Dataset Information

Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials.

ABSTRACT: Background: Screening to identify individuals with elevated brain amyloid (A?+) 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 A?+ (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 A?+ 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.

SUBMITTER: Langford O

PROVIDER: S-EPMC7745538 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials.

Langford O O Raman R R Sperling R A RA Cummings J J Sun C-K CK Jimenez-Maggiora G G Aisen P S PS Donohue M C MC

The journal of prevention of Alzheimer's disease 20200101 4

<h4>Background</h4>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 ...[more]

PMID: 32920622

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Project description:BackgroundDetecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer's disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials.MethodsBaseline and annualized % change in [11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database ( www.oasis-brains.org ). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1 < Centiloid ≤ 49.4) amyloid burden, and (3) a primary prevention trial focusing on subjects with low amyloid burden (Centiloid ≤ 20.1). Trial scenarios were set to detect 20% reduction in accumulation rates across the whole population and in APOE-ε4 carriers only.ResultsAlthough highly correlated to DVR (ρ = .96), cortical SUVR overestimated DVR cross-sectionally and in annual % change. In secondary prevention trials, DVR required 143 subjects per arm, compared with 176 for SUVR. Both restricting inclusion to individuals with intermediate amyloid burden levels or to APOE-ε4 carriers alone further reduced sample sizes. For primary prevention, SUVR required less subjects per arm (n = 855) compared with DVR (n = 1508) and the early composite also provided considerable sample size reductions (n = 855 to n = 509 for SUVR, n = 1508 to n = 734 for DVR).ConclusionSample sizes in AD secondary prevention trials can be reduced by the acquisition of dynamic PET scans and/or by restricting inclusion to subjects with intermediate amyloid burden or to APOE-ε4 carriers only. Using a targeted early composite only leads to reductions of sample size requirements in primary prevention trials. These findings support strategies to enable smaller Proof-of-Concept Phase II clinical trials to better streamline drug development.

Dataset Information

Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials.

Publications

Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials.

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