Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.

Background: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by expert clinicians.

Objectives: Our study explores the use of natural language processing (NLP) and artificial intelligence (AI) methods to streamline and standardize clinician coding of adverse event data in Alzheimer's disease (AD) clinical trials.

Racial and ethnic differences in plasma biomarker eligibility for a preclinical Alzheimer's disease trial.

Introduction: In trials of amyloid-lowering drugs for Alzheimer's disease (AD), differential eligibility may contribute to under-inclusion of racial and ethnic underrepresented groups. We examined plasma amyloid beta 42/40 and positron emission tomography (PET) amyloid eligibility for the ongoing AHEAD Study preclinical AD program (NCT04468659).

Methods: Univariate logistic regression models were used to examine group differences in plasma and PET amyloid screening eligibility.

Plasma Aβ42/Aβ40 and phospho-tau217 concentration ratios increase the accuracy of amyloid PET classification in preclinical Alzheimer's disease.

Introduction: Incorporating blood-based Alzheimer's disease biomarkers such as tau and amyloid beta (Aβ) into screening algorithms may improve screening efficiency.

Methods: Plasma Aβ, phosphorylated tau (p-tau)181, and p-tau217 concentration levels from AHEAD 3-45 study participants were measured using mass spectrometry. Tau concentration ratios for each proteoform were calculated to normalize for inter-individual differences.

Centralizing prescreening data collection to inform data-driven approaches to clinical trial recruitment.

Background: Recruiting to multi-site trials is challenging, particularly when striving to ensure the randomized sample is demographically representative of the larger disease-suffering population. While previous studies have reported disparities by race and ethnicity in enrollment and randomization, they have not typically investigated whether disparities exist in the recruitment process prior to consent. To identify participants most likely to be eligible for a trial, study sites frequently include a prescreening process, generally conducted by telephone, to conserve resources.

Early-stage Alzheimer disease: getting trial-ready.

Slowing the progression of Alzheimer disease (AD) might be the greatest unmet medical need of our time. Although one AD therapeutic has received a controversial accelerated approval from the FDA, more effective and accessible therapies are urgently needed. Consensus is growing that for meaningful disease modification in AD, therapeutic intervention must be initiated at very early (preclinical or prodromal) stages of the disease.

Corrigendum to: ATRI EDC: a novel cloud-native remote data capture system for large multicenter Alzheimer's disease and Alzheimer's disease-related dementias clinical trials.

[This corrects the article DOI: 10.1093/jamiaopen/ooab119.].

ATRI EDC: a novel cloud-native remote data capture system for large multicenter Alzheimer's disease and Alzheimer's disease-related dementias clinical trails.

Objective: The Alzheimer's Therapeutic Research Institute (ATRI) developed a novel clinical data management system, the ATRI electronic data capture system (ATRI EDC), to address the complex regulatory, operational, and data requirements that arise in the conduct of multicenter Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRDs) clinical trials. We describe the system, its utility, and the broader implications for the field of clinical trials and clinical research informatics.

Materials And Methods: The ATRI EDC system was developed, tested, and validated using community-based agile software development methods and cloud-native single-page application design principles.

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

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

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

Results: Age, family history, online Cognitive Function Instrument and CogState scores were important predictors.

World-Wide FINGERS Network: A global approach to risk reduction and prevention of dementia.

Reducing the risk of dementia can halt the worldwide increase of affected people. The multifactorial and heterogeneous nature of late-onset dementia, including Alzheimer's disease (AD), indicates a potential impact of multidomain lifestyle interventions on risk reduction. The positive results of the landmark multidomain Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) support such an approach.