Prescreening for European Prevention of Alzheimer Dementia (EPAD) trial-ready cohort: impact of AD risk factors and recruitment settings

The usage of population and disease registries as pre-screening tools for clinical trials, a systematic review

OBJECTIVE

This systematic review aims to outline the use of population and disease registries for clinical trial pre-screening.

MATERIALS AND METHODS

The search was conducted in the time period of January 2014 to December 2022 in three databases: MEDLINE, Embase, and Web of Science Core Collection. References were screened using the Rayyan software, firstly based on titles and abstracts only, and secondly through full text review. Quality of the included studies was assessed using the List of Included Studies and quality Assurance in Review tool, enabling inclusion of publications of only moderate to high quality.

RESULTS

The search originally identified 1430 citations, but only 24 studies were included, reporting the use of population and/or disease registries for trial pre-screening. Nine disease domains were represented, with 54% of studies using registries based in the USA, and 62.5% of the studies using national registries. Half of the studies reported usage for drug trials, and over 478,679 patients were identified through registries in this review. Main advantages of the pre-screening methodology were reduced financial burden and time reduction.

DISCUSSION AND CONCLUSION

The use of registries for trial pre-screening increases reproducibility of the pre-screening process across trials and sites, allowing for implementation and improvement of a quality assurance process. Pre-screening strategies seem under-reported, and we encourage more trials to use and describe their pre-screening processes, as there is a need for standardized methodological guidelines.

Sociotechnical feasibility of natural language processing-driven tools in clinical trial eligibility prescreening for Alzheimer's disease and related dementias

BACKGROUND

Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants.

OBJECTIVE

This study investigates the sociotechnical feasibility of NLP-driven tools for ADRD research prescreening and analyzes the tools' cognitive complexity's effect on usability to identify cognitive support strategies.

METHODS

A randomized experiment was conducted with 60 clinical research staff using three prescreening tools (Criteria2Query, Informatics for Integrating Biology and the Bedside [i2b2], and Leaf). Cognitive task analysis was employed to analyze the usability of each tool using the Health Information Technology Usability Evaluation Scale. Data analysis involved calculating descriptive statistics, interrater agreement via intraclass correlation coefficient, cognitive complexity, and Generalized Estimating Equations models.

RESULTS

Leaf scored highest for usability followed by Criteria2Query and i2b2. Cognitive complexity was found to be affected by age, computer literacy, and number of criteria, but was not significantly associated with usability.

DISCUSSION

Adopting NLP for ADRD prescreening demands careful task delegation, comprehensive training, precise translation of eligibility criteria, and increased research accessibility. The study highlights the relevance of these factors in enhancing NLP-driven tools' usability and efficacy in clinical research prescreening.

CONCLUSION

User-modifiable NLP-driven prescreening tools were favorably received, with system type, evaluation sequence, and user's computer literacy influencing usability more than cognitive complexity. The study emphasizes NLP's potential in improving recruitment for clinical trials, endorsing a mixed-methods approach for future system evaluation and enhancements.

Recruitment of pre-dementia participants: main enrollment barriers in a longitudinal amyloid-PET study

BACKGROUND

The mismatch between the limited availability versus the high demand of participants who are in the pre-dementia phase of Alzheimer's disease (AD) is a bottleneck for clinical studies in AD. Nevertheless, potential enrollment barriers in the pre-dementia population are relatively under-reported. In a large European longitudinal biomarker study (the AMYPAD-PNHS), we investigated main enrollment barriers in individuals with no or mild symptoms recruited from research and clinical parent cohorts (PCs) of ongoing observational studies.

METHODS

Logistic regression was used to predict study refusal based on sex, age, education, global cognition (MMSE), family history of dementia, and number of prior study visits. Study refusal rates and categorized enrollment barriers were compared between PCs using chi-squared tests.

RESULTS

535/1856 (28.8%) of the participants recruited from ongoing studies declined participation in the AMYPAD-PNHS. Only for participants recruited from clinical PCs (n = 243), a higher MMSE-score (β =  - 0.22, OR = 0.80, p < .05), more prior study visits (β =  - 0.93, OR = 0.40, p < .001), and positive family history of dementia (β = 2.08, OR = 8.02, p < .01) resulted in lower odds on study refusal. General study burden was the main enrollment barrier (36.1%), followed by amyloid-PET related burden (PCresearch = 27.4%, PCclinical = 9.0%, X2 = 10.56, p = .001), and loss of research interest (PCclinical = 46.3%, PCresearch = 16.5%, X2 = 32.34, p < .001).

CONCLUSIONS

The enrollment rate for the AMYPAD-PNHS was relatively high, suggesting an advantage of recruitment via ongoing studies. In this observational cohort, study burden reduction and tailored strategies may potentially improve participant enrollment into trial readiness cohorts such as for phase-3 early anti-amyloid intervention trials. The AMYPAD-PNHS (EudraCT: 2018-002277-22) was approved by the ethical review board of the VU Medical Center (VUmc) as the Sponsor site and in every affiliated site.

Digital health technologies and Alzheimer's disease clinical trials: might decentralized clinical trials increase participation by people with cognitive impairment?

BACKGROUND

Therapeutic trials in Alzheimer's disease (AD) face many obstacles-particularly with regard to screening and recruitment.

DISCUSSION

Decentralized clinical trials (DCTs) are being developed in other diseases and appear to be of value for overcoming these difficulties. The use of remote visits offers hope of broader recruitment and thus a reduction in inequalities due to age, geography, and ethnicity. Furthermore, it might be easier to involve primary care providers and caregivers in DCTs. However, further studies are needed to determine the feasibility of DCTs in AD. A mixed-model DCT might constitute the first step towards completely remote trials in AD and should be assessed first.

The European Prevention of Alzheimer's Dementia Programme: An Innovative Medicines Initiative-funded partnership to facilitate secondary prevention of Alzheimer's disease dementia

INTRODUCTION

Tens of millions of people worldwide will develop Alzheimer's disease (AD), and only by intervening early in the preclinical disease can we make a fundamental difference to the rates of late-stage disease where clinical symptoms and societal burden manifest. However, collectively utilizing data, samples, and knowledge amassed by large-scale projects such as the Innovative Medicines Initiative (IMI)-funded European Prevention of Alzheimer's Dementia (EPAD) program will enable the research community to learn, adapt, and implement change.

METHOD

In the current article, we define and discuss the substantial assets of the EPAD project for the scientific community, patient population, and industry, describe the EPAD structure with a focus on how the public and private sector interacted and collaborated within the project, reflect how IMI specifically supported the achievements of the above, and conclude with a view for future.

RESULTS

The EPAD project was a €64-million investment to facilitate secondary prevention of AD dementia research. The project recruited over 2,000 research participants into the EPAD longitudinal cohort study (LCS) and included over 400 researchers from 39 partners. The EPAD LCS data and biobank are freely available and easily accessible via the Alzheimer's Disease Data Initiative's (ADDI) AD Workbench platform and the University of Edinburgh's Sample Access Committee. The trial delivery network established within the EPAD program is being incorporated into the truly global offering from the Global Alzheimer's Platform (GAP) for trial delivery, and the almost 100 early-career researchers who were part of the EPAD Academy will take forward their experience and learning from EPAD to the next stage of their careers.

DISCUSSION

Through GAP, IMI-Neuronet, and follow-on funding from the Alzheimer's Association for the data and sample access systems, the EPAD assets will be maintained and, as and when sponsors seek a new platform trial to be established, the learnings from EPAD will ensure that this can be developed to be even more successful than this first pan-European attempt.

Organized Registration for the Assessment of Dementia by the Nationwide General Consortium Toward Effective Treatment (ORANGE) Registry: Current Status and Perspectives of Mild Cognitive Impairment

Background: With increasingly aging societies, a comprehensive strategy for dementia research is important. The Organized Registration for the Assessment of dementia by the Nationwide General consortium toward Effective treatment (ORANGE) Registry is the first longitudinal multicenter prospective trial-ready cohort in Japan. Objective: To establish a large cohort for use in clinical trials and research in Japan. Methods: This registry, based on communities, hospitals, and nursing homes, covers three dementia stages (preclinical, mild cognitive impairment [MCI], and advanced dementia), and includes more than 30 hospitals. We analyzed enrollment and 1-year follow-up data for disease progression. Results: There were 1450 registered patients (649 men, 801 women; mean age, 77.92±6.70 years; mean Mini-Mental State Examination [MMSE] score, 25.19±2.76). The conversion rates from MCI to dementia and MCI to normal were 14.3% and 1.1%, respectively. High Clinical Dementia Rating score (odds ratio [OR] = 11.085, 95% confidence interval [CI]:1.619–75.913, p = 0.014), low MMSE score (OR = 0.835, 95% CI: 0.761–0.917, p <  0.001), high Geriatric Depression Scale score (OR = 1.093, 95% CI: 1.005–1.189, p = 0.038), and low body mass index (OR = 0.895, 95% CI: 0.829–0.967, p = 0.005) at enrollment were significant factors for conversion. Conclusion: The ORANGE MCI Registry is an established registry that facilitates creation of trial-ready cohorts to accelerate promotion of clinical trials with low reversion rates as it originates from a hospital. One-year follow-up analysis suggested assessing various factors for conversion risk. Further analyses will be possible in future with registry expansion. We will continue to refine this registry, including how it can be used more efficiently.

Early-stage Alzheimer disease: getting trial-ready

Slowing the progression of Alzheimer disease (AD) might be the greatest unmet medical need of our time. Although one AD therapeutic has received a controversial accelerated approval from the FDA, more effective and accessible therapies are urgently needed. Consensus is growing that for meaningful disease modification in AD, therapeutic intervention must be initiated at very early (preclinical or prodromal) stages of the disease. Although the methods for such early-stage clinical trials have been developed, identification and recruitment of the required asymptomatic or minimally symptomatic study participants takes many years and requires substantial funds. As an example, in the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease Trial (the first phase III trial to be performed in preclinical AD), 3.5 years and more than 5,900 screens were required to recruit and randomize 1,169 participants. A new clinical trials infrastructure is required to increase the efficiency of recruitment and accelerate therapeutic progress. Collaborations in North America, Europe and Asia are now addressing this need by establishing trial-ready cohorts of individuals with preclinical and prodromal AD. These collaborations are employing innovative methods to engage the target population, assess risk of brain amyloid accumulation, select participants for biomarker studies and determine eligibility for trials. In the future, these programmes could provide effective tools for pursuing the primary prevention of AD. Here, we review the lessons learned from the AD trial-ready cohorts that have been established to date, with the aim of informing ongoing and future efforts towards efficient, cost-effective trial recruitment.

Consensus is growing that intervention in the very early stages of Alzheimer disease is necessary for disease modification. Here, the authors discuss the challenges of recruiting asymptomatic or mildly symptomatic participants for clinical trials, focusing on ‘trial-ready’ cohorts as a potential solution.

Trial-ready cohorts are an effective strategy for the identification of participants eligible for clinical trials in early-stage Alzheimer disease (AD).

Building these cohorts requires considerable planning and technological infrastructure to facilitate recruitment, remote longitudinal assessment, data management and data storage.

Trial-ready cohorts exist for genetically determined populations at risk of AD, such as those with familial AD and Down syndrome; the longitudinal data from these cohorts is improving our understanding of the disease progression in early stages, informing clinical trial design and accelerating recruitment to intervention studies.

So far, the challenges experienced by trial-ready cohorts for early-stage AD have included difficulties recruiting an ethnically and racially representative cohort; and for online cohorts, difficulty retaining participants.

The results of ongoing work will reveal the success of strategies to improve cohort diversity and retention, and the rates of referral to clinical trials.

Application of the ATN classification scheme in a population without dementia: Findings from the EPAD cohort

BACKGROUND

We classified non-demented European Prevention of Alzheimer's Dementia (EPAD) participants through the amyloid/tau/neurodegeneration (ATN) scheme and assessed their neuropsychological and imaging profiles.

MATERIALS AND METHODS

From 1500 EPAD participants, 312 were excluded. Cerebrospinal fluid cut-offs of 1000 pg/mL for amyloid beta (Aß)1-42 and 27 pg/mL for p-tau181 were validated using Gaussian mixture models. Given strong correlation of p-tau and t-tau (R2  = 0.98, P < 0.001), neurodegeneration was defined by age-adjusted hippocampal volume. Multinomial regressions were used to test whether neuropsychological tests and regional brain volumes could distinguish ATN stages.

RESULTS

Age was 65 ± 7 years, with 58% females and 38% apolipoprotein E (APOE) ε4 carriers; 57.1% were A-T-N-, 32.5% were in the Alzheimer's disease (AD) continuum, and 10.4% suspected non-Alzheimer's pathology. Age and cerebrovascular burden progressed with biomarker positivity (P < 0.001). Cognitive dysfunction appeared with T+. Paradoxically higher regional gray matter volumes were observed in A+T-N- compared to A-T-N- (P < 0.001).

DISCUSSION

In non-demented individuals along the AD continuum, p-tau drives cognitive dysfunction. Memory and language domains are affected in the earliest stages.

NIA-AA Alzheimer's Disease Framework: Clinical Characterization of Stages

BACKGROUND

To operationalize the National Institute on Aging - Alzheimer's Association (NIA-AA) Research Framework for Alzheimer's Disease 6-stage continuum of clinical progression for persons with abnormal amyloid.

METHODS

The Mayo Clinic Study of Aging is a population-based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We evaluated persons without dementia having 3 consecutive clinical visits. Measures for cross-sectional categories included objective cognitive impairment (OBJ) and function (FXN). Measures for change included subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (ΔNBS). We calculated frequencies of the stages using different cutoff points and assessed stability of the stages over 15 months.

RESULTS

Among 243 abnormal amyloid participants, the frequencies of the stages varied with age: 66 to 90% were classified as stage 1 at age 50 but at age 80, 24 to 36% were stage 1, 32 to 47% were stage 2, 18 to 27% were stage 3, 1 to 3% were stage 4 to 6, and 3 to 9% were indeterminate. Most stage 2 participants were classified as stage 2 because of abnormal ΔOBJ only (44-59%), whereas 11 to 21% had SCD only, and 9 to 13% had ΔNBS only. Short-term stability varied by stage and OBJ cutoff points but the most notable changes were seen in stage 2 with 38 to 63% remaining stable, 4 to 13% worsening, and 24 to 41% improving (moving to stage 1).

INTERPRETATION

The frequency of the stages varied by age and the precise membership fluctuated by the parameters used to define the stages. The staging framework may require revisions before it can be adopted for clinical trials. ANN NEUROL 2021;89:1145-1156.

Predicting amyloid risk by machine learning algorithms based on the A4 screen data: Application to the Japanese Trial-Ready Cohort study

BACKGROUND

Selecting cognitively normal elderly individuals with higher risk of brain amyloid deposition is critical to the success of prevention trials for Alzheimer's disease (AD).

METHODS

Based on the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease study data, we built machine-learning models and applied them to our ongoing Japanese Trial-Ready Cohort (J-TRC) webstudy participants registered within the first 9 months (n = 3081) of launch to predict standard uptake value ratio (SUVr) of amyloid positron emission tomography.

RESULTS

Age, family history, online Cognitive Function Instrument and CogState scores were important predictors. In a subgroup of J-TRC webstudy participants with known amyloid status (n = 37), the predicted SUVr corresponded well with the self-reported amyloid test results (area under the curve = 0.806 [0.619-0.992]).

DISCUSSION

Our algorithms may be usable for automatic prioritization of candidate participants with higher amyloid risks to be preferentially recruited from the J-TRC webstudy to in-person study, maximizing efficiency for the identification of preclinical AD participants.

Dutch Brain Research Registry for study participant recruitment: Design and first results

INTRODUCTION

The Dutch Brain Research Registry aims to facilitate online recruitment of participants for brain disease studies.

METHODS

Registrants were primarily recruited through an online social media campaign. The registration process included a short questionnaire, which was subsequently used in the prescreening process to match participants to studies.

RESULTS

In the first 18 months, 17,218 registrants signed up (58±11 years old, 78% female). Out of 34,696 study invitations that were sent, 36% were accepted by registrants, of which 50% to 84% were finally enrolled, resulting in 10,661 participants in 28 studies. Compared to non-participants, study participants were more often older, male, more highly educated, retired or unemployed, non-smoking, healthier, and more often had a family member with dementia.

DISCUSSION

The Dutch Brain Research Registry facilitates effective matching of participants to brain disease studies. Participant factors related to study enrollment may reflect facilitators or barriers for participation, which is useful for improving recruitment strategies.

Predicting amyloid status using self-report information from an online research and recruitment registry: The Brain Health Registry

INTRODUCTION

This study aimed to predict brain amyloid beta (Aβ) status in older adults using collected information from an online registry focused on cognitive aging.

METHODS

Aβ positron emission tomography (PET) was obtained from multiple in-clinic studies. Using logistic regression, we predicted Aβ using self-report variables collected in the Brain Health Registry in 634 participants, as well as a subsample (N = 533) identified as either cognitively unimpaired (CU) or mild cognitive impairment (MCI). Cross-validated area under the curve (cAUC) evaluated the predictive performance.

RESULTS

The best prediction model included age, sex, education, subjective memory concern, family history of Alzheimer's disease, Geriatric Depression Scale Short-Form, self-reported Everyday Cognition, and self-reported cognitive impairment. The cross-validated AUCs ranged from 0.62 to 0.66. This online model could help reduce between 15.2% and 23.7% of unnecessary Aβ PET scans in CU and MCI populations.

DISUCSSION

The findings suggest that a novel, online approach could aid in Aβ prediction.