The conventional method for risk stratification in MM has primarily focused on disease-based characteristics. However, there has been a growing emphasis on the role of frailty as a predictive factor for adverse outcomes in older MM patients with the development of several frailty scoring systems. In this study, we adopted the cumulative deficit approach to characterize frailty in a real-world cohort of patients with newly diagnosed MM, the majority of whom received novel therapies. Approximately 40% were classified as frail and had decreased PFS and OS compared to non-frail patients. As expected, the prevalence of frailty increased with age, but 28% of those < 65 years were classified as frail and had a trend towards decreased OS, although not reaching statistical significance. Equally important, among patients ≥ 75 years, 38% were non-frail and had longer survival compared to frail patients, emphasizing that advanced age alone is inadequate to classify patients as frail. Indeed, patients classified as frail based on age alone (> 80 years) using the IMWG index have been shown to have better outcomes than patients > 80 years with additional frailty criteria, highlighting the importance of distinguishing these two groups.37 In addition, the IMWG system, has not been shown to perform well in the subset of patients > 75 years.38 While the incorporation of ECOG PS has the potential to increase uptake of frailty tools in practice, this measure does not always correlate with patient-reported functional status.17, 18 This was reflected in our findings, where 29% of patients with ECOG PS < 2 were frail and had inferior outcomes compared to non-frail patients within this group.
The cumulative deficit approach has been adopted previously to define frailty in MM using population-based registries, classifying patients into 2 or more groups with different outcomes.23, 39, 40 Given the nature of these studies, disease-specific characteristics including stage and cytogenetic profile were not available. Our study also spans a more recent treatment era (up to 2018), and we demonstrate the prognostic impact of frailty both before and after 2013, and in the different groups based on induction regimen. In this study, we observed that frail patients were older, had higher disease stage, a slightly higher rate of high-risk FISH abnormalities, and lower probability of early transplantation compared to non-frail patients. However, the prognostic impact of frailty was independent of these factors. The relationship between frailty and advanced disease characteristics suggests that higher disease burden may contribute to frailty in at least a subset of patients. With the ongoing efforts to tailor treatments based on frailty, it is important to differentiate disease-related frailty, which has the potential to improve once disease control is achieved.
As studies assessing the impact of frailty in MM have largely focused on treatment toxicity and survival, the association with patient-reported outcomes is underexplored. Compared to non-frail patients, those who were frail had higher symptom burden, reporting higher scores for fatigue and pain, and lower scores for QOL. These have been shown to be the most common symptoms reported in newly diagnosed myeloma patients and associated with increased healthcare utilization, dose reduction, and premature treatment discontinuation.41–46 This emphasizes the need for aggressive symptom management especially in frail patients.
In addition to frailty, social support and SES are important factors that need to be considered when managing patients with MM. We observed that patients who did not have a partner were more likely to be frail and to have higher disease stage, which may be, at least in part, related to delayed diagnosis. Not having a partner was also associated with increased risk of death independent of these factors. Similarly, living alone was associated with decreased survival. A separate study similarly reported that widowed patients with hematologic malignancies were more likely to be frail and to have decreased survival compared to both single/divorced patients and those with a partner, while the latter 2 groups exhibited similar outcomes.47 Given the low sample size, with 81% of patients being married, we could not compare outcomes separately between individual groups. Interestingly, the prognostic impact of relationship status in our study was more pronounced in females.
The impact of SES on outcomes of patients with MM has been explored in several studies mainly using neighborhood characteristics and insurance type, with mixed results.27–29, 48 Using individual housing characteristics, we observed that patients belonging to the highest quartile for SES had improved survival and were less likely to be frail, but this association was not statistically significant when adjusting for age and disease stage. However, our cohort, which includes patients seen at a single tertiary care center, may not provide an adequate representation of underserved populations. Nevertheless, the management of all patients with MM should take into consideration both disease-specific and patient-specific factors and include an assessment of physiological status, financial resources, transportation, and social support.
This study is limited by its retrospective nature and potential for selection bias by including only those patients with available requisite data for calculating the FI. Our cohort also reflects patients who were able to obtain care at a tertiary institution. So, it is likely that the proportion of frail patients is higher in the general population. It is important to highlight that in this study, we assessed the impact of frailty at the time of diagnosis only. However, frailty is dynamic and changes during treatment in a large proportion of patients,40 which represents a future opportunity to expand on this research utilizing a prospective, longitudinal approach.
Despite these limitations, our study has several strengths: it is based on a real-world population, includes both transplant-eligible and ineligible patients, and characterizes frailty using an easy to calculate index. In addition, this FI does not include chronological age and relies of patient-provided information for functional status. The availability of data on disease characteristics, including cytogenetics, transplant status, and treatment response, allowed us to evaluate the prognostic impact of frailty, social support, and SES after controlling for these variables. In addition to survival, this study demonstrates an association between frailty and symptom burden derived from patient-completed questionnaires.
As the prognostic impact of frailty is increasingly appreciated, its assessment in routine practice remains a major barrier to frailty-adapted care. Thus, it is important that proposed tools are practical and feasible, even in resource-limited settings. When assessing functional status, patient-provided information is more accurate than physician assessments and should be adopted. Our index relied on a simple and short questionnaire that can fit in a busy clinical practice and can be adapted based on available resources. It is important to highlight that while most frailty tools classify patients into two or more discrete groups, individuals may exhibit various degrees of frailty, and interventions should be tailored accordingly.