Tackling a Major Deficiency of Diversity in Alzheimer’s Disease Therapeutic Trials: An CTAD Task Force Report

Raman, Rema; Aisen, Paul S.; Carillo, Maria C.; Detke, M; Grill, Joshua D.; Okonkwo, Ozioma C.; Rivera‐Mindt, Monica; Sabbagh, Marwan; Vellas, Bruno; Weiner, Michael W.; Sperling, Reisa A.

doi:10.14283/jpad.2022.50

Cited by 16 publications

(20 citation statements)

References 15 publications

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“…Nearly 25% of older Americans are from a racial or ethnic minority group [ 40 ] and certain groups may be overburdened by AD [ 41 ]. Especially late-stage trials should aim to recruit representative samples of these populations but rarely do [ 42 , 43 ]. In addition to the challenges of recruiting participants from underrepresented groups, participants from these groups may be more likely to be excluded based on eligibility criteria [ 42 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

Recruitment across two decades of NIH-funded Alzheimer’s disease clinical trials

Ritchie

Gillen²,

Grill³

2023

Alz Res Therapy

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Background Timely accrual of a representative sample is a key factor in whether Alzheimer’s disease (AD) clinical trials successfully answer the scientific questions under study. Studies in other fields have observed that, over time, recruitment to trials has become increasingly reliant on larger numbers of sites, with declines in the average per-site recruitment rate. Here, we examined the trends in recruitment over a 20-year period of NIH-funded AD clinical trials conducted by the Alzheimer’s Disease Cooperative Study (ADCS), a temporally consistent network of sites devoted to interventional research. Methods We performed retrospective analyses of eleven ADCS randomized clinical trials. To examine the recruitment planning, we calculated the expected number of participants to be enrolled per site for each trial. To examine the actual trial recruitment rates, we quantified the number of participants enrolled per site per month. Results No effects of time were observed on recruitment planning or overall recruitment rates across trials. No trial achieved an overall recruitment rate greater than one subject per site per month. We observed the fastest recruitment rates in trials with no competition and the slowest in trials that overlapped in time. The highest recruitment rates were consistently seen early within trials and declined over the course of studies. Conclusions Trial recruitment projections should plan for fewer than one participant randomized per site per month and consider the number of other AD trials being conducted concurrently.

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Section: Discussionmentioning

confidence: 99%

Recruitment across two decades of NIH-funded Alzheimer’s disease clinical trials

Ritchie

Gillen²,

Grill³

2023

Alz Res Therapy

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“…This work focussed on developing a pipeline that can solve the systematic reduction in the FOV of T1w MRI scans ("cropping") seen in real-world data from a memory clinic in the UK. This was motivated by allowing such data to be used in quantitative research and development, which is critical for addressing the current lack of ethnic and sociodemographic diversity [32,33] in the large neuroimaging data sets such as ADNI that are currently driving innovations including data-driven disease progression modelling [34,35]. The cropping issue is essentially a missing data problem, and thus the approach implemented was to recover the data in a sensible way.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Crop Filling: a pipeline for repairing memory clinic MRI corrupted by partial brain coverage

Castro-Leal

Whitfield

Praharaju

et al. 2023

Preprint

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Data-driven solutions offer great promise for improving healthcare. However standard clinical neuroimaging data is subject to real-world imaging artefacts that can render the data unusable for computational research. T1 weighted structural MRI is used in research to obtain volumetric measurements from cortical and subcortical brain regions. However, clinical radiologists often prioritise T2 weighted or FLAIR scans for visual assessment. As such, T1 weighted scans are often acquired but may not be a priority. This can result in artefacts such as partial brain coverage being systematically present in memory clinic data. Here we present a neuroimaging pipeline to ameliorate such situations by filling the missing regions with synthetic data. We validate on artificially cropped scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI), showing that our pipeline largely removes the artefact, improving volumetric biomarker accuracy while also retaining statistical differences between diagnostic groups. We demonstrate utility by achieving diagnostic classification performance comparable to uncorrupted data. This is an important contribution towards moving research from the lab into the real world.

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“…As previously mentioned, the next 5-year phase of ADNI4 will recruit more minorities and less-educated individuals to improve the generalizability of future studies (Veitch et al, 2022). Similar efforts to increase diversity are being seen in the UK, Europe, Latin America and in the Asia-Pacific region (Raman et al, 2022). Major advancements are still needed to help combat the Alzheimer's disease crisis predicted in the coming decades.…”

Section: Future Considerationsmentioning

confidence: 97%

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Mirkin

Albensi

2023

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

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Tackling a Major Deficiency of Diversity in Alzheimer’s Disease Therapeutic Trials: An CTAD Task Force Report

Cited by 16 publications

References 15 publications

Recruitment across two decades of NIH-funded Alzheimer’s disease clinical trials

Recruitment across two decades of NIH-funded Alzheimer’s disease clinical trials

Crop Filling: a pipeline for repairing memory clinic MRI corrupted by partial brain coverage

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

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