Barnes, Mason S.

Introduction: Light-chain amyloidosis (ALA) is a rare plasma cell disorder characterized by multiorgan involvement. Patients often present with non-specific symptoms, which can lead to a delay in diagnosis. This is associated with progressive organ involvement and shorter survival. Reducing the time from symptom onset to diagnosis is a critical step in improving outcomes. This study was designed to evaluate the clinical prodrome and challenges associated with the diagnosis of ALA from the patient perspective. Methods: This retrospective study surveyed patients with ALA through the online HealthTree® Cure Hub platform, which helps patients navigate their disease in a holistic way. The survey consisted of 32 questions, including 23 multiple-choice, 7 date-entry, and two free-response questions. Data on diagnosis and demographics were extracted from the electronic health record and self-reported data linked to HealthTree® Cure Hub. Results were reported as numbers and percentages, and comparisons were made by age (≥70 vs. <70 years) and sex. The following statistical methods were used: Fisher's exact test for categorical data, two-sample t-test and F-test for normally distributed continuous data, Wilcoxon rank-sum test for skewed data and Spearman correlation coefficient. Results: We surveyed 248 patients. Of those, a total of 87 patients with a self-reported diagnosis of ALA completed the questionnaire between March 12, 2023, and May 8, 2024. The median age was 66 years; 55% were female, and 91% were white. The most common initial symptoms prompting patients to seek medical evaluation were fatigue/weakness (47%), swelling (38%), shortness of breath (28%), chest pain (23%), and foamy urine (22%). Pain in hands/feet, carpal tunnel syndrome, loss of appetite, hoarseness, macroglossia, diarrhea, periorbital puffiness, nail changes, and erectile dysfunction each accounted for <10% of cases. Signs and symptoms were first detected by patients themselves in 58% of cases, by a physician in 33% and by a family member or friend for the rest. The median number of symptoms per individual was 2; 31% reported only one symptom, 37% 2-3, 18% 4-6, and 14% >6; with chest pain being the most common (39%), followed by swelling (22%), shortness of breath (13%), and foamy urine (13%); one patient reported erectile dysfunction and another reported arrhythmias as their only symptom. There was a correlation between hoarseness and nail changes (ρ=0.71), and carpal tunnel syndrome and orthostatic hypotension (ρ=0.63). Only 16% were aware of ALA as a condition prior to symptom onset. Thirty six percent presented to a general practitioner for initial evaluation; about 50% sought a specialist, including a myeloma/amyloid specialist (21%), and 14% were first evaluated in the emergency department. While most patients (45%) were diagnosed by a hematologist/oncologist, patients reported seeing a median of 3 physicians (range: 1-10) before the diagnosis was established. Among responders, 61% reported having myeloma at the time of testing. After diagnosis, about 50% were referred to a center specialized in ALA, and 72% met with a stem cell transplant specialist. Most patients reported receiving multidisciplinary care, with a median of 3 additional specialists participating in ALA care (range: 0-16). More than half of patients (59%) felt the diagnosis was delayed; among those, 42%, 24%, 24%, and 11% reported a delay of 1-6, 7-12, 13-24, and >24 months, respectively. The proportion of patients reporting >12-month delay was higher among older patients (54% vs. 27%, P=0.02). A quarter of responders felt their treatment was delayed. Half of patients felt the diagnostic delay contributed to a decrease in treatment efficacy and 48% did not feel that all medical professionals were knowledgeable about ALA. Only 38% felt the diagnostic process was simple and easy within their healthcare system. Conclusion: Most patients with AL amyloidosis initially present with non-specific symptoms, with fatigue/weakness being the most common. Over a third were evaluated first by a general practitioner, and about a third reported a diagnostic delay of >12 months. The diagnostic process is associated with high healthcare utilization, even among patients with concurrent multiple myeloma. Increasing disease awareness among patients and medical providers may shorten the time to diagnosis and contribute to improved outcomes.

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.