Clinical Trial Enrollment Research Articles

Barriers and facilitators to enrollment in pediatric clinical trials: an overview of systematic reviews

BackgroundRecruiting a sufficient number of patients is often a challenge for conducting clinical trials. Published data reveal that only 10% of eligible patients according to inclusion and exclusion criteria are enrolled in clinical trials. Consequentially, identifying barriers and facilitators may improve enrollment. These factors may differ in the pediatric population, for example, due to the involvement of parents in the decision-making process. We aimed to conduct an overview of systematic reviews to summarize the barriers and facilitators influencing the enrollment of pediatric participants in clinical trials.MethodsA systematic literature search in PubMed and Epistemonikos of published systematic reviews focusing on barriers and facilitators influencing the enrollment of pediatric patients in clinical trials was conducted. Study selection, data extraction, and quality assessment were performed by two authors independently. The methodological quality was judged using a critical appraisal tool. Finally, data were narratively synthesized.ResultsOf 283 identified systematic reviews, four met the inclusion criteria and were included in the overview. Parents belonging to an ethnic minority or having low socioeconomic status were identified as barriers to enrollment whereas higher parental education and higher age served as facilitators. Additionally, existing expectations, previous treatment experiences and preferences, study duration, type of control group, and the child’s attitude toward study participation could favor or hinder participation. Furthermore, physicians’ opinions of study-related treatments may also influence the enrollment process.ConclusionThis overview provides a summary of barriers and facilitators to the enrollment of pediatric patients in clinical trials. Taking into account this information may enhance the enrollment of this hard-to-reach population.

Expansion of an Academic Molecular Tumor Board to Enhance Access to Biomarker-Driven Trials and Therapies in the Rural Southeastern United States.

Targeting tumor-specific molecular alterations has shown significant clinical benefit. Molecular tumor boards (MTBs) connect cancer patients with personalized treatments and clinical trials. However, rural cancer centers often have limited access to MTB expertise. We established an academic-community partnership expanding our academic MTB to affiliated rural community cancer centers. We developed a centralized molecular registry of tumors (MRT) to aggregate the comprehensive genomic profiling (CGP) results and facilitate multidisciplinary MTB review. Of the 151 patients included, 87 (58%) had actionable genomic biomarkers, 42 (28%) were eligible for a targeted off-label therapy, and 27 (18%) were matched to a clinical trial. Of those with a clinical trial match, only 1 of 27 (3%) was enrolled in the identified trial. One year into implementation, community oncology providers were anonymously surveyed on persistent barriers to precision treatment utilization. The primary barriers to clinical trial enrollment were the distance to the trial center (70%), lack of transportation (55%), and lack of local trials (50%). This study offers a framework to improve access to molecular expertise, but significant barriers to the equitable use of CGP and trial enrollment persist.

Facilitation of Enrollment onto Cancer Clinical Trials Using a Novel Navigator-Assisted Program: A Cross-Sectional Study.

Clinical trials are essential to the advancement of clinical therapies that improve the outcomes of people with cancer. However, enrollment in clinical trials remains a challenge. The Clinical Trial Navigator [CTN] Program was designed to address the current gap in the cancer care journey by assisting with the clinical trials search process. Between March 2019 and July 2024, applicants of the CTN program included people with cancer, their family members, and/or their care team. Applicants entered the CTN program through a REDCap® survey that collected the patient's medical history. A final curated list of potential clinical trials was provided to the applicant. Metrics of success included clinical trial referral and enrollment, and we examined the factors that impacted these outcomes. A total of 445 people with cancer applied to the CTN program during the study. Of the 262 patients with referral and enrollment information, a trial referral occurred in 27.5% [n = 72]. Of the 72 patients who were referred to a clinical trial, 13 [18.1%] were enrolled, 9 [12.5%] are pending enrollment, and 50 [69.4%] were not enrolled. We identified a potential trial for 88% of applicants, with a median of one potential trial per patient. Physicians were highly involved as applicants. The CTN program is successful in searching for clinical trials for people with cancer. Ongoing implementation into other Canadian sites, assessments of patient-reported outcomes, website and social media campaigns, and research into the factors that impact referral and enrollment are underway.

Abstract 4137986: Evaluation of an AI-Based Clinical Trial Screening Method Through a Randomized Controlled Implementation Study

Background: Clinical trial screening is labor-intensive, time-consuming, and error prone. We have developed RECTIFIER, an AI-based clinical trial screening tool, to enhance the efficiency and accuracy of patient recruitment. This study aims to evaluate RECTIFIER's effectiveness compared to manual screening in a randomized implementation study. Methods: This study was designed as an implementation study as part of an active heart failure trial named COPILOT-HF (NCT05734690). Potential eligible patients were identified via a structured electronic medical record query and randomized to be screened for clinical trial eligibility either by RECTIFIER or manually by clinical staff. The outcome measures included the number of patients contacted, and the number of patients reached for clinical trial enrollment. Data was collected over a period of 3 months. Results: A total of 3834 patients were included in the study, with 1919 patients randomized to the RECTIFIER group and 1915 patients to the manual screening group (Figure). Study staff could manually screen only 1367 patients at the end of the 3-month period. RECTIFIER identified more eligible patients compared to manual screening (833[43.4%] vs. 284[14.8%], p<0.001). The most common reason for exclusion was absence of symptomatic heart failure in both groups (287 in RECTIFIER 304 in manual screening). Screening via RECTIFIER also led to a higher contact rate, with 455 patients being contacted compared to 216 in the manual group (23.7% vs. 11.3%, p<0.001). The percentage of patients who could be reached for an enrollment call was also higher in the RECTIFIER group, with 330 patients responding versus 164 in the manual group (17.2% vs. 8.6%, p<0.001). Conclusion: Screening via RECTIFIER significantly outperformed manual screening in identifying eligible patients, contacting them, and eliciting responses. These results suggest that integrating AI into clinical trial recruitment processes can enhance efficiency and effectiveness, potentially leading to increased patient participation in clinical trials. Future studies with sufficient power should assess recruitment endpoints and explore scalability.

Unconventional Histopathological Features With KIAA1549::BRAF Fusion in Advanced Prostatic Adenocarcinoma.

This report delineates an intriguing example of advanced prostatic adenocarcinoma displaying distinctive histopathological characteristics associated with a KIAA1549::BRAF fusion, a genomic anomaly predominantly identified in central nervous system tumors. A 66-year-old man, presenting with acute renal failure, exhibited metastatic disease involving pelvic soft tissue, bladder, liver, and bone. Histological examination revealed a markedly unconventional morphology within the prostate, characterized by infiltrative tumor sheets exhibiting abundant vacuolated cytoplasm, hyperchromatic nuclei, and irregular nuclear membranes, distinct from typical prostatic adenocarcinoma. Immunophenotyping confirmed NKX3.1 positivity and GATA3 negativity. Molecular analysis revealed the rare KIAA1549::BRAF fusion alongside pertinent mutations in phosphatidylinositol 3-kinase, phosphatase and tensin homolog, and tumor protein 53 genes. Despite diverse therapeutic interventions targeting mitogen-activated protein kinase signaling and subsequent clinical trial enrollment, disease progression remained relentless, culminating in the patient's demise within 4 years of diagnosis. This report highlights the exceptional histopathological presentation associated with KIAA1549::BRAF fusion in prostatic adenocarcinoma, emphasizing the need for a deeper comprehension of its implications on disease behavior and therapeutic responsiveness in similar instances.

QOL-11. GBM PATIENTS CONTINUE TO FACE BARRIERS TO PERSONALIZED AND COMPREHENSIVE CARE THAT MAY IMPACT LONGEVITY AND QUALITY-OF-LIFE

Abstract We assessed the quality and accessibility of healthcare services provided to Glioblastoma (GBM) patients across the United States through an online survey distributed among the OurBrainBank community and partners and promoted on social media. A total of 297 patients or caregivers completed the survey (85% caregivers; 15% GBM patients). Median age at diagnosis was 57, lower than the national average of 64. 78% of our respondents described having some college education, and 69% had commercial insurance. Preliminary results revealed that 69% were informed about tumor testing. Among those informed, 64% did not have any molecular testing beyond initial diagnostic tests. For all respondents, 65% were not informed about storing tissue for future testing, and 79% reported not having their tissue stored for possible treatments or clinical trial qualification. Additionally, 52% reported no discussion of seeking a second opinion, and 64% reported not being offered enrollment in any clinical trial. Overall, people living with GBM face significant barriers to achieving comprehensive and personalized care. Lack of tumor testing and storage present missed opportunities for future access to emerging therapies that could improve individual outcomes. Over half of patients had no discussion about seeking a second opinion and were not offered enrollment in clinical trials, highlighting significant barriers to accessing life-extending or quality-of-life (QOL) improving treatments. Compared to the general population affected by GBM, survey respondents represented a disproportionately young and urban population, suggesting potential bias in who is able to respond to the survey. This may underestimate the challenges faced by patients with additional barriers. We are implementing strategies to reach a more geographically diverse participant group, particularly in rural areas. Ongoing targeted recruitment will further elucidate barriers to patient-centered care in GBM, with the potential for significant improvements in survival and QOL.

DISP-01. FACTORS AFFECTING POST-ABLATION OUTCOMES AND TRIAL ENROLLMENT IN DIVERSE PATIENTS UNDERGOING LASER INTERSTITIAL THERMAL THERAPY

Abstract BACKGROUND Laser interstitial thermal therapy (LITT) is a surgical treatment for progressive brain metastases (BM) following stereotactic radiosurgery (SRS). Although LITT has been increasingly accepted, little is known regarding factors influencing presentation and outcomes of patients with diverse backgrounds. METHODS Retrospective review of all patients receiving their index LITT treatment for BM at a single center from 2015 – 2023. Patient demographics and geospatial data was used to compare differences in receipt of LITT by race or ethnicity, survival outcomes, and clinical trial enrollment. RESULTS From 2015-2023, 137 patients presented for LITT to 146 intracranial lesions. One hundred and six patients (77.3%) were non-Hispanic White (NHW), while among Hispanic or non-White (HNW) patients, 25 (18.2%) were Black or African American, 5 (3.6%) were Asian American, and 1 (0.7%) was Hispanic/Latino. Among HNW patients, women more frequently received LITT (p = 0.022), with skin cancers being a more frequent indication among NHW patients (P = 0.019). NHW patients traveled a median of 62.0 (6.2 – 1045.9) miles to receive LITT vs 25.9 (1.31 – 238.3) miles for HNW patients (p = 0.001). Length of stay, discharge location, or readmission rates did not differ between groups. In multivariate analyses, probability of clinical trial enrollment was less among those of female sex (P = 0.049), HNW race/ethnicity (P = 0.041), or external referral status (P = 0.035), while post-LITT overall survival was predicted by pre-LITT KPS (P = 0.0007) and recurrent tumor on biopsy (P = 0.0002). CONCLUSIONS Differences in presentation for LITT by race or ethnicity may be influenced by underlying patient factors, including sex, systemic disease histology, and distance from a treating center, which do not influence post-LITT procedural outcomes or survival. Accordingly, clinical trial enrollment should be prioritized for vulnerable patient populations to realize the full benefits of laser ablation.

DISP-14. THE IMPACT OF DISTANCE IN TREATING GLIOBLASTOMA: A RETROSPECTIVE SINGLE CENTER STUDY

Abstract INTRODUCTION Glioblastoma (GBM) is an aggressive primary brain tumor associated with poor survival. Research shows that patients living in rural areas have poorer outcomes than those in metropolitan areas; however, little is known about how distance between patients and their subspecialist impacts outcomes. We explored the effect of distance from a National Cancer Institute-designated Comprehensive Cancer Center (CCC) has on overall survival (OS) and clinical trial enrollment for patients with GBM. METHODS Retrospective data was collected from patients with newly diagnosed GBM per the World Health Organization 2021 who received treatment at Huntsman Cancer Institute (HCI) from 2018 to 2022. Distance was evaluated as a categorical (near: 0-25 miles, intermediate: 25-40 miles, far: >40 miles) and continuous variable for analyses. In addition to univariable models, multivariable models adjusting for known prognostic factors were also performed. RESULTS A total of 167 patients were identified. Baseline characteristics including age, sex, race, and KPS scores were similar across groups. The univariable COX regression model found no significant difference in OS when comparing the near group to the intermediate (p=0.125) and far (p=0.599). However, the multivariable model found poorer OS in the intermediate group that was statistically significant when compared to the near group (HR (95% CI) = 1.55 (1.01, 2.39), p = 0.047). When comparing distances as a continuous variable, patients in the far group had statistically significant lower clinical trial enrollment rates (near group 43%, intermediate 35%, far 18%; p=0.0001). DISCUSSION Patients with GBM seeing a neuro-oncologist at our CCC did have a difference in OS as well as clinical trial enrollment when considering distance traveled. Clinical trials are standard of care for patients with GBM; our data provides evidence that distance from a CCC significantly affects trial enrollment, underscoring the importance of ensuring availability of resources for clinical trial participants.

DISP-05. SUPERVISED MACHINE LEARNING PREDICTION OF THERAPEUTIC CLINICAL TRIAL ENROLLMENT FOR MINORITY PATIENTS AND WOMEN IN NEURO-ONCOLOGY

Abstract BACKGROUND Despite changes in national policy, women and minority patients remain under-accrued in glioma clinical trials. Supervised machine learning may serve as a novel approach to understanding which variables and their complex interplay most influence enrollment for underrepresented patients. We aimed to design and externally validate machine learning models to predict therapeutic clinical trial enrollment for a) all patients, b) women, and c) NIH-designated minority patients with low- and high-grade glioma. METHODS In a 20-year retrospective cohort of 1042 adult patients with low- and high-grade glioma, boosted neural network (BNN) models for the whole, women, and minority cohorts were used to analyze the combined effects of demographic, socioeconomic, and oncologic variables on clinical trial enrollment. These models were validated using an external cohort (n=103). RESULTS In the development cohort (445 [42.7%] women; 151 [14.5%] minorities; median age, 51.5 [interquartile range: 39.4-62.5] years), 350 patients (33.6%) enrolled in a therapeutic clinical trial. The whole cohort BNN (AUC: 0.827, misclassification rate (MR): 0.175), the women cohort BNN (AUC: 0.895, MR: 0.125), and the minority cohort BNN (AUC: 0.983, MR: 0.053) models accurately predicted trial enrollment on external validation. For the whole cohort and women models, several demographic (race, ethnicity, minority status, sex), socioeconomic (insurance, employment status), and oncologic variables (seizure, chemotherapy, KPS, WHO grade, tumor lobe) were equally influential (Main effect (ME): 1.000, Total effect (TE): 1.000) on enrollment. For the minority model, socioeconomic variables including insurance status (ME: 0.089, TE: 0.417) and occupation classification (ME: 0.087, TE: 0.381) drove enrollment. CONCLUSION We designed and externally validated the first machine learning model for predicting therapeutic clinical trial enrollment for patients with diffuse glioma. Model features driving trial enrollment varied for underrepresented patients. These results may help prioritize personalized patient-specific initiatives to address barriers to equitable patient accrual.

DISP-03. HEALTH DISPARITIES IN NEURO-ONCOLOGY

Abstract Significant disparities in diagnosis, research, and treatment exist in neuro-oncology. Patients who identify as racial or ethnic minorities, sexual or gender minorities, elderly, rural, or have low socioeconomic status are more likely to have adverse outcomes. Using PubMed and expert opinion, we summarized the existing literature on health disparities in neuro-oncology. We categorized disparities based on primary central nervous system tumors (CNS), secondary CNS tumors, pediatric neuro-oncology, clinical trial enrollment and diversity, and financial toxicity. For patients with primary CNS tumors, we found that marginalized groups faced delays in diagnosis, lower rates of resection, and lower rates of postoperative radiation. For secondary CNS tumors, marginalized groups had lower rates of indicated cancer screening, less access to genetic testing, less access to advanced imaging, and lower rates of stereotactic radiosurgery and targeted therapies. Pediatric neuro-oncology patients had differences in morbidity and mortality based on socioeconomic status and race and faced substantial delays in diagnosis. Financial toxicity, the side effects of the high financial cost of a cancer diagnosis, disproportionately affects marginalized populations. This may be further exacerbated by the use of long-term medications such as IDH inhibitors. There is also unequal representation of diverse populations in neuro-oncology clinical trials. Compared to other cancer subspecialties, there is less available data on disparities, however, the types of disparities such as delays in diagnosis and outcomes are similar. Notably, there are not many interventions focused on mitigating these disparities. There needs to be additional research on health disparities in neuro-oncology, including both descriptions of existing disparities, but also on methods and interventions to address these disparities.

Navigating the Path to Inclusion: Understanding Barriers and Facilitators to Clinical Trial Participation Among Chinese Older Adults in the United States with Multimorbidity.

Older adults with multimorbidity are underrepresented in clinical trials, with enrollment of Asians particularly low. Understand perspectives of US Chinese older adults regarding clinical trial participation. Focus group interviews analyzed using thematic analysis. Community/senior centers, academic health systems in Northern and Southern California, and a nationwide registry of Asian Americans/Pacific Islanders. Mandarin- and English-speaking Chinese adults aged ≥ 65years with multimorbidity. Themes related to barriers and facilitators of enrollment in clinical trials of medications. We conducted 12 focus groups: 7 with non-US-born and 5 with US-born Chinese older adults (n = 83 total). Mean age was 74years (SD = 5.9), 43 (51.8%) were female, and 47 (56.6%) Mandarin-speaking. US-born participants had greater educational attainment than non-US-born participants. Participants took a mean of 6.1 prescriptions (SD = 1.5). Barriers to participation in clinical trials of medications included lack of awareness of/exposure for patients and community-based Chinese physicians, preference for natural/traditional medicine, risk aversion and safety concerns, desire for privacy, and inconvenience. Trusted influences included physicians, hospitals/health systems, Asian/Chinese community centers, and family (for non-US-born participants). Suggestions to enhance participation included using language and culturally concordant materials/personnel, educating community-based Chinese physicians about clinical trials, involving patient-trusted physicians in recruitment, promoting trials on conditions common in Chinese people or for an existing condition, and financial incentives. US-born participants expressed greater understanding and willingness to join trials. All groups attributed low clinical trial enrollment to non-US-born Chinese adults. Chinese older adults perceived obstacles to clinical trial participation that could be mitigated by involving trusted physicians in recruitment, using language and culturally concordant materials/staff, and educating patients and community-based physicians. Recognition of differences in attitudes among US- and non-US-born Chinese people may be important to tailoring recruitment strategies.

Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endpoint assessment, suffers from high reader variability. This study represents the most comprehensive multisite analytical and clinical validation of an artificial intelligence (AI)-based pathology system, AI-based measurement of metabolic dysfunction-associated steatohepatitis (AIM-MASH), to assist pathologists in MASH trial histology scoring. AIM-MASH demonstrated high repeatability and reproducibility compared to manual scoring. AIM-MASH-assisted reads by expert MASH pathologists were superior to unassisted reads in accurately assessing inflammation, ballooning, MAS ≥ 4 with ≥1 in each score category and MASH resolution, while maintaining non-inferiority in steatosis and fibrosis assessment. These findings suggest that AIM-MASH could mitigate reader variability, providing a more reliable assessment of therapeutics in MASH clinical trials.

Language as a barrier to clinical trial enrollment: Identifying methods to improve clinical trial enrollment in minority and non-English-speaking patients

Language as a barrier to clinical trial enrollment: Identifying methods to improve clinical trial enrollment in minority and non-English-speaking patients

Impact of geographic region on racial disparities in gynecologic oncology clinical trial enrollment

Impact of geographic region on racial disparities in gynecologic oncology clinical trial enrollment

Evaluation and Use of Natural Language Processing (NLP) Reasoning and Classification Models to Support Clinical Trial Patient Identification and Enrollment in the Community Oncology Setting

Clinical trial research in oncology relies heavily on clinical documentation within the electronic medical record (EMR) to ascertain patient eligibility in clinical trials based on inclusion and exclusion criteria. The structured data elements within the EMR serve as the primary information source for defining patient cohorts, with clinical cancer stage and performance status being two pivotal criteria determining trial eligibility. The challenge arises from the inconsistent availability of clinical stage and performance status data within the structured fields of the EMR despite their consistent presence in clinical notes. Additionally, there is a deficiency of standardization of this data that exists in the unstructured field. Hence, due to lack of structured data and standardization of said data, there are limitations in developing artificial intelligence (AI) models. To increase the comprehensiveness of clinical records, a clinical research team at a community oncology practice was consulted to identify requirements and extract essential clinical features from de-identified data. The methods outlined in this paper focused on eliminating false positives to allow future development of Large Language Models (LLM) using the outputted structured fields which resulted in an increase in patient record completeness with high accuracy. The accuracy ranged from 97.5-97.75% for the models that were developed. Out of the 60,000+ patients, the numerical staging, TNM (tumor, node, metastasis) staging, and Karnofsky performance score models added a structured field for 29.62%, 21.01%, and 40.64% patients respectively. Additionally, a semi-supervised NLP algorithm was applied on the performance status algorithm which achieved a mean absolute error (MAE) of 1.57. This work demonstrates the use case of natural language processing (NLP) in optimizing the clinical research enrollment process by providing an efficient and accurate method to detect key clinical values in unstructured patient data. Similar methodology with more advanced algorithms such as LLM can be employed to detect additional patient elements such as molecular biomarkers, imaging reports, postoperative surgical outcomes (i.e., clear margins etc.) and patient treatment outcomes using the extracted structured fields.

Clinical value of comprehensive genomic profiling on clinical trial enrollment for patients with advanced solid tumors.

The use of biomarker testing to inform treatment decisions has emerged as a standard of care in multiple cancer types. However, the rates of patients with genomic testing results in hand to inform treatment decision-making remain variable. Here, we studied the impact of comprehensive genomic profiling (CGP) on clinical trial enrollment rates in patients with advanced-stage non-small cell lung, colorectal, breast, and prostate cancer using a real-world clinicogenomic database. On average, clinical trial enrollment in the therapy line immediately after CGP report receipt was 5.4%, which represents a 3.0 percentage point increase compared to therapy lines preceding CGP report receipt, supporting a meaningful association between CGP report availability and increased clinical trial enrollment.

Enrichment for clinical trials of early AD: Combining genetic risk factors and plasma p-tau as screening instruments.

Identifying low-cost, minimally-invasive screening instruments for Alzheimer's disease (AD) trial enrichment will improve the efficiency of AD trials. A total of 685 cognitively normal (CN) individuals and individuals with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were grouped according to cutoffs of genetic risk factor (G) polygenic hazard score (PHS) and tau pathology (T) plasma phosphorylated tau-181 (p-tau181) into four groups: G+T+, G-T-, G+T-, and G-T+. We assessed the associations between group level and longitudinal cognitive decline and AD conversion. Power analyses compared the estimated sample size required to detect differences in cognitive decline. The G+T+ group was associated with faster cognitive decline and higher AD risk. Clinical trials enrolling G+T+ participants would benefit from significantly reduced sample sizes compared with similar trials using only single makers as an inclusion criterion. The combination of two low-cost, minimally-invasive measures-genetics and plasma biomarkers-would be a promising screening procedure for clinical trial enrollment. Participants with unimpaired or mildly impaired cognition were grouped based on cutoffs on genetic risk factors (G: polygenic hazardous score [PHS]) and Alzheimer's pathology (T: baseline plasma phosphorylated tau-181 [p-tau181]). Participants with high PHSs and plasma p-tau181 levels (G+T+) were at risk of faster cognitive decline and AD progression. The combination of PHS and plasma p-tau181 could enhance clinical trial enrichment more effectively than using single biomarkers.

Standard HIPAA Authorization Forms Decreased Response Rates for a Multi-site Pragmatic Trial.

The Health Insurance Portability and Accountability Act (HIPAA) aims to safeguard patient information; however, complex legal language may lead to confusion and mistrust, and hinder enrollment in clinical trials. To evaluate the effect of a standard HIPAA authorization included in mailed survey packets on study enrollment for a multi-site pragmatic trial. This study is nested within an advance care planning pragmatic trial at 50 primary care clinics across three University of California (UC) Health Systems. We included English and Spanish-speaking seriously ill patients. One third of eligible patients received and 2/3 did not receive the HIPAA authorization in their enrollment packet. We compared enrollment rates at 3months and assessed the readability, understandability, and actionability of the standard HIPAA form using the Federal Plain Language Guidelines Checklist for Plain Language, the Automatic Readability Checker consensus calculator (grade 8 is the average reading level for US adults), and the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P, 0-100%, 70% considered the minimum). Of 4632 eligible patients (mean age 71, 48% women, 11% Spanish-speaking, 40% racial/ethnic minority); 1543 received a mailed enrollment packet with a HIPAA form and 3089 did not. Patients mailed the HIPAA form were less likely to enroll (10.2% vs. 14.8%, p < 0.001). The standard HIPAA form scored at the 12th grade reading level, had a PEMAT-P Understandability score of 42%, had an Actionability score of 40%, and only met 50% of Federal Plain Language Guideline Checklist items. The inclusion of a standard HIPAA authorization in mailed enrollment packets for a large pragmatic trial led to lower rates of study enrollment. This study informs how HIPAA authorization forms should be redesigned to be more accessible to patients to prevent unnecessary barriers to research enrollment.

Genetic Panel Reveals Coexisting Neuromuscular Disorders in Patients With Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy is a genetically based neuromuscular disorder characterized by progressive physical impairment and cardiomyopathy in children, leading to fatal cardiac or respiratory failure. Duchenne muscular dystrophy shares some overlapping clinical features with other disorders, complicating clinical differentiation. We hypothesized that some Duchenne muscular dystrophy patients may have a secondary neuromuscular disorders that could negatively skew data during pharmaceutical clinical trials and lead to incomplete treatment plans. Consecutive genetic panels on 353 patients were reviewed. Thirty-two (32; 9.1%) patients with Duchenne muscular dystrophy were identified. Three (3; 9.4%) were found to have at least 1 genetically confirmed secondary neuromuscular disorder. Overlooking these coexisting disorders could lead to unexpected treatment failures, potentially affecting medication efficacy in trials or commercial use. Secondary neuromuscular disorders should be considered in Duchenne muscular dystrophy patients before clinical trial enrollment or treatment planning, with expanded genetic testing, such as whole exome sequencing or whole genome sequencing, likely to reveal even more secondary disorders.

The Diagnostic Challenges of Late-onset Neuropsychiatric Symptoms and Early-onset Dementia: A Clinical and Neuropathological Case Study

The emergence of new-onset neuropsychiatric symptoms in middle age presents a diagnostic challenge, particularly when differentiating between a primary psychiatric disorder and an early neurodegenerative disease. The discrepancy between bedside clinical diagnosis and subsequent neuropathological findings in such cases further highlights the difficulty of accurately predicting pathology, especially when there are no evident focal lesions or changes in brain volume. Here we present the case of a 59-year-old woman with inconclusive neuroimaging who exhibited pronounced neuropsychiatric and behavioral symptoms initially suggestive of a mood disorder, then of behavioral variant frontotemporal dementia. However, upon autopsy, we identified coexisting Lewy body disease pathology and tau-related changes, including argyrophilic grain disease and primary age–related tauopathy. This case illustrates the challenges encountered when diagnosing late-onset neuropsychiatric symptoms, emphasizes the link between such symptoms and early-onset dementia and argyrophilic grain disease, and contributes to our understanding of the impact of mixed neuropathology in this population. Accurate diagnosis is essential for the development of molecular-specific therapies and, as well as for accurate prognosis and enrollment in clinical trials.