Ailawadhi, Dhaani

Abstract Background: MM is the second most common hematologic malignancy, which despite significant therapeutic advancements, remains incurable. The complexity of MM and its intricate management creates substantial informational demands for patients navigating their care. Methods: A cross-sectional survey study (Mayo Clinic IRB # 25-004671) was conducted to assess MM patients’ awareness about disease basics. A voluntary English language survey (47 questions) encompassing multiple domains of patient MM awareness was administered (REDCap) via the patient EMR portal (Mayo Clinic) and MM support groups nationally. Responses were analyzed using descriptive and inferential statistics (R; Bluesky Statistics, V 10.3.4). Multiple model logistic regression was used for multivariate analysis using age (≤70 or >70 yrs), gender, education (high school or less vs bachelor's and above), income (≤$74999 vs ≥$75000) and insurance status (private vs public). Results: The survey was completed by 2353 patients (pts) with 2177 MM (83.4% active, 16.6% smoldering) and 176 MGUS. Among MM pts median age was 63 yrs with 52.5% males, 91.5% white and 95.7% non-Hispanic. 17.5% had a prior diagnosis of MGUS and 20.2% of smoldering MM prior to MM, and 79.5% were not aware of MM prior to their own diagnosis. Majority (77%) depended most on their doctors to gain MM information but 24.3% reported not having sufficient discussion about MM at diagnosis. 31.2% were not sure what type of MM they had, 22.9% could not correctly report the cell of MM origin (plasma cell), 40.6% did not receive MM stage information at diagnosis, 45.4% were either unsure or reported wrong risk factors for MM and 22% were unsure or mistaken about MM symptoms. Majority (62.9%) did not understand genetic risk categories of MM, 32.3% did not report adequate understanding of MM tests and 31.9% did not understand the difference between response and remission. 57.5% reported not understanding about MRD status and 57.1% never had MRD testing. Majority (86.8%) felt they got adequate opportunity to participate in treatment decisions but only 54.2% felt confident in managing MM with their current knowledge and 28.2% had not discussed their prognosis although 77.7% wanted to know about it. Majority (64.4%) felt they had knowledge gaps about MM and its management. Multivariate analysis showed that females were less likely (OR 0.67) while those with public insurance were more likely (OR 1.35) to report receiving adequate information at diagnosis. Older pts (OR 0.55), males (OR 0.43), with lower education (OR 0.5), lower income (OR 0.5) and public insurance (OR 0.72) were less likely to identify the cell of MM origin correctly. These same groups were significantly less likely to be aware of types of MM as well. Older pts (OR 0.73), with lower education (OR 0.6), lower income (OR 0.77) and public insurance (OR 0.76) were less likely to be aware of MM genetic risk categories. Females (OR 0.81), with lower education (OR 0.68) and lower income (OR 0.63) were less likely to understand MM lab tests. Females and those with lower income were significantly less likely to report receiving adequate information about treatment options. Females reported that they did not get adequate opportunities to participate in treatment decisions (OR 0.65). Older pts, and those with lower income, lower education or public insurance were less likely to understand minimal residual disease (MRD) and all these groups were less likely to have had MRD testing. Those with lower income (OR 0.75) and lesser education (OR 0.72) felt less confident in managing MM with current knowledge. Older pts, females and those with lesser education, lower income and public insurance were also significantly less likely to have discussed about MM prognosis. Conclusion: This study demonstrates substantial basic knowledge deficits integral to managing and prognosticating MM among patients. Patients from unfavorable sociodemographic-economic groups were more likely to have awareness gaps and report lack of information which is integral to having adequate understanding of MM being provided to them. These findings highlight the critical need for structured, comprehensive educational interventions that address fundamental disease concepts to empower pts, overcome healthcare disparities, and engage pts in shared decision-making.

e13661 Background: MM survival has improved significantly, resulting in increased survivorship and caregiver burden and is crucial to understand the real-world impact of MM. We analyzed results from a large, prospective MM patient survey using ML models to identify key predictive factors for survivorship and caregiver burden. Methods: A 45-item questionnaire spanning sociodemographics, treatment preferences, symptom burden and caregiver impact was distributed to MM patients at Mayo Clinic and International Myeloma Foundation support groups. Questions about quality of life (QOL) and treatment satisfaction (TS) represented survivorship outcomes while caregiver workday loss (WL) and job loss (JL) were surrogates for caregiver burden. 6 ML models (multinomial, random forest (RF), XGB (XGBoost), k-NN, GBM, Naïve Bayes) with a training: testing cohort of 80:20 were used to predict each of the 4 outcomes independently, considering model accuracy, C-index, precision, recall, and F1 score (Table), and using R studio (V4.4.1) and Bluesky Statistics (V10.3.4). Results: 2,239 participants (54% male, 89% Whites) with median age 68 yrs completed the survey. 71% respondents had a college degree, 44% resided in Midwest states, 62% reported household income >$75,000, and 67% had Medicare/Medicaid. Median age at MM diagnosis was 67 yrs with 58% diagnosed within the past 5 yrs and 73% currently receiving MM treatment. 72% reported their spouse as the primary caregiver. XGB was the best predictive model for QOL with gender, household income, stem cell transplant, CAR-T therapy, support from medical staff, and symptom burden (pain, neuropathy, mental well-being) as significant predictors. The RF model was the best for TS with pain and neuropathy levels as the most impactful predictors. For caregiver burden, XGB was the best model for WL with gender, household income, CAR-T therapy, financial strain, lack of support from medical staff, self-workdays missed, self-job loss, and pain and neuropathy levels as significant predictors. For JL, RF was the best model with significant variables being workdays missed by self, increased healthcare needs, symptom burden (pain, neuropathy, and mental well-being), and restricted physical activity. Conclusions: Our study shows the significant impact of MM survivorship and efficiently identifies predictive factors associated with survivorship and caregiver burden from a large prospective dataset using validated ML models. These findings highlight the complex challenges from MM and provide contemporary ML methods to better understand their interplay such that a framework of real-world interventions can be developed. Outcome Best ML Model Accuracy C-Index Precision Recall F1 score QOL XG 0.996 0.996 0.996 0.997 0.996 TS RF 0.984 0.900 0.937 0.948 0.937 WL XGB 0.984 1.000 0.944 0.947 0.945 JL RF 0.989 0.800 0.829 0.848 0.835

Introduction: Therapeutic advancements for MM have led to improved patient survival, but notable differences in management exist. Novel treatment options are resource-intensive and require adequate patient awareness as well as consideration of patient preferences such that informed decision making is practiced. Despite this, detailed understanding of factors that impact patient decisions regarding healthcare choices, and potentially contribute to variability in healthcare utilization, are under-studied. Additionally, with improved survivorship, the medical, psychosocial, and financial burden of MM management needs to be better understood. Methods: An anonymous, online, 45-question English survey exploring demographics, social determinants of health, selected clinical characteristics, treatment preferences, and survivorship burden was distributed to MM patients within Mayo Clinic and to all International Myeloma Foundation (IMF) support groups. Descriptive statistics were utilized to document participant socio-demographic characteristics, selected treatment history, factors affecting their preferences, and MM survivorship burden. Results: A total of 2239 participants responded to the survey with 54% males, median age 68 years (range 21-95) and median age at MM diagnosis of 67 years. Non-Hispanic Whites (NHW) were the largest (89%) racial-ethnic group. Most participants (71%) had a college degree and 67% had Medicare or Medicaid as primary insurance. MM was diagnosed within 5 years in 58% and 73% were currently on MM treatment at the time of responding to the survey. Majority (76%) had never been on a clinical trial, 59% had received stem cell transplant, 8% had undergone CAR-T therapy and 8% had received Bispecific antibodies as part of their MM management. Majority (64%) preferred oral drugs while 19% preferred injections and 14% infusion therapy.17% had ≥4 healthcare visits per month for MM treatment, 91% used their personal vehicle as the mode of transport for these appointments and the median time to travel to the treatment facility was 30-minutes.Survey participants reported that >10 days of work were missed per year due to MM management among 15% of the patients and 9% of the caregivers, while 15% reported suffering job loss related to MM management. Top factors for treatment decisions were physician recommendations (92%), effectiveness of treatment option (88%), understanding of the treatment(74%), side effects (57%), and family/caregiver input (38%). Psychological challenges and financial strain were reported by 38% and 18% of the respondents, respectively. Increase in healthcare needs since MM diagnosis were reported by 48% and majority reported ongoing symptom burden including fatigue (78%), pain (53%) and neuropathy (60%). Furthermore, of the participants with these symptoms, 67% reported that their fatigue was not relieved by rest, 73% reported their pain to be moderate to severe, and 29% reported their neuropathy to be moderate to severe. Only 42% reported being fully active with the rest reporting varying degrees of functional inabilities. Nearly half (47%) of the participants reported lack of interest or enjoyment in activities they used to like prior to MM diagnosis, however majority (74%) still reported “good” or “excellent” quality of life. A small number of participants reported lack of support from family and friends (4%), lack of support from their healthcare team (3%), difficulty accessing healthcare due to financial hardship (7%) or difficulty accessing healthcare due to job commitments (3%). Conclusions: Decision-making for MM treatment is complex and there are several factors that affect patients behavior. Improved survival in MM is associated with significant survivorship and caregiver burden which can also affect the patient's treatment choices for subsequent therapy. These factors must be addressed systematically to achieve equitable healthcare utilization, shared decision making, and optimal clinical, psychosocial and financial wellbeing of patients with MM.

Introduction: As therapeutic options for cancer increase at an unprecedented rate, understanding patient's awareness and attitudes is imperative to optimize their participation in shared decision making. Patient surveys are a powerful tool to gain insight into patient awareness but are cumbersome to administer on a large scale. With the advent of technology and virtual care, electronic surveys are being utilized more widely but their validity compared to approaching the patients in-person has not been established. We conducted a survey regarding awareness and preferences for clinical trials in-person and electronically and compared results systematically in a matched cohorts of patients with MM. Methods: A 52-question survey (including the domains: knowledge, trust, fear, external influence and optimism) was administered in-person to cancer patients at Mayo Clinic campuses (NCT04630431), of which patients with MM were identified for this analysis. The same survey was administered to MM patients enrolled in the online portal HealthTree® Cure Hub. Observations from the in-person and online cohorts were matched and weighted based on coarsened exact matching utilizing the “MatchIt” package based on age, gender, race, ethnicity, education, and marital status. Question responses were converted to an agreeance score scaled from 1 (Strongly Disagree) to 5 (Strongly Agree). Mean and standard deviation measurements were calculated. Weighted t-tests were conducted using these numeric scores and the matched weights. Results: Final matched cohorts included 83% (n=65) of the in-person and 69% (n=149) of the online participants. Mean age was 63.5 (± 8.5) years with 52% females for in-person vs. 65.9 (± 7.5) years and 54% females in the online participants. In-person cohort included 83% White, 14% Black and 12% Hispanic participants while online cohort had 94% White, 5% Black and 13% Hispanics/Latinos. Education level with 4-year degree or higher was reported by 66% in-person and 75% online participants. Both cohort respondents on average rated a strong likelihood of participating in a clinical trial (in-person 4.04, CI: 3.73-4.35; online 4.34, CI: 4.19-4.5). There was significant agreeance between the 2 cohorts on majority of the questionnaire domains. For basic knowledge about clinical trials, while none of the individual questions in the category were significantly different, the online group had a higher aggregate agreeance score of 3.91 vs. 3.73 for the in-person group with a significant difference of 0.18 (CI: 0.5-0.31, p=0.008). For external influence, the online group had a higher aggregate agreeance score (3.52) than the in-person group (3.15; Difference: 0.37, CI: 0.22-0.52, p<0.001), with the only question-level significant difference being for the question that regulatory agencies effectively control what is being advertised by the pharmaceutical industry (4.46 ± 0.64 vs. 3.48 ± 0.76, p<0.001). Conclusions: Online surveys are an effective tool for gaining insight into patient understanding, attitudes and awareness on a variety of topics and can be conducted efficiently on a larger scale than in-person studies, which may be more time and resource intensive. Our study provides evidence of general agreeance between a matched cohort of in-person and online survey respondents, providing validity to carefully conducted, planned online survey tools. The online cohort in our study overall had better knowledge about clinical trials and a better understanding about external influences on available clinical trial result information. These results will help boost studies to collect patient-level responses such that their beliefs and awareness can be better understood and incorporated in optimizing healthcare resource utilization.

Introduction: Treatment options for multiple myeloma (MM) are evolving rapidly with recent introduction of BCMA-targeting CAR-T (Ide-cel, Cilta-cel) and bispecific antibody (Bi-Ab; Teclistamab) alternatives. These are complex, logistically demanding therapies, and patient awareness/knowledge can affect treatment decisions. There are traditionally underserved patients in MM, and awareness among them must be investigated to uncover disparities which may impact their access to and choice of novel therapies, contributing to disparate outcome. Methods: The study involved an anonymous 25-question online English language survey covering basic demographics, understanding of available T-cell directed MM treatments (CAR-T, Bi-Ab), and preferences for these agents. MM patients within the Mayo Clinic database and members of International Myeloma Foundation's MM support groups were sent a link to the voluntary survey. The survey was active from March 30 until July 12, 2023. Descriptive statistics were calculated with R language (Bluesky Statistics, V7.40), and categorical variables among outcome demographic groups were compared using Pearson Chi-square test (p-value ≤0.05 considered statistically significant). Multiple model logistic regression was used for multivariate analysis amongst statistically significant outcomes. Results: The survey had 2370 respondents with median age 68 years (range: 30-95) and median age at MM diagnosis 62 years (range: 18-88). Time since diagnosis was <5 years in 59.5%. Majority of the respondents (89.4%) were non-Hispanic Whites (NHW). Maximum education categories were high school (10.9%), some college but no degree (18.1%), associate/bachelor (39.7%), and master's/professional (32.3%). Geography and type of residence were reported as: West (24%), Midwest (46.2%), Northeast (3.3%), South (24.8%), outside U.S. (1.7%), and rural (26.7%), urban (21.4%), suburban (51.9%). The goals of MM treatment in order of reported importance were complete remission (66.1%), longer survival (55.3%) and low symptom burden (43.3%). Only 6.5% and 2.6% of respondents had received CAR-T and Bi-Ab, respectively, while 27.9% and 70.5% had never heard of CAR-T and Bi-Ab, respectively. A high proportion of respondents did not understand the risks/benefits of CAR-T (65.5%) or Bi-Ab (90.7%). Given a preference among CAR-T products, 5.4% would prefer Cilta-cel, 1.9% Ide-cel and 92.7% were not sure/no preference. Amongst only those who reported understanding the risks/benefits of CAR-T, the majority (73%) said they would prefer Cilta-cel. The majority (87.5%) were not sure of any differences between the two CAR-T products while some reported different targets (6.3%), response rate (5.9%), response duration (6.2%), side effects (4.9%), and manufacturing time (2.7%). The biggest concern for both CAR-T and Bi-Ab was side effects. Majority (89.4%) were not sure that Teclistamab Bi-Ab targeted BCMA, and nearly 70% felt that CAR-T was superior to Bi-Ab for treating MM. Reported sources of knowledge about MM treatment were healthcare team (82.6%), online resources (43.6%) and other patients/support groups (37.9%). Several demographic characteristics that significantly affected patient's responses to different questions in the survey. Selected questions and respondent categories with statistically significant results in multivariate analysis are shown in Table 1. Generally, higher education, living in northeast U.S., urban or suburban residency, and >5 years since MM diagnosis were associated with better awareness/understanding of CAR-T and Bi-Ab. Respondents with >70-year age were less likely to be aware of these agents or understand their risks/benefits. Race-ethnicity had no statistically significant associations in this cohort of predominantly NHW. Conclusions: This large cross-sectional study identifies lack of adequate awareness regarding CAR-T and Bi-Ab and provides a glimpse of current understanding of these complex therapies. There are disparate groups including rural, less educated, elderly and those with recent diagnosis of MM who have significantly lesser awareness. Some better-informed respondents had preferences among these treatments. Targeted awareness campaigns should be developed so that knowledge gaps can be bridged and patients across all sociodemographic groups are better equipped to take informed decisions about MM treatment.