PROMs improved by 6 points or more, which means a clinically significant change as defined in previous studies. Finally, the results of our study showed that the total FROM-16 score improved from a mean score of 14.95 to 10.86 between the third month of the study, compared to the sixth month of the study, i.e., higher than 4, the minimal important change as defined in a previous study. In summary, integrating PROMs & FROM-16 questionnaires as part of the standard of care, along with the available tools, will enable a better and holistic understanding of the patient’s status, and the ability of the patient and the caregiver to cope with MM.

Study limitations and future recommendations

Our study had several limitations, including a small sample size, recruitment of patients at different disease stages, and a relatively short follow-up period. Future research should aim to address these limitations by recruiting larger and more homogenous patient populations and extending the duration of follow-up thus monitoring the change in the patient (PROMs) and caregiver reports (FROM-16) after several regimens and lines of treatments. In addition, a longer follow-up will enable us to understand if the correlation between the clinical will persist over time. Furthermore, in this study we calculated Cronbach’s alpha of the tools, however, validation steps were not done and should be addressed in future research. Finally, specialists should consider integrating PROMs/FROM-16 evaluations into routine clinical assessments to better monitor patient status and address troubling symptoms.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Author contributions

Conceptualization, M.P.; methodology, K.M., M.P, and B.S.; validation, M.P. and B.S; investigation, M.B.H., K.M., R.D., E.M., O.V., S.P., A.W., and I.Z.; resources, M.P., M.B.H., E.M., O.V., S.P., and A.W.; data curation, M.P., M.B.H., K.M., R.D., E.M., O.V., S.P., A.W., and I.Z; writing—original draft preparation, K.M., B.S., and M.P.; writing—review and editing, B.S., and M.P.; supervision, B.S., and M.P.; project administration, M.P.; All authors have read and agreed to the published version of the manuscript.

Funding

No funding for this research.

Data availability

Data can be accessed via the corresponding author.

Declarations

Competing interests

Conflicts of Interest: Andrew Yule Finlay is a joint copyright owner of FROM-16. Cardiff University gains royalties from its use. Andrew Yule Finlay receives a share of these under standard university policy.

Institutional review board statement

According to current guidelines and following the Ethics Committee of the Carmel Medical Center, Haifa, Israel, the committee reviewed and approved this research project and study design with approval number 0083-19-CMC.

Informed consent

Informed consent was obtained from all subjects involved in the study.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Mouna Ballan-Haj and Kareem Midlej have contributed equally to this work.

References

1. Ludwig, H., Novis Durie, S., Meckl, A., Hinke, A. & Durie, B. Multiple myeloma incidence and mortality around the globe; interrelations between health access and quality, economic resources, and patient empowerment. Oncologist 25 , e1406–e1413 (2020).

2. Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2021. CA Cancer J. Clin. 71 , 7–33 (2021).

3. Kyle, R. A. et al. Review of 1027 patients with newly diagnosed multiple myeloma. Mayo Clin. Proc. 78 , 21–33 (2003).

4. Matsui, W. et al. Clonogenic multiple myeloma progenitors, stem cell properties, and drug resistance. Cancer Res. 68 , 190–197 (2008).

5. Brigle, K. & Rogers, B. Pathobiology and diagnosis of multiple myeloma. Semin Oncol. Nurs. 33 , 225–236 (2017).

6. Bahlis, N. J. & Lazarus, H. M. Multiple myeloma-associated AL amyloidosis: is a distinctive therapeutic approach warranted? Bone Marrow Transpl. 38 , 7–15 (2006).

7. Michels, T. C. & Petersen, K. E. Multiple myeloma: diagnosis and treatment. Am. Fam Physician . 95 , 373–383 (2017).

8. Multiple myeloma. Clinical features, Laboratory manifestations, and Diagnosis. https://www.uptodate.com/contents/multiple-myeloma-clinical-features-laboratory-manifestations-and-diagnosis?search=multiple%20myeloma&source=search_result&selectedTitle=1~150&usage_type=default&display_rank=1 . 2025

9. Cho, S. F., Yeh, T. J., Anderson, K. C. & Tai, Y. T. Bispecific antibodies in multiple myeloma treatment: a journey in progress. Front. Oncol. 12 , 1032775 (2022).

10. IMWG Uniform Response Criteria | Int’l Myeloma Fn. https://www.myeloma.org/resource-library/international-myeloma-working-group-imwg-uniform-response-criteria-multiple

11. Mols, F. et al. Health-related quality of life and disease-specific complaints among multiple myeloma patients up to 10 year after diagnosis: results from a population-based study using the PROFILES registry. Eur. J. Haematol. 89 , 311–319 (2012).

12. Gulbrandsen, N., Hjermstad, M. J., Wisløff, F. & Nordic Myeloma Study Group. Interpretation of quality of life scores in multiple myeloma by comparison with a reference population and assessment of the clinical importance of score differences. Eur. J. Haematol. 72 , 172–180 (2004).

13. Johnsen, A. T., Tholstrup, D., Petersen, M. A., Pedersen, L. & Groenvold, M. Health related quality of life in a nationally representative sample of haematological patients. Eur. J. Haematol. 83 , 139–148 (2009).

14. Rajkumar, S. V., Gertz, M. A. & Kyle, R. A. Primary systemic amyloidosis with delayed progression to multiple myeloma. Cancer 82 , 1501–1505 (1998).

15. Ríos-Tamayo, R. et al. AL amyloidosis and multiple myeloma: A complex scenario in which cardiac involvement remains the key prognostic factor. Life (Basel) 13 , 1-11 (2023).

16. Overview of amyloidosis. https://www.uptodate.com/contents/overview-of-amyloidosis?search=amyloidosis%20&source=search_result&selectedTitle=1~150&usage_type=default&display_rank=1#H10 .

17. Osborne, T. R. et al. Understanding what matters most to people with multiple myeloma: a qualitative study of views on quality of life. BMC Cancer . 14 , 496 (2014).

18. Bennink, C. et al. Improving Outcome-Driven care in multiple myeloma using Patient-Reported outcomes: A qualitative evaluation study. Patient 16 , 255–264 (2023).

19. Golics, C. J., Basra, M. K. A., Finlay, A. Y. & Salek, S. The development and validation of the family reported outcome measure (FROM-16)© to assess the impact of disease on the partner or family member. Qual. Life Res. 23 , 317–326 (2014).

20. Wootten, A. C. et al. The impact of prostate cancer on partners: a qualitative exploration. Psychooncology 23 , 1252–1258 (2014).

21. Kornblith, A. B., Herr, H. W., Ofman, U. S., Scher, H. I. & Holland, J. C. Quality of life of patients with prostate cancer and their spouses. The value of a data base in clinical care. Cancer 73 , 2791–2802 (1994).

22. Poston, D. et al. Family quality of life: a qualitative inquiry. Ment Retard. 41 , 313–328 (2003).

23. Shah, R., Ali, F. M., Finlay, A. Y. & Salek, M. S. Family reported outcomes, an unmet need in the management of a patient’s disease: appraisal of the literature. Health Qual. Life Outcomes . 19 , 194 (2021).

24. Charlson, M. E., Carrozzino, D., Guidi, J. & Patierno, C. Charlson comorbidity index: a critical review of clinimetric properties. Psychother. Psychosom. 91 , 8–35 (2022).

25. Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40 , 373–383 (1987).

26. Charlson, M., Szatrowski, T. P., Peterson, J. & Gold, J. Validation of a combined comorbidity index. J. Clin. Epidemiol. 47 , 1245–1251 (1994).

27. Cocks, K. et al. An international field study of the reliability and validity of a disease-specific questionnaire module (the QLQ-MY20) in assessing the quality of life of patients with multiple myeloma. Eur. J. Cancer . 43 , 1670–1678 (2007).

28. Osborne, T. R. et al. What issues matter most to people with multiple myeloma and how well are we measuring them? A systematic review of quality of life tools. Eur. J. Haematol. 89 , 437–457 (2012).

29. Wisløff, F. et al. Measurement of health-related quality of life in multiple myeloma. Br. J. Haematol. 92 , 604–613 (1996).

30. FAQ Categories. Scoring | EORTC – Quality of Life: EORTC – Quality of Life. https://qol.eortc.org/faq-category/scoring/

31. Emotional. Scoring of the QLQ-C30 Summary Score.

32. QLQ-MY. EORTC QLQ - MY20 Scoring Manual. https://www.eortc.be/qol/ScoringInstructions/MY20%20Summary.pdf

33. IBMCorp. IBM SPSS Statistics for Windows, Version 25.0 Armonk, NY (2017).

34. Dubois, D. et al. Descriptive and prognostic value of patient-reported outcomes: the bortezomib experience in relapsed and refractory multiple myeloma. J. Clin. Oncol. 24 , 976–982 (2006).

35. Sonneveld, P. et al. Review of health-related quality of life data in multiple myeloma patients treated with novel agents. Leukemia 27 , 1959–1969 (2013).

36. Shah, R. et al. Measurement of the major ignored burden of multiple myeloma, pernicious anaemia and of other haematological conditions on partners and family members: a cross-sectional study. Eur. J. Haematol. 113 , 117–126 (2024).

37. Kvam, A. K. & Waage, A. Health-related quality of life in patients with multiple myeloma–does it matter? Haematologica . 100 , 704–705 (2015).

38. King, M. T. A point of minimal important difference (MID): a critique of terminology and methods. Expert Rev. Pharmacoecon Outcomes Res. 11 , 171–184 (2011).

39. Shah, R. et al. Responsiveness and minimal important change of the family reported outcome measure (FROM-16). J. Patient Rep. Outcomes . 8 , 38 (2024).

40. Kvam, A. K., Wisløff, F. & Fayers, P. M. Minimal important differences and response shift in health-related quality of life; a longitudinal study in patients with multiple myeloma. Health Qual. Life Outcomes . 8 , 79 (2010).

41. Gottlieb, B. H. & Wolfe, J. Coping with family caregiving to persons with dementia: a critical review. Aging Ment Health . 6 , 325–342 (2002).

Associated Data

Supplementary Materials

Supplementary Material 1

Data Availability Statement

Data can be accessed via the corresponding author.