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RECRUITMENT INTO THE ALZHEIMER PREVENTION TRIALS (APT) WEBSTUDY FOR A TRIAL-READY COHORT FOR PRECLINICAL AND PRODROMAL ALZHEIMER’S DISEASE (TRCPAD)

S. Walter1, T.B. Clanton1, O.G. Langford1, M.S. Rafii1, E.J. Shaffer1, J.D. Grill3, G.A. Jimenez-Maggiora1, R.A. Sperling2, J.L. Cummings4, P.S. Aisen1 and the TRC-PAD Investigators*

1. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 2. Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3. Institute for Memory Impairments and Neurological Disorders, University of California, Irvine;
4. Department of Brain Health, School of Integrated Health Sciences, University of Las Vegas, Nevada; Cleveland Clinic Lou Ruvo Center for Brain Health, USA;* TRC-PAD Investigators are listed at www.trcpad.org

Corresponding Author: S. Walter, Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, waltersa@usc.edu

J Prev Alz Dis 2020;4(7):219-225
Published online August 11, 2020, http://dx.doi.org/10.14283/jpad.2020.46

 


Abstract

BACKGROUND: The Alzheimer Prevention Trials (APT) Webstudy is the first stage in establishing a Trial-ready Cohort for Preclinical and Prodromal Alzheimer’s disease (TRC-PAD). This paper describes recruitment approaches for the APT Webstudy.
Objectives: To remotely enroll a cohort of individuals into a web-based longitudinal observational study. Participants are followed quarterly with brief cognitive and functional assessments, and referred to Sites for in-clinic testing and biomarker confirmation prior to enrolling in the Trial-ready Cohort (TRC).
Design: Participants are referred to the APT Webstudy from existing registries of individuals interested in brain health and Alzheimer’s disease research, as well as through central and site recruitment efforts. The study team utilizes Urchin Tracking Modules (UTM) codes to better understand the impact of electronic recruitment methods.
Setting: A remotely enrolled online study.
Participants: Volunteers who are at least 50 years old and interested in Alzheimer’s research.
Measurements: Demographics and recruitment source of participant where measured by UTM.
Results: 30,650 participants consented to the APT Webstudy as of April 2020, with 69.7% resulting from referrals from online registries. Emails sent by the registry to participants were the most effective means of recruitment. Participants are distributed across the US, and the demographics of the APT Webstudy reflect the referral registries, with 73.1% female, 85.0% highly educated, and 92.5% Caucasian.
Conclusions: We have demonstrated the feasibility of enrolling a remote web-based study utilizing existing registries as a primary referral source. The next priority of the study team is to engage in recruitment initiatives that will improve the diversity of the cohort, towards the goal of clinical trials that better represent the US population.

Key words: Trial-ready cohort, online registry, remote recruitment, web-based, preclinical, Alzheimer’s disease, prevention.


 

Background

Identifying eligible participants for early intervention Alzheimer’s disease (AD) clinical trials continues to be a significant challenge in the field (1, 2). The overarching aim of the Trial-Ready Cohort in Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD) program is to accelerate enrollment for early stage AD clinical trials (3). This will be accomplished by identifying and screening participants to confirm eligibility for these trials, including amyloid biomarker confirmation, and then monitoring and maintaining engagement with these participants through longitudinal visits until an appropriate trial is available. The considerations behind the design of TRC-PAD are described by Aisen et al. (4). The first step in establishing the Trial-ready Cohort (TRC) was to recruit participants into the Alzheimer Prevention Trials (APT) Webstudy, an online assessment tool designed to serve as a feeder to the in-person TRC-PAD cohort. We projected the APT Webstudy would require between 25,000 and 50,000 participants, with at least 20% participants from under-represented communities, in order to identify enough eligible participants for a planned TRC of n=2,000. The APT Webstudy program requires secure and scalable informatics infrastructure (5), as well as an algorithm to identify participants and rank them by risk of brain amyloidosis and development of AD dementia (6). These elements of the program are described in separate papers in this series.
The APT Webstudy was launched as clinical trials have increasingly utilized web-based tools, including registries, to improve efficiency in screening (7-9). Although leveraging registries to recruit for clinical trials is not a new concept, the establishment of online registries has broadened access to participants who are interested and eligible for studies (10-13). Going further than remote recruitment, Orri et al (14) conducted the first entirely web-based clinical trial run under an Investigational New Drug (IND) application. Digital tools allow researchers to optimize the use of mobile technologies in clinical trials, respond to the preferences of participants (15), and measure and fine-tune communication methods (16). To our knowledge, TRC-PAD is the first program inviting participants from various existing registries to a join a longitudinal Webstudy with identification and referral of high-risk individuals to an in-person TRC. In this article, we describe the preliminary experience of efforts to recruit to APT Webstudy, including from national and local registries, as a unifying path to enrollment in TRC-PAD.

 

Methods

APT Webstudy Experience

Participants log in using either their existing social login credentials or by creating an account and providing a username, email address and password. Once logged on, participants are considered ‘registered.’ The Webstudy is designed as a ‘walk through’ experience, with each new section opening after completion of the former. The sections are: Step 1: Personal Profile; Step 2: Consent; Step 3 Lifestyle; Step 4: Remote cognitive and functional assessments; Step 5: Review scores. Sections are described in more detail in a separate paper in this series (17). The questionnaires and assessments were designed to be brief with a target duration of 15 minutes.

Recruitment

APT Webstudy participants are recruited from multiple sources. For the purposes of this paper, the term registry refers to a online registry, study, or service matching individuals interested in participating in studies or clinical trials to prevent or delay AD dementia. Early in its development, the TRC-PAD study team established partnerships with each of the largest “Feeder” registries, and in collaboration with the managing team or investigators, developed a referral strategy based on the registry’s unique population and existing communication pathways. Each strategy began small and was expanded when we were able to ensure the stability of the Webstudy infrastructure, as well as our capacity to provide user support. Outreach took the form of direct email campaigns highlighting the APT Webstudy on the registry website, e-newsletters, and social media posts. In addition to referrals from registries, both central and site-based strategies were employed.

UTM Codes

Urchin Tracking Modules (UTM) were generated to track participants that registered for the APT Webstudy in response to digital outreach, and were embedded in emails, webpages, and social media advertisements. For some registries, although various outreach activities were utilized, all responses linked back through the registry website, requiring use of a single UTM, and limiting our ability to understand the response rates to different digital communications. Recruitment strategies that did not utilize a UTM included printed materials (i.e., brochures, newsletters and magazines) and earned media (i.e., online and print newspaper articles).

The Alzheimer’s Prevention Registry (APR) (www.endalznow.org)

APR was launched in October 2012 by the Banner Alzheimer’s Institute with the aim of providing a shared resource to the AD scientific community to facilitate enrollment in studies to prevent AD. In 2015, APR began offering an optional APOE genotyping program (GeneMatch) to members ages 55-75 to help match individuals to research studies. As of August 2018, APR enrolled a total 320,000 participants with 75,351 agreeing to the GeneMatch program, and approximately 75,000 agreeing to be contacted by researchers (18). APR participants are primarily women (65.6%) and Caucasian (45.5%); 1.8% are Hispanic/Latino and less than 1% are from other underrepresented groups. It should be noted that these percentages are a reflection of only the 60.8% of APR participants who provided their Race or Ethnicity (Table 1) (19). 14% of APR participants are age 50-59, 35% age 60-69, and 23% age 70-79 (Table 1). The APT Webstudy recruitment strategy began with a pilot phase in April 2018, with batches of emails sent from APR to 7,293 individuals (Figure 1). This was followed by an article in the APR quarterly newsletter introducing the APT Webstudy and posts on APR’s social media accounts. In January 2019, emails were sent in batches to 75,000 registrants inviting them to join the APT Webstudy. In March and April 2020, follow up emails were sent to participants who had not opened the email or clicked the link for the APT Webstudy, with additional reminders scheduled for May 2020.

Alzheimer’s Association TrialMatch (trialmatch.alz.org)

Alzheimer’s Association TrialMatch (trialmatch.alz.org) is a free online matching service that utilizes user’s information to generate a custom report of clinical trials for which they may be a good fit. TrialMatch has a large pool of 322,997 users, with 134,148 providing contact and personal information. Individuals enrolled in TrialMatch indicate whether they are a healthy volunteer (52.8%), a caregiver looking for clinical trials for someone else such as a family member with AD (31.7%), or a person living with the disease looking for trials (13.3%). A small percent (2.2%) of users are entered into TrialMatch by a physician or researcher. Individuals under 50 comprise 35% of the Healthy Volunteers and 20% of all TrialMatch participants. 69% of TrialMatch are over the age of 50. Participants are 73.4% Caucasian, 4.5% Hispanic/Latino, and 65% are women. Women comprise 78% of the healthy controls and 54% of caregivers looking for trials for someone else. 22% of TrialMatch users either care for someone with a diagnosis of AD or have a diagnosis of AD. The first APT Webstudy recruitment campaign began in March 2019, with direct emails targeting 48,000 TrialMatch users living within 200 miles of potential TRC-PAD clinical sites. An additional 33,000 users were invited to join APT Webstudy beginning in December 2019. Emails were sent in batches of 5,000 twice a week, and is ongoing at the time of this manuscript.

The Brain Health Registry (BHR) (brainhealthregistry.org)

The Brain Health Registry (BHR) (brainhealthregistry.org) collects longitudinal health, cognitive, and lifestyle data through detailed self-report questionnaires and online cognitive tests (Cogstate, Lumosity, and MemTrax) (16). BHR was launched in 2014 and currently has baseline data on 56,982 participants. BHR participants are 80.9% Caucasian, 5.3% Hispanic/Latino, 73.9% women, with 73% of participants over the age of 50 (20) (Table 1). The BHR team sent emails to 18,240 participants inviting them to register for the APT Webstudy beginning in March 2019 (Figure 1). Emails were sent in batches of 500 every week. If participants do not respond, two follow-up emails are sent, with a second set of reminder emails 231 and 238 days from their initial email contact. The BHR team also featured the APT Webstudy in their e-newsletter.

Table 1. Feeder Registries and APT Demographics

The Cleveland Clinic Healthy Brains Registry (healthybrains.org)

 

The Cleveland Clinic Healthy Brains Registry (healthybrains.org) is a longitudinal, web-based symptomatic and lifestyle assessment (21), with over 13,000 registrants, and over half expressing interest in enrolling into clinical trials. HealthyBrains has registrants and newsletter subscribers from across the nation. The highest number of registrants in the US states of Ohio, Nevada, California and Florida. Registrants were invited to join the APT Webstudy through an article on the HealthyBrains website in May 2018, followed by features in two newsletters, sent by email (Figure 1).

Figure 1. Alzheimer Prevention Trials (APT) Webstudy: Feeder Registry Recruitment Campaign Timeline

 

UCI Consent-to-Contact (C2C) Registry (c2c.uci.edu)

UCI Consent-to-Contact (C2C) Registry (c2c.uci.edu) is a confidential online tool to help match local volunteers in Orange County, CA, with research studies at the University of California, Irvine (22). Registrants enroll by providing an email address or by phoning the research site, remotely completing a series of questions regarding medical history and research interests. Beginning in July 2019, 7,300 C2C participants were invited by email to join the APT Webstudy (Figure 1).

Other sources

Anticipating that the registry-based approach would have limitations, especially in identifying eligible participants from under-represented groups, the APT Webstudy team developed recruitment strategies utilizing the TRC-PAD site network as well as other central activities. Sites participating in the TRC-PAD cohort study were identified early in the development of the program, with some agreeing to work locally to recruit participants to the APT Webstudy. Each of the TRC sites were invited to utilize their own databases of individuals interested in clinical research and email information about the APT Webstudy. The TRC-PAD study team provided flyers, postcards, newsletter and email template language, social media content and leaflets describing the APT Webstudy. Language for these materials was approved by the Institutional Review Board (IRB) and UTM codes were generated where appropriate. Sites also held community outreach events, partnered with other local community organizations to share information about the study, advertised on social media, and posted information about the Webstudy on their own webpages. Central recruitment efforts included generating earned media including newspaper and online and print edition magazine articles, local TV interviews, and posting the study on websites for clinical trials and AD. The earned media stories included an article in the San Diego Union Tribune in January 2018, two letters to the editor in May 2019, in local papers that have circulations of 80,000 (Charleston, SC) and 150,000 (Lexington, KY) respectively. Grand Magazine published an online piece about the APT Webstudy on August 12, 2019, generating 54,000 impressions. The Saturday Evening Post, with a circulation of 302,000 and majority of readers over the age of 45, included APT in its January/February 2020 print edition. So far, the only paid advertising was in the form of Facebook advertisements. Facebook ads ran in eight markets for two weeks in November 2018 for a cost of $12,000, and six markets for 5 weeks in August-September 2019 for a cost of $3,000. The ads were targeted geographically and to the largest minority population in each location, based around the location of TRC sites.

 

Results

APT Webstudy Enrollment: At the time of preparing this mansuscript, there are 30,650 participants consented to the APT Webstudy. Recruitment strategies for the first year were a mix of central and local efforts (Figure 1). The first notable increase was in January 2018 following local newspaper coverage. In March 2018, email referrals were piloted for APR Registry. In April 2018, APR and HealthyBrains introduced the Webstudy in their newsletters. In the first year, 388 participants per month consented to the APT Webstudy on an average. The APR email referrals began in earnest in January 2019, leading to a dramatic increase in consented participants, with 5,196 consenting in January 2019 (Figure 1). This was followed by email referrals from TrialMatch and BHR. In the second year, participants consented to the APT Webstudy on an average of 1,514 per month.

Demographics

Participants in the APT Webstudy have a mean age of 64.56 with a majority of participants ages 50-59 (28.9%) and 60-69 (44.1%) (Table 1). Most participants identify as women (73.0%), white (92.5%) and more than high school level education (85.0%). 2.3% of APT Webstudy participants self describe as Hispanic/Latino. Although most participants are retired or not working (53.2%), a significant percentage are employed either full (30.6%) or part-time (14.7%) (Table 2). A majority of participants have a family history of AD (62.6%) and do not have a personal diagnosis of AD (94.6%). Further details on lifestyle and medical history are provided on Tables 2 and 3.

Table 2. APT Webstudy Health and Lifestyle

Table 3. APT Webstudy Recruitment by Referral Sourc

 

Enrollment by Referral sources

At this point in the recruitment to the APT Webstudy, registries were the primary source of participants, with referrals resulting in 69.69% of consented individuals, according to UTM codes. APR was by far the biggest contributer with 38.98% of all APT Webstudy consented participants, followed by 25.40% referred by TrialMatch. Those referred by APR were also slightly more likely to both register and consent to APT (Table 3). All together 15.9% of the APR participants that were contacted consented to APT, compared to 9.8% or less for other registries. Email (32.92%) and websites (40.78%) were the most common mode of referral, however website visits were largely driven by email campaigns. Central media efforts that could be tracked with UTM resulted in 234 participants. The central Facebook ads accounted for 7,800 and 3,000 clicks which translated to 0.15% of consenting participants.

Geographic Distribution

APT Webstudy participants reside in each of the 50 United States (US), the District of Columbia, and Puerto Rico. States with the highest number of consented participants include California (16.63%), Florida (5.65%), New York (4.67%), Texas (4.66%), and Virginia (4.38%). International location is not currently collected. Participants consented to the APT Webstudy reside in 1931 (or 60%) of US counties. The top ten counties with participants consented to APT are San Diego County, CA (n=1621); Orange County, CA (n=861) Maricopa County, AZ (n=764), Los Angeles County, CA (n=612), Cook County, IL (n=443) Charleston County, SC (n=384), Fayette County, KY (n=279), King County, WA (n=270) Pima County, AZ (n=239) and Middlesex County, MA (n=238) (Figure 2).

Figure 2. APT Webstudy Enrollment: Heatmap of US Counties

 

Discussion

We have demonstrated that online registries are not only feasible but they are an excellent method to identify and recruit participants for a Webstudy. Participants in a registry have already demonstrated an interest in research and willingness to provide information about themselves. In addition, registries have communication infrastructure and digital platforms designed to engage individuals through educational materials, newsletters and other outreach, which may lead to higher rates of referral. UTM codes were shown to be an effective method to track the referral source in this study. The strategy that yielded highest rates of responses was to first feature the APT Webstudy in the registry’s newsletter, followed by direct email communication to registrants. Although not tracked with separate UTM codes, the consistent increase of participants demonstrates that sending second and third emails to non-responders produces additional participants. Although central media efforts and social media advertising were piloted in this first stage of recruitment, this strategy has not been fully explored as a potential source for remotely enrolled participants.
The registries used in this study had a contact-to-consent rate ranging from 1.8%-15.9%, despite having very similar composition of registrants. This brings up several questions as to best practices. Was the higher rate of consent from APR compared to BHR due to the fact that APR directly targets individuals interested in clinical trials? Could the observed rate of consent to contacted participant be influenced by the level of engagement utilized by the respective registries?
It is not surprising that the demographics of participants in the APT Webstudy are similar in demographics to the registries that referred the majority of participants. However, understanding why such a large majority of participants are women is important. Further research may reveal both barriers to in-person research and preferences for online studies. The low rate of Hispanic/Latino involvement in APT Webstudy can likely be attributed to 2 factors, (1) the low rates of Hispanic/Latino participants in the referral registries and (2) the APT Webstudy and recruitment materials had not been translated into Spanish.
We acknowledge that the APT Webstudy has an inherent selection bias, in that participants must have access to the internet in order to participate. This disproportionately excludes many people from under-represented communities, where according to recent Pew reports, only 57% of Hispanic and African American adults own a laptop or a tablet (23), compared to 82% of Caucasians. Although those over 65 years of age are more likely to use a desktop or tablet to access the internet, lower income Americans, those with less than college education, and black and Hispanic populations, are all more likely to use a cell phone to access the internet (24). Although the APT Webstudy is mobile-friendly, the cognitive testing at present requires use of a tablet or computer. The study team is considering changes to cognitive testing that will allow for the use of smart phones and expand accessibility to all communities. Other researchers (25) have demonstrated that text messages can be an effective communication channel with research participants. Would people be more responsive to a text message inviting them to return for a study visit?
The Spanish language version of the APT Webstudy was launched early in 2020, with efforts underway to optimize the cultural sensitivity of the Webstudy and all participant-facing content. A key aim of the study is to engage in recruitment initiatives that will improve the diversity of the cohort, towards the goal of clinical trials that better represent the US population. For the African-American community in particular, recruitment campaigns will highlight disparities in Alzheimer’s disease risk and care, and the role research and clinical trials can play in effecting change.
This study has several limitations. The feeder registries differ in numerous ways, including sample sizes, aims or purpose, geographic distribution, length of time from when participants were first engaged with, and frequency of participant engagement. The current analyses did not account for these differences. Similarly, varying levels of data were available for participants in feeder registries, preventing combination of data streams for more sophisticated analyses of recruitment efficiency. Recruitment from feeder registries was peformed over multiple years, introducing potential confounding by time. Quantification of site level efforts toward recruitment was minimal, limiting our ability to understand the efficacy of site level efforts relative to using central efforts or these feeder registries.
In conclusion, this study demonstrates the feasibility of recruiting from feeder registries into a common platform for identifying potentially eligible participants for a Trial-ready cohort. A robust sample was assembled in a relatively short period of time that is anticipated to play a key role in the national AD clinical trial agenda.

 

Acknowledgements: From the Alzheimer’s Assocation, our thanks to Keith Fargo, Stephen Hall, and Martha Tierney. From APR: Jessica Langbaum, Cassandra Kettenhoven, and Nellie High. From Brain Health Registry: Rachel Nosheny, and Joseph Eichenbaum. From University California Irvine Registry we’d like to thank Meagan Witbracht. Coordinating Center staff providing support to APT Webstudy participants are Godfrey Coker and Rocio Gonzalez-Beristain. The informatics development team is Stefania Burschi, Jia-Shing So, and Marian Wong.

Funding: The study was supported by R01AG053798 from NIA/NIH. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Ethical standard: Institutional Review Boards (IRBs) approved these studies, and all participants gave informed consent before participating.

Conflict of interest: The authors report grants from National Institute on Aging, during the conduct of the study. None of the authors have additional financial interests, relationships or affiliations relevant to the subject of this manuscript.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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THE TRIAL-READY COHORT FOR PRECLINICAL/PRODROMAL ALZHEIMER’S DISEASE (TRC-PAD) PROJECT: AN OVERVIEW

 

P.S. Aisen1, R.A. Sperling2, J. Cummings3, M.C. Donohue3, O. Langford3, G.A. Jimenez-Maggiora3, R.A. Rissman4, M.S. Rafii3, S. Walter3, T. Clanton3, R. Raman3

 

1. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 2. Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3. Department of Brain Health, School of Integrated Health Sciences, University of Las Vegas, Nevada; Cleveland Clinic Lou Ruvo Center for Brain Health, USA; 4. Department of Neurosciences, University of California San Diego, San Diego, CA, USA

Corresponding Author: PS Aisen, Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, paisen@usc.edu

J Prev Alz Dis 2020;4(7):208-212
Published online August 11, 2020, http://dx.doi.org/10.14283/jpad.2020.45

 


Abstract

The Trial-Ready Cohort for Preclinical/prodromal Alzheimer’s Disease (TRC-PAD) project is a collaborative effort to establish an efficient mechanism for recruiting participants into very early stage Alzheimer’s disease trials. Clinically normal and mildly symptomatic individuals are followed longitudinally in a web-based component called the Alzheimer’s Prevention Trial Webstudy (APT Webstudy), with quarterly assessment of cognition and subjective concerns. The Webstudy data is used to predict the likelihood of brain amyloid elevation; individuals at relatively high risk are invited for in-person assessment in the TRC screeing phase, during which a cognitive battery is administered and Apolipoprotein E genotype is obtained followed by reassessment of risk of amyloid elevation. After an initial validation study, plasma amyloid peptide ratios will be included in this risk assessment. Based on this second risk calculation, individuals may have amyloid testing by PET scan or lumbar puncture, with those potentially eligible for trials followed in the TRC, while the rest are invited to remain in the APT Webstudy. To date, over 30,000 individuals have participated in the Webstudy; enrollment in the TRC is in its early stage..

Key words: Trial-Ready Cohort, Alzheimer’s disease.


 

Introduction

The critical need for effective disease-slowing therapy for Alzheimer’s disease (AD) is among the most important health care challenges. Advances in understanding the biology of AD reveal that the disease has a 15-20 year preclinical period during which individuals are cognitively normal but have fibrillar brain amyloid, a prodromal phase during which mild cognitive impairment is present, and a dementia phase with more severe cognitive and functional compromise (1). Disease-modifying therapies may best be evaluated in the early stages of AD when it seems most feasible to preserve cognition and forestall decline. Amyloid changes are the earliest identifiable biological changes of AD, and recent trials of anti-amyloid agents in patients with prodromal AD and very mild AD dementia suggest that anti-amyloid approaches may be viable therapeutics in early stages of the illness. Aducanumab, BAN2401 and gantenerumab have all had outcomes in recent trials suggesting clinical or biological benefit in symptomatic participants [https://investors.biogen.com/news-releases/news-release-details/biogen-plans-regulatory-filing-aducanumab-alzheimers-disease]; suggesting that that earlier treatment, before the extensive accumulation of amyloid plaques and irreversible synaptic damage, may provide clinically meaningful gains. Indeed, it may be the case that all disease-modifying strategies may need to target individuals that are at early, preclinical points on the Alzheimer’s continuum (2).

Very early, large, intervention trials are feasible. The A4 (Anti-Amyloid treatment in Asymptomatic Alzheimer’s) trial, for example, is a multicenter trial in sporadic pre-symptomatic AD that demonstrated that clinically normal individuals 65 years of age and older can be screened for amyloid elevation using positron emission tomography (PET) and enrolled in a long-term, placebo-controlled treatment study (3). A total of 1169 individuals were randomized into A4, though the recruitment process took over three years.
Recruitment challenges are especially severe for trials in preclinical and prodromal AD populations, in which the minimal nature or absence of cognitive symptoms means that individuals do not to seek medical care for memory decline. While AD dementia trials typically recruit from medical practices and clinics specializing in caring for patients with cognitive disorders, the preclinical AD population requires a different approach. Clinically normal A4 participants were identified by screening on the basis of age alone. As expected, about 30% of asymptomatic individuals 65 years or older were amyloid positive by PET.
Therefore a large number of cognitively normal individuals needed to be screened with a lengthy and expensive process (including education, behavioral assessment prior to scanning, then scanning and disclosure) in order to fully enroll this prevention trial. Thus, early stage trials require a method to efficiently connect with individuals who are concerned about their risk for AD and pre-screening to identify those individuals who are at high-risk in order to reduce the high costs and delay associated with a high screen-fail rate (4).
The Trial-Ready Cohort in Preclinical/prodromal Alzheimer’s disease (TRC-PAD) program grew out of a series of meetings of academic and industry investigators, organized by the Global Alzheimer Platform (GAP) to address the challenges of early stage trial recruitment (5). An academic team filed a successful application to the National Institute on Aging, and the program was launched in early 2018. This overview summarizes the considerations behind the design and implementation of TRC-PAD.

 

Figure 1. The TRC-PAD program. APOE – apolipoprotein E genotype; CFI – Cognitive Function Instrument (6, 7); LP – lumbar puncture; Cogstate – Cogstate Brief Battery (8); PACC – Preclinical Alzheimer Cognitive Composite (9)

 

Overall design elements and the APT Webstudy

The TRC-PAD project aims to establish a recruitment infrastructure for early stage AD trials that will shorten the enrollment period from years to months. Participants are drawn from existing registries (“feeders”) plus media and outreach efforts to join the Alzheimer Prevention Trials Webstudy (APT Webstudy). The Webstudy is an online tool designed to collect brief information on demographics, family history, medical history, and subjective cognitive concerns. Unsupervised cognitive assessment collects data on intellectual and memory function relevant to possible early AD. Participants are asked to return to the site quarterly to provide longitudinal cognitive and subjective data. Each participant’s demographic and cognitive data inform his/her individualized risk assessment. The APT Webstudy data is analyzed in an adaptive algorithm using statistical models to determine likelihood of elevation in brain amyloid; initial algorithms are based primarily on analysis of the pre-randomization data from the A4 trial (10). Based on the risk determination, as well as proximity to active TRC-PAD clinical sites and the entry criteria for available trials, individuals may be invited for in-person assessment (including Preclinical Alzheimer Cognitive Composite (PACC) testing and apolipoprotein E (APOE) genotyping) and, based on the updated risk assessment, amyloid testing by amyloid PET or lumbar puncture for measurement of cerebrospinal fluid (CSF) amyloid peptides. Those with amyloid results consistent with AD are invited to be cohort participants, followed in-person longitudinally and ready for enrollment into trials. Those without amyloid abnormalities continue to be followed remotely in the APT Webstudy to continue to provide data for updated risk assessments.
The demographic characteristics of individuals currently enrolled in the APT Webstudy are provided in the companion paper in this issue (11).

 

Building on existing registries

In addition to common strategies such as earned media coverage and social media advertising, we sought to build on prior efforts to connect with the concerned, aging population through registries. Examples of such registries are the Brain Health Registry (BHR) (2), the Alzheimer’s Prevention Registry (APR) (13) and the Alzheimer’s Association TrialMatch program (https://www.alz.org/alzheimers-dementia/research_progress/clinical-trials/about-clinical-trials). We partnered with investigators from these efforts to inform and invite registrants to the APT Webstudy. The APR, with 75,000 registrants agreeing to be contacted by researchers, was particularly successful in generating Webstudy participants.
While we have exceeded our anticipated rate of accrual with 30,000 consented participants to the APT Webstudy, and a rate of 1,000 participants consenting every month in the past year, we have not been successful in attracting an inclusive group of participants representative of the U.S. population (Walter et al, 2020). The priority of this next phase of the program will be to address this deficiency through recruitment in Spanish language, and other community-based approaches.

 

Designing a low-burden, informative longitudinal study to assess risk

A challenge noted by registries in the field is that participant retention can be low. In the APT Webstudy, we can estimate risk of amyloid elevation using cross-sectional data from the pre-randomization phase of A4, but longitudinal change in subjective concerns and cognitive performance are expected to significantly improve accuracy. We have tried to improve retention by minimizing participant burden, keeping follow-up visits to 20 minutes or less, and by optimizing engagement, through sharing of graphical representations of longitudinal performance as well as up-to-date information on available and expected therapeutic trials using the Webstudy itself as well as quarterly newsletters. We provide timely responses, by email or phone, to all queries from participants. These efforts are ongoing; more work toward this goal is required.

 

SRS: a data system to connect high-risk Webstudy participants to TRC sites for in-person testing

Webstudy participants determined to have relatively high risk for amyloid elevation in brain and are located near a TRC-PAD clinical site are invited to have in-person assessments to screen for enrollment into the TRC. In addition to predicted amyloid PET SUVr levels, the selection process considers demographics to achieve diversity, particularly important since Webstudy participants tend to be homogeneous. At this time, the final selections are manually reviewed; after gaining more experience with the system, we will increase automation. Selected participants are presented to site teams through the Site Referral System (SRS) described in a later paper in this series (15). In instances where participants in the APT Webstudy are do not reside close to a TRC-PAD site, they are provided with the opportunity to download and print a report that displays their performance on the various assessments as well as an explanation of the assessments, that they can review with their healthcare provider.
TRC sites are provided a list of potential participants on a monthly basis; the size of the geographic referral area and the number of participants to be referred customized based on site capacity and recruitment needs.

 

Recalculation of risk and assessment of potential trial eligibility to select for amyloid imaging

Amyloid testing, by PET or CSF analysis, is an expensive and somewhat invasive component of the assessment of early stage trial eligibility. TRC-PAD aims to dramatically reduce the number of amyloid tests required to recruit trial participants. The first in-person visit of participants referred via the SRS to TRC sites includes confirmation of demographic information, medical and neurological assessment, cognitive testing with the PACC and APOE genotyping; these data allow a more precise prediction of brain amyloid level. APOE genotype in particular substantially improves prediction of brain amyloid; if APOE genotype is included in the risk assessment, almost all selected would be APOE ε4 carriers. Our target trial sample will have a distribution of APOE genotypes representative of the AD population, meaning 30-40% APOE ε4 non-carriers. We therefore assess risk separately for carriers and non-carriers to allow control over final genetic distribution. Again, the selection process for amyloid testing permits adjustment to support diversity goals.

 

Enrollment in TRC based on SUVr or CSF amyloid peptide ratio

Eligibility criteria for the TRC is based on criteria, including amyloid levels, for preclinical and prodromal clinical trials anticipated to be available at each site. The AHEAD 3-45 platform, a public private partnership collaboration of the NIA Alzheimer’s Clinical Trials Consortium and Eisai Pharmaceuticals including most TRC sites, is currently in its start-up phase; this program will enroll clinically normal individuals with elevated and intermediate levels of amyloid. Current TRC amyloid requirements are based on the this platform. Amyloid-eligible individuals are invited to join the TRC for semiannual in-person reassessment including PACC testing.

 

Connection to early-stage clinical trials

The informatics architecture for TRC-PAD envisions use of longitudinal TRC data as run-in data for therapeutic trials. The system is seamlessly integrated with the Alzheimer’s Treatment Research Institute/Alzheimer’s Clinical Trial Consortium (ATRI/ACTC) Electronic Data System (EDC), is 21 CFR Part 11 compliant, and supports the inclusion of TRC data in trial datsets.
Selection of TRC participants to specific trials available at a site is based on the preferences of participants in discussions with their site investigators. While TRC-PAD procedures are designed with ongoing or coming ATRI/ACTC trials in mind, participants may choose to be screened for any available trials. Additionally, the TRC is designed to allow participants to return after beling either screened or participating in a clinical trial, meeting the important need to the field of retaining and following screen fails.

 

Incorporation of plasma abeta ratios into the TRC-PAD amyloid risk assessment

In a newly funded revision of the TRC-PAD program, we are now in the process of integrating plasma amyloid peptide ratio assays into the final risk assessment in-person screening prior to brain amyloid testing. The promise of plasma amyloid ratio testing has been confirmed by two independent labs using different immunoprecipitation/mass spectrometry approaches (16, 17); each finds a strong association between plasma ratios and brain amyloid load. Encouraging results have also been reported using an automated immunoassay (18). We will assess these methods by obtaining plasma prior to brain amyloid testing for the initial few hundred APT Webstudy participants to undergo brain amyloid PET. The optimal pre-processing approach and assay methodology will then be incorporated into the risk algorithm for the remaining participants. We expect a substantial improvement in accuracy of our algorithm, as well as a significant reduction the number of negative amyloid PET scans and CSF draws, reducing burden to participants and high cost of screening.

 

TRC-PAD and Primary Prevention of AD

Our ultimate goal is the primary prevention of AD. This will require monitoring individuals prior to amyloid elevation in brain to identify characteristics (demographic, genetic, biochemical, clinical) that predict later amyloid elevation, enabling the selection of high-risk people for primary prevention trials involving reducing production or promoting clearance of amyloid peptides. We believe that the APT Webstudy, with the addition of remote acquisition of DNA, and longitudinal collection of blood to assess Aβ42/Aβ40 ratios over time (5), will provide the necessary infrastructure for this effort. Plasma assays of Aβ42/Aβ40 followed longitudinally will be key; encouraging data suggest that plasma amyloid ratios predict later amyloid PET positivity (16).
The TRC-PAD program is a work in progress. While we have passed our initial target of 25,000 registrants in the APT Webstudy, TRC screening and amyloid testing are still in a very early stage, and validation of a plasma abeta ratio assay is still in the future. Many investigators across the U.S. and around the world are contributing to the continued optimization and implementation of TRC-PAD. We hope that this program will accelerate recruitment into early intervention AD trials and facilitate work toward the primary prevention of AD.

 

Acknowledgements: The authors are grateful for the enormous contributions of the entire TRC-PAD team, listed at: https://trcpad.org/wp-content/uploads/2020/06/TRCPAD-study-team-list-journal-v1.0-20200602.pdf.

Funding: The study was supported primarily by R01 AG053798 from NIA/NIH. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Conflicts of interest: This work was supported by grants from National Institute on Aging. None of the authors have additional financial interests, relationships or affiliations relevant to the subject of this manuscript.

Ethical Standards: Institutional Review Boards approved these studies, and all participants gave informed consent before participating.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

References

1. Jack CR, Jr., Albert MS, Knopman DS, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):257-262.
2. Aisen PS, Cummings J, Jack CR, Jr., et al. On the path to 2025: understanding the Alzheimer’s disease continuum. Alzheimers Res Ther. 2017;9(1):60.
3. Sperling RA, Donohue MC, Raman R, et al. Association of Factors With Elevated Amyloid Burden in Clinically Normal Older Individuals. JAMA Neurol. 2020.
4. Rafii MS, Aisen PS. Alzheimer’s Disease Clinical Trials: Moving Toward Successful Prevention. CNS Drugs. 2019;33(2):99-106.
5. Sperling R, Cummings J, Donohue M, Aisen P. Global Alzheimer’s Platform Trial Ready Cohorts for the Prevention of Alzheimer’s Dementia. J Prev Alzheimers Dis. 2016;3(4):185-187.
6. Walsh SP, Raman R, Jones KB, Aisen PS, Alzheimer’s Disease Cooperative Study G. ADCS Prevention Instrument Project: the Mail-In Cognitive Function Screening Instrument (MCFSI). Alzheimer Dis Assoc Disord. 2006;20(4 Suppl 3):S170-178.
7. Amariglio RE, Donohue MC, Marshall GA, et al. Tracking early decline in cognitive function in older individuals at risk for Alzheimer disease dementia: the Alzheimer’s Disease Cooperative Study Cognitive Function Instrument. JAMA Neurol. 2015;72(4):446-454.
8. Maruff P, Thomas E, Cysique L, et al. Validity of the CogState brief battery: relationship to standardized tests and sensitivity to cognitive impairment in mild traumatic brain injury, schizophrenia, and AIDS dementia complex. Arch Clin Neuropsychol. 2009;24(2):165-178.
9. Donohue MC, Sperling RA, Salmon DP, et al. The preclinical Alzheimer cognitive composite: measuring amyloid-related decline. JAMA Neurol. 2014;71(8):961-970.
10. Langford O, Raman R, Sperling RA, et al. Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.44
11. Walter S, Langford OG, Clanton TB, et al. The Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD): Experience from the First 3 Years. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.47
12. Weiner MW, Nosheny R, Camacho M, et al. The Brain Health Registry: An internet-based platform for recruitment, assessment, and longitudinal monitoring of participants for neuroscience studies. Alzheimers Dement. 2018;14(8):1063-1076.
13. Langbaum JB, Karlawish J, Roberts JS, et al. GeneMatch: A novel recruitment registry using at-home APOE genotyping to enhance referrals to Alzheimer’s prevention studies. Alzheimers Dement. 2019;15(4):515-524.
14. Walter S, Clanton TB, Langford OG, Recruitment into the Alzheimer Prevention Trials (APT) Webstudy for a Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD). J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.46
15. Jimenez-Maggiora GA , Bruschi S., Raman R, et al. TRC-PAD: Accelerating Recruitment of AD Clinical Trials through Innovative Information Technology. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.48.
16. Schindler SE, Bollinger JG, Ovod V, et al. High-precision plasma beta-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019;93(17):e1647-e1659.
17. Nakamura A, Kaneko N, Villemagne VL, et al. High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature. 2018;554(7691):249-254.
18. Palmqvist S, Janelidze S, Stomrud E, et al. Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease-Related beta-Amyloid Status. JAMA Neurol. 2019.

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PREDICTING AMYLOID BURDEN TO ACCELERATE RECRUITMENT OF SECONDARY PREVENTION CLINICAL TRIALS

O. Langford1, R. Raman1, R.A. Sperling2, J. Cummings3, C.-K. Sun1, G. Jimenez-Maggiora1, P.S. Aisen1, M.C. Donohue1 and the TRC-PAD Investigators*

1. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 2. Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3. Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada, Las Vegas; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; * TRC-PAD Investigators are listed at www.trcpad.org

Corresponding Author: M.C. Donohue, Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, mdonohue@usc.edu

J Prev Alz Dis 2020;4(7):213-218
Published online August 11, 2020, http://dx.doi.org/10.14283/jpad.2020.44

 


Abstract

BACKGROUND: Screening to identify individuals with elevated brain amyloid (Aβ+) for clinical trials in Preclinical Alzheimer’s Disease (PAD), such as the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s disease (A4) trial, is slow and costly. The Trial-Ready Cohort in Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) aims to accelerate and reduce costs of AD trial recruitment by maintaining a web-based registry of potential trial participants, and using predictive algorithms to assess their likelihood of suitability for PAD trials.
OBJECTIVES: Here we describe how algorithms used to predict amyloid burden within TRC-PAD project were derived using screening data from the A4 trial.
DESIGN: We apply machine learning techniques to predict amyloid positivity. Demographic variables, APOE genotype, and measures of cognition and function are considered as predictors. Model data were derived from the A4 trial.
SETTING: TRC-PAD data are collected from web-based and in-person assessments and are used to predict the risk of elevated amyloid and assess eligibility for AD trials.
PARTICIPANTS: Pre-randomization, cross-sectional data from the ongoing A4 trial are used to develop statistical models.
MEASUREMENTS: Models use a range of cognitive tests and subjective memory assessments, along with demographic variables. Amyloid positivity in A4 was confirmed using positron emission tomography (PET).
RESULTS: The A4 trial screened N=4,486 participants, of which N=1323 (29%) were classified as Aβ+ (SUVR ≥ 1.15). The Area under the Receiver Operating Characteristic curves for these models ranged from 0.60 (95% CI 0.56 to 0.64) for a web-based battery without APOE to 0.74 (95% CI 0.70 to 0.78) for an in-person battery. The number needed to screen to identify an Aβ+ individual is reduced from 3.39 in A4 to 2.62 in the remote setting without APOE, and 1.61 in the remote setting with APOE.
CONCLUSIONS: Predictive algorithms in a web-based registry can improve the efficiency of screening in future secondary prevention trials. APOE status contributes most to predictive accuracy with cross-sectional data. Blood-based assays of amyloid will likely improve the prediction of amyloid PET positivity.

Key words: Trial-ready cohort, Alzheimer’s disease, machine learning.


 

Background

Screening cognitively normal older individuals for the presence of elevated cerebral amyloid-beta protein (“Aβ+”) and inclusion in secondary prevention trials for Alzheimer’s disease (AD) is invasive, expensive and slow. The current gold standards to measure Amyloid-β in the brain require either positron emission tomography (PET) or cerebrospinal fluid (CSF) assay. For example, the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s disease (A4) trial conducted amyloid PET on 4,486 individuals in order to identify 1,323 Aβ+ individuals for an amyloid PET screen fail rate of 71% (1). The Number Needed to Screen (NNS) to identify each Aβ+ individual was 3.39 individuals.
Trial-Ready Cohort in Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) is a research program that was initiated to find solutions to these challenges in trial recruitment and site management, as described in Aisen, et al. Submitted (2). There are three elements that make up the TRC-PAD platform; Alzheimer’s Prevention Trial (APT) webstudy (aptwebstudy.org), Site Referral System (SRS) and the Trial Ready Cohort (TRC). The APT webstudy invites participants to enroll into the study. At the time of enrollment, participants are asked for demographic, medical and lifestyle information. They are asked to complete longitudinal web-based cognitive testing and symptom questionnaires. With these data, we aim to estimate the likelihood that an individual is Aβ+ before they are invited to participate in a secondary prevention trial. The SRS helps facilitate the participants deemed to be most likely Aβ+ from APT to go for in-clinic assessments where they proceed with the TRC screening. During the TRC screening phase participants are administered additional testing, including Preclinical Alzheimer’s Cognitive Composite (PACC) (3) and genotyping, before assessing their eligibility for an amyloid test.
In this paper, we describe how the prediction models and algorithms used in TRC-PAD were derived from A4 screening data. We anticipate blood-based biomarkers will greatly improve predictions of amyloid positivity, and this is a focus of future work and an aim of TRC-PAD. Predictors in the current analysis are limited to demographics, cognitive and functional assessments, and APOE genotype.

 

Methods

Population and Study Design

The study design and screening data for A4 have been previously described (7, 8) and Institutional Review Boards have approved both A4 and TRC-PAD studies. The A4 screening dataset contains N=4,486 participants, of which 1323 (29%) were classified as Aβ+. Amyloid PET imaging was conducted with florbetapir F18 and summarized by mean cortical standardized uptake value ratio (SUVR) relative to the whole cerebellum. Participants were considered eligible to continue screening for A4 based on an algorithm combining both quantitative SUVR (≥1.15) and qualitative visual read performed at a central laboratory. A SUVR between 1.10 and 1.15 was considered to be elevated amyloid only if the visual read was considered positive by a two-reader consensus determination. Participants who were considered Aβ+ were slightly older; with mean/standard deviation (SD) age of 72.10/4.89 in the Aβ+ group and 70.95/4.53 in the Aβ- group. However, there were no observed differences in sex and education. Aβ+ participants were more likely to have a family history of dementia and at least one APOEε4 allele. In addition, Aβ+ participants performed worse on the screening Preclinical Alzheimer Cognitive Composite (PACC) results and had higher scores on the Cognitive Function Index.

Variables

Table 1 describes the collections of predictors that we considered to train different predictive algorithms. All screening data for the A4 Study were collected during supervised clinic visits. However some components of the A4 screening battery are being captured remotely in the APT webstudy, including demographic, Cogstate brief battery (9), family history (sibling or parent with Alzheimer’s), and Cognitive Function Instrument (10) (CFI) variables indicated in Table 1. We consider predictive algorithms using these “remote” variables only, as well as a more thorough battery that would require a supervised clinic visit with an administration of the PACC3. In all, we considered 6 models: (1) remote battery without APOE, (2) remote battery with APOE, (3) in clinic battery without APOE, (4) in clinic battery with APOE, (5) in clinic battery with individual PACC component scores without APOE, and (6) in clinic battery with individual PACC component scores with APOE. The PACC component scores include the Mini-Mental State Exam (MMSE) (11), Wechsler Memory Scale-Revised Logical Memory, Digit Symbol Substitution (DSST), and Free and Cued Selective Reminding Test (FCSRT) (12).

Table 1. Predictors Considered

We considered predictive algorithms which could be applied to data captured either remotely via a web-based registry, or in the clinic (though all data in A4 was collected in clinic), as indicated in the table. In all we considered 6 models: (1) remote battery with APOE, (2) remote battery without APOE, (3) in clinic battery with APOE, (4) in clinic battery without APOE, (5) in clinic battery with individual PACC component scores and APOE, and (6) in clinic battery with individual PACC component scores without APOE.

 

Statistical Analysis

Extreme Gradient Boosting (XGBoost) (4) is a decision tree-based machine learning technique (6). A single decisions tree, or regression tree, is easy to interpret but provides relatively poor prediction. Aggregating a large number of trees can improve prediction accuracy. Boosting is a technique in which models are trained in sequence, with each new model making cumulative improvements. At each iteration the data are re-weighted such that misclassified data points receive larger weights. XGBoost is a scalable tree boosting algorithm, that is optimized and designed to be highly efficient, flexible, and portable.
XGBoost supports monotone constraints and customized objective functions. We applied monotone constraints to predictors such as age, number of APOEε4 alleles (0, 1 or 2), and assessment scores that we expect to have a generally monotonic relationship with amyloid PET SUVR (Supplemental Figure 1). The default XGBoost objective function is mean squared loss, meaning decision trees are selected to minimize the residual sum of squares. Because XGBoost does not provide confidence intervals with mean squared loss, we applied the Quantile Regression loss function to estimate the 50%, 2.5%, and 97.5% quantiles of the predictions. XGBoost model has a number of hyper-parameters that are used to assist in the issue known as the bias-variance trade-off (13). Hyper-parameters are fixed before the model is fitted and are not learned from data. We used 10-fold Cross-Validation (CV) to assess the out-of-sample bias and variance for given hyper-parameter values, and Bayesian Optimization (14) to optimize the hyper-parameter selection. We use SHapley Additive exPlanation (SHAP) (15) values to summarize the importance of each predictor to the overall predictive accuracy of each model. More details about the model fitting procedures are provided in the supplemental material (Supplemental Table 1). Our main interest lies in the predictive accuracy of the models. In order to assess this, we split the data randomly into 80% training and 20% test. After fitting the models with the training data, we assess their predictive accuracy with the independent test data. Analyses were conducted with R version 3.6.2 (r-project.org) with packages xgboost (4) version 0.90.0.2 and mlrMBO (16) version 1.1.2.

Figure 1. Contribution of 5 best predictors in each model

Using the model training data we see the contribution to prediction accuracy expressed in terms of the mean absolute SHAP value (mean|SHAP|). Abbreviations: SHAP, SHapley Additive explanation; OCL, One Card Learning; OBR, One Back Reaction; DER, Detection Reaction; IDR, Identification Reaction; FH, Family History; FH P, FH Parent; FH S, FH Sibling; CFI, Cognitive Function Index; CFI Pt, CFI Participant; CFI SP, CFI Study Partner; ADL, Activities of Daily Living; ADL Pt, ADL Participant; ADL SP, ADL Study Partner; PACC, Preclinical Alzheimer Cognitive Composite

 

Results

Figure 1 shows the relative contributions, in terms of SHAP values, for each predictor to the predictive accuracy of each model. As expected, when available, APOE genotype is the most important predictor for these cross-sectional models. We see that age, CFI, education, and family history also enter the top 5 most valuable predictors in some models. Figure 2, the Receiver Operating Characteristic (ROC) curves and Area under the Curve (AUC) for the 6 models, also demonstrates the relative value of APOE. The dashed lines are models fitted without the APOEε4 variable and the solid lines are for models that include APOEε4. The ROC curves were generated using a cut point SUVR value of 1.15 for a binary separation between amyloid positive and negative. In general, we see AUCs in the range 0.60 (without APOE) to 0.73 (with APOE).
Figure 3 expresses prediction accuracy in terms of screening for a clinical trial. The top panel shows 1/Positive Predictive Value (PPV), which is equivalent to the number needed to screen (with amyloid PET) to identify one eligible participant. In this figure, movement along the horizontal axis represents varying the threshold applied to SUVRs predicted from each model. The bottom panel provides the required number of potential participants (e.g. webstudy participants) in order to identify 1,000 Aβ+ participants.
Table 2 reports operating characteristics from several screening algorithm scenarios. The top half provides operating characteristics when a threshold is selected to provide 50% prediction prevalence (i.e. select half the participant pool to receive amyloid PET scans). With 50% prediction prevalence, the NNS is about 2.5 participants with APOE and 3.0 participants without APOE. When the threshold for predicted amyloid PET is increased to 1.15, the NNS is reduced to about 1.7 participants with APOE and 2.5 participants without APOE. However, this results in much lower sensitivity, and as we can see from Figure 3, a threshold of 1.15 would be practical only with participant registries of 10,000-13,000 to identify 1,000 Aβ+ participants.

Table 2. Operating characteristics of screening algorithms using the test data with Aβ+ set to SUVR ≥ 1.15

The top half of the table provides operating characteristics when a threshold is applied to predicted amyloid PET SUVR that results in a 50% prediction prevalence (half of the screening pool is predicted positive and tested with a PET scan). The first column indicates the threshold required to attain 50% prediction prevalence. The bottom half of the table applies a threshold of 1.15, which reduces Number Need to Screen (NNS), but also greatly reduces sensitivity. The NNS is the inverse of the Positive Predictive Value (PPV). The PPV indicates the percentage of participants that are truly positive when the model indicates them as positive. Likewise, the Negative Predictive Value (NPV), this gives the probability that a participant is truly amyloid negative when the model indicates them as negative.

Table 3. Demographic characteristics of amyloid positive selections from the test data with Aβ+ set to SUVR ≥ 1.15

The top half of the table provides demographic characteristics when a threshold is applied to predicted amyloid PET SUVR that results in a 50% prediction prevalence (half of the screening pool is predicted positive and tested with a PET scan). The first column indicates the threshold required to attain 50% prediction prevalence. The bottom half of the table applies a threshold of 1.15. We can see in all the scenarios where APOE is included in the model, at least 29 of the 30 participants with APOE4 2 allele (in the test data) have been selected.

Figure 2. ROC curves and AUCs

ROCs and AUCs for each model are determined using the independent test set and Aβ+ set to SUVR ≥ 1.15. The colors represent the setting type; Remote (red), In-Clinic (blue) and PACC components (blue). Abbreviations: ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; PACC, Preclinical Alzheimer Cognitive Composite

Figure 3. Number needed to screen and required registry size

The top panel shows the number needed to screen (which is equivalent to 1/PPV) with amyloid PET to identify one Aβ+ individual by applying the given SUVR threshold to the values predicted from each model. The middle panel shows sensitivity. The models not containing APOEε4 all have lower sensitivity. The bottom panel shows the size of the screening pool (e.g. web-based registry) that would be required to recruit 1,000 Aβ+ individuals by applying the given SUVR threshold to values predicted from each model.
Abbreviations: PPV, Positive Predictive Value; SUVR, Standardized Uptake Value Ratio; PACC, Preclinical Alzheimer Cognitive Composite; PET, positron emission tomography

 

Discussion

This work, in the context of the TRC-PAD platform, can facilitate the development of participant selection algorithms. TRC-PAD has two main selection points; the first is from the APT webstudy to in-clinic assessment (stage 1) and the second is from in-clinic to amyloid testing (stage 2). In stage 1, consented webstudy participants are referred to their nearest TRC-PAD site, identified via the use of self-reported zip codes. They are then ranked based on their SUVR prediction. In addition to this predicted SUVR, the selection process considers demographics to achieve diversity and if the participant has known prior amyloid testing and results. During the first in-clinic visit of the referred participants in stage 1, additional cognitive testing, in the form of the PACC, and APOE genotyping is performed. With this additional information, the SUVR predictions are updated and presented for central authorization of amyloid testing.
This work has shown that by collecting relatively simple demographics, cognitive and functional assessments remotely, via the webstudy, we will be able to reduce screen fail rates and improve enrollment. Even small improvements in NNS can have a large impact on the expense of screening for Preclinical AD clinical trials. For example, assuming a conservative estimate of 3,500 US Dollars (USD) per scan, the A4 study spent a total of about 4,486×3,500(USD) = 15,701,000(USD) for screening amyloid PET scans alone to identify 1,323 Aβ+ individuals (NNS=3.39). Reducing the NNS from 3.39 to 2.62, which seems plausible with the simplest remote battery, would have reduced this cost by 3,569,090(USD) to 1,323×2.62×3,500(USD) = 12,131,910 (USD). In addition to the remote data setting, this work included the value of APOE genotyping and collection of PACC during an in-clinic screening. Adding APOE genotype might reduce NNS to below 2.00, for a total PET screening cost of 1,323×2.00×3,500(USD) = 9,261,000(USD). The financial impact would be less with a cerebrospinal fluid (CSF)-based, or blood-based, amyloid screen, but the impact on subject and site burden would remain significant. From a statistical aspect, we have demonstrated the use of Machine Learning Techniques to both optimize, via Bayesian Optimization, and produce predictive models using XGBoost. We have illustrated how to make inferences from a modelling approach that is primarily used for prediction via the SHAP metric.
One limitation of these pre-screening algorithms is that the cohort characteristics will be impacted. For example, we would expect the algorithms to produce an older cohort with an even greater proportion of APOEε4 carriers than a cohort selected without a pre-screen. This could be mitigated by stratifying the screening process to ensure an adequate sample of younger, APOEε4 non-carriers; but with adverse effects on the NNS. Another consideration is the inability for these models to extrapolate beyond the data in the continuous variables such as age. A second potential limitation is in the bias of the training data. As we start using these models in TRC-PAD and collect additional data, we will assess whether the models are biased against any additional covariates collected.
Future work will focus on utilizing longitudinal cognitive and functional change and/or the use of blood-based biomarkers to improve the performance of these predictive models and algorithms. We anticipate, based on analyses of the Alzheimer Disease Neuroimaging Initiative (ADNI) (5), that longitudinal change may be a valuable predictor of amyloid status. In addition, we will incorporate plasma amyloid peptide ratios (currently in validation testing) into the final stage of prediction and expect a large improvement in prediction.

 

Acknowledgements: We thank the A4 Study Team for their data sharing policy (NIA grants U19AG010483 and R01AG063689). Without the use of such a rich dataset we would not be able to conduct this research. We would also like to thank the Alzheimer’s Therapeutic Research Institute (ATRI) and the members that make up the TRC-PAD project. Dr Cummings is supported by Keep Memory Alive (KMA); NIGMS grant P20GM109025; NINDS grant U01NS093334; and NIA grant R01AG053798.

Ethical standard: Institutional Review Boards (IRBs) approved these studies, and all participants gave informed consent before participating.

Conflict of interest: Dr. Raman reports grants from National Institute on Aging, grants from Eli Lilly, during the conduct of the study. Dr. Sperling reports personal fees from AC Immune , personal fees from Biogen, personal fees from Janssen, personal fees from Neurocentria, personal fees from Eisai, personal fees from GE Healthcare, personal fees from Roche, personal fees from InSightec, personal fees from Cytox, personal fees from Prothena, personal fees from Acumen, personal fees from JOMDD, personal fees from Renew, personal fees from Takeda Pharmaceuticals, personal fees from Alnylam Pharmaceuticals, personal fees from Neuraly, grants from Eli Lilly, grants from Janssen, grants from Digital Cognition Technologies, grants from Eisai, grants from NIA, grants from Alzheimer’s Association, personal fees and other from Novartis, personal fees and other from AC Immune, personal fees and other from Janssen, outside the submitted work. Dr. Cummings has provided consultation to Acadia, Actinogen, AgeneBio, Alkahest, Alzheon, Annovis, Avanir, Axsome, Biogen, BioXcel, Cassava, Cerecin, Cerevel, Cortexyme, Cytox, EIP Pharma, Eisai, Foresight, GemVax, Genentech, Green Valley, Grifols, Karuna, Merck, Novo Nordisk, Otsuka, Resverlogix, Roche, Samumed, Samus, Signant Health, Suven, Third Rock, and United Neuroscience pharmaceutical and assessment companies.Dr. Cummings has stock options in ADAMAS, AnnovisBio, MedAvante, BiOasis. Dr. Cummings owns the copyright of the Neuropsychiatric Inventory. Dr Cummings is supported by Keep Memory Alive (KMA); NIGMS grant P20GM109025; NINDS grant U01NS093334; and NIA grant R01AG053798. Mrs. Jimenez-Maggiora, Langford, and Sun report grants from National Institutes of Health (NIH) National Institute on Aging Grant number: R01AG053798, during the conduct of the study. Dr. Aisen reports grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, grants from Eisai, personal fees from Merck, personal fees from Biogen, personal fees from Roche, personal fees from Lundbeck, personal fees from Proclara, personal fees from Immunobrain Checkpoint, outside the submitted work. Dr. Donohue reports grants from National Institutes of Health (NIH) National Institute on Aging Grant number: R01AG053798, during the conduct of the study; personal fees from Biogen, personal fees from Roche, personal fees from Neurotrack, personal fees from Eli Lilly, other from Janssen, outside the submitted work.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

SUPPLEMENTARY MATERIAL

References

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GLOBAL ALZHEIMER’S PLATFORM TRIAL READY COHORTS FOR THE PREVENTION OF ALZHEIMER’S DEMENTIA

 

R. Sperling1, J. Cummings2, M. Donohue3, P. Aisen3

 

1. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 2. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 3. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA

Corresponding Author: Reisa Sperling, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, reisa@rics.bwh.harvard.edu
 
J Prev Alz Dis 2016;3(4):185-187
Published online August 16, 2016, http://dx.doi.org/10.14283/jpad.2016.108

 


 

Introduction

The recent launch of several Alzheimer’s disease (AD) clinical trials targeting the preclinical stage of the disease has highlighted the need for a paradigm shift in prevention trial recruitment. While there are multiple promising mechanisms to test, each new clinical trial can take up to 1-2 years to set up and likely 2-3 years to complete enrollment. Consequently, we are running out of time to outpace the public health epidemic precipitated by the aging of the world’s population. .
The concept of preclinical AD is based on the now widely-accepted observation that amyloid accumulates in the brain for many years prior to the development of symptoms (1, 2).  In 2011, an international working group convened by the National Institute on Aging and the Alzheimer’s Association proposed a conceptual framework and operational research criteria for defining preclinical AD based on presence of amyloidosis, with or without neurodegeneration and subtle cognitive decline (3). In this framework, amyloidosis could be assessed using positron emission tomography (PET) imaging or cerebrospinal fluid (CSF) analysis (low CSF Aβ1-42).  A similar framework, but with a somewhat different lexicon was proposed by the International Working Group for New Research Criteria for the Diagnosis of AD, which has a similar concept of “preclinical AD,” but defines presymptomatic as individuals with autosomal dominant genetic risk, and refers to biomarker-positive individuals as “at-risk” (4).  
Since these criteria were first introduced, the concept of preclinical AD has evolved as increasing evidence has supported the hypothetical temporal evolution of AD biomarkers and clinical symptoms (5). Studies in populations with autosomal dominant forms of AD (ADAD), in particular, have suggested that disease markers can be detected in a predictable order prior to the expected onset of symptoms: changes in the CSF levels of amyloid 25 years before expected onset; amyloid deposition assessed using PET imaging 15 years before expected onset, and impaired episodic memory 10 years before expected onset (6).

 

The challenge of identifying and recruiting participants for secondary prevention trials

This evolving understanding of the earliest stages in the AD continuum have spawned secondary prevention trials in both genetic-at-risk and amyloid-at-risk cohorts, defined by an absence of clinically detectable impairment but the presence of either 1) a deterministic genetic mutation that confers near certainty of developing AD, or 2) biomarker evidence that amyloid has begun to accumulate in the brain. Identifying individuals who fit into these two categories has thus become a major challenge for those planning such trials.    
The Global Alzheimer’s Platform (GAP) was established in 2013 as a collaboration of the Global CEO initiative on Alzheimer’s disease (CEOi) and the New York Academy of Sciences (NYAS). In parallel with initiatives in Europe, Canada, and Japan, GAP aims to coalesce the special expertise and infrastructure needed to accelerate clinical trials across all stages of AD, including preclinical stages. GAP comprises several components, including GAP-NET to support site infrastructure with pre-certifications, master contracts, and a centralized IRB (7); and GAP Trial Ready Cohorts for Preclinical and Prodromal Alzheimer’s Dementia (GAP TRC-PAD).
The goal of GAP TRC-PAD is to build an efficient and sustainable recruitment system for upcoming secondary prevention trials (Figure 1). Drawing from existing registries and studies, including the Brain Health Registry (BHR), Alzheimer’s Prevention Registry (APR), the Cleveland Clinic’s Healthybrains.org (8), and the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) study, non-demented individuals over the age of 60 who are interested in participating in clinical trials will be invited to join the GAP Registry. Those who sign an electronic informed consent, will be asked to submit data on demographics, family, medical, and lifestyle history, and cognitive function.   

 

Figure 1. Structure of GAP TRC-PAD

 

GAP TRC-PAD set as its initial goal to identify a large number of potential participants, rapidly screen these individuals using an adaptive risk algorithm, and ultimately identify 1000 preclinical and 1000 prodromal participants as a “Trial Ready Cohort” for the first GAP clinical trials.  In addition, GAP seeks to develop and validate web-based cognitive and functional assessments for use in future trials.

 

Predicting amyloid status

Amyloid status can be determined by CSF studies or PET imaging, and in cognitively healthy controls, low baseline CSF Aβ1-42 was shown to be associated with future Aβ positivity (9). However, screening large numbers of people with these tests would be prohibitively expensive and not feasible from a pragmatic point of view. Thus, investigators have identified other inexpensive and non-invasive measures that are predictive of amyloid status. For example, in a population-based study of cognitive normal elderly, age and APOE genotype were shown to be predictive of amyloid accumulation (10); and in a study of clinically normal older individuals, subjective cognitive concerns (SCC) were shown to be predictive of Aβ positivity (11). APOEε4 genotype has also been linked to high amyloid burden in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Among cognitively healthy individuals, APOEε4 carriers were more than twice as likely to have positive amyloid PET scans compared to non-carriers (12). More recently, results from the AIBL study showed that high amyloid burden was associated with older age, subjective memory complaints, and APOEε4 genotype (13).  Another novel “measure” that may be predictive of amyloid positivity is lack of practice effects on cognitive testing. In a preliminary study, higher uptake of 18F-flutemetamol on PET imaging was five times higher in individuals with low practice effects on a delayed recall memory task compared to those with high practice effects (14).   
Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) also showed that among cognitively normal older adults with significant memory concern, elevated amyloid deposition and abnormal CSF biomarker were strongly associated with APOEε4 carrier status (15). Aβ-positive participants in ADNI and AIBL also show more decline in cognition compared to Aβ-negative participants as measured by the ADCS Preclinical Alzheimer’s Cognitive Composite (ADCS-PACC), especially among APOEε4 carriers (16).  
Working with ADNI data, investigators at the University of Southern California’s Alzheimer’s Therapeutic Research Institute (ATRI) developed an adaptive algorithm to predict amyloid status based on APOE carriage status, baseline scores on the ADCS-PACC, age, and family history.  Preliminary results suggest that this algorithm, applied to populations of cognitively normal (pre-clinical) and non-demented but cognitively impaired (prodromal) potential clinical trial participants, will enable identification of those likely to be amyloid positive. Amyloid testing on only these pre-selected groups should then reduce the number of screen fails, expedite the enrollment process, and reduce the overall costs of a study. A larger study is planned in 2016 to confirm these findings.   
As more is learned about algorithmic function, it will be possible to adjust algorithms to yield specific populations of interest.  For example, APOE 4 carriage may be highly influential in current amyloid lowering strategies but some drugs may have genotype-specific effects or side effects and recruitment of both e4 carriers and non-carriers may be important.  Algorithm adjustment will be necessary to achieve this.  Other biomarkers might also play a greater role in algorithms that currently conceptualized, including Tau PET imaging which may be more useful in staging individuals along the preclinical/prodromal progression.  
Some interventions might be most effective prior to substantial tangle accumulation or cerebral atrophy, suggestive of neuronal loss, has begun whereas other interventions might have a greater effect after the onset of atrophy when inflammation and tau-related cell death may have a greater role.  Integrating Tau PET and magnetic resonance imaging (MRI) into the algorithm might assist in identifying varying pathologies in early populations that can be paired with different mechanisms of action of test therapies.
 A greater range of data — from sleep measured to “low friction” assessments such as amount of cell phone use, to higher friction measures such as success in on-line games — might be integrated into future algorithms to identify patients in early phases of disease or to more fully characterize the range of abnormalities exhibited by minimally cognitively affected individuals.  

 

Conclusions

Registries that capture large numbers of cognitively normal potential clinical trial participants will be essential to enable testing of interventions designed for secondary prevention. Equally important will be a means for quickly and accurately identifying individuals within the registry who meet the requirements of a particular study. The GAP Registry and GAP TRC-PAD are designed specifically to meet these needs, and in combination with the infrastructure developed by GAP-NET, should provide the integrated platform necessary for efficient clinical trials not only in the pre-dementia space, but across all disease stages.
The Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) Study is the first prevention trial targeting individuals at risk for AD based on evidence of brain amyloid accumulation (17). Using the adaptive algorithm developed as described in this paper, we anticipate being able to select from an initial registry of approximately 50,000 individuals, bringing in a subset for further screening with PET or other measures, ultimately yielding a trial ready cohort of 1000 preclinical and 1000 prodromal patients, thus reducing the time required to fully enroll participants for the first GAP trials. While the A4 Study is designed with the goal of showing efficacy of an anti-amyloid agent to prevent the cognitive decline due to AD, equally important will be the additional information we will acquire to inform future prevention trials regarding expediting enrollment and developing more sensitive endpoints, creating population-appropriate outcome measures, and novel biomarkers, including theragnostic markers to track therapeutic responses.  

 

Disclosures: Reisa Sperling has served as a consultant for Abbvie, Biogen, Bracket, Genentech, Lundbeck, Roche, and Sanofi. She has served as a co-investigator for Avid, Eli Lilly, and Janssen Alzheimer Immunotherapy clinical trials. She has spoken at symposia sponsored by Eli Lilly, Biogen, and Janssen. R. Sperling receives research support from Janssen Pharmaceuticals, and Eli Lilly and Co.. She also receives research support from the following grants: P01 AG036694, U01 AG032438, U01 AG024904, R01 AG037497, R01 AG034556, K24 AG035007, P50 AG005134, U19 AG010483, R01 AG027435, Fidelity Biosciences, Harvard NeuroDiscovery Center, and the Alzheimer’s Association. Paul Aisen has served as a consultant to the following companies:  NeuroPhage, Elan, Eisai, Bristol-Myers Squibb, Eli Lilly, Merck, Roche, Amgen, Genentech, Abbott, Pfizer, Novartis, AstraZeneca, Janssen, Medivation, Ichor, Lundbeck, Biogen, iPerian, Probiodrug, Anavex, Abbvie, Janssen, Cohbar.  Dr. Aisen receives research support from Eli Lilly, the Alzheimer’s Association and the NIH [NIA U01-AG10483 (PI), NIA U01-AG024904 (Coordinating Center Director), NIA R01-AG030048 (PI), and R01-AG16381 (Co-I)]. Jeffrey Cummings has received in kind research support from Avid Radiopharmaceuticals and Teva Pharmaceuticals. He has provided consultation to AbbVie, Acadia, ADAMAS, Alzheon, Anavex, AstraZeneca, Avanir, Biogen-Idec, Biotie, Boehinger-Ingelheim, Chase, Eisai, Forum, Genentech, Intracellular Therapies, Lilly, Lundbeck, Merck, Neurotrope, Novartis, Nutricia, Otsuka, Pfizer, Prana, QR Pharma, Resverlogix, Roche, Suven, Takeda, and Toyoma companies. He has provided consultation to GE Healthcare and MedAvante and owns stock in ADAMAS, Prana, Sonexa, MedAvante, Neurotrax, and Neurokos. Dr. Cummings owns the copyright of the Neuropsychiatric Inventory.

Acknowledgments: The authors wish to acknowledge the invaluable contributions of colleagues at the Harvard Aging Brain Study at Massachusetts General Hospital, the Center for Alzheimer Research and Treatment at the Brigham and Women’s Hospital, the Cleveland Clinic Low Ruvo Center for Brain Health, and the Alzheimer Therapeutic Research Institute at University of Southern California Keck School of Medicine. The authors also wish to thank Lisa Bain for assistance with the manuscript preparation.

Conflict of interest: None.

 

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