Prediction of cognitive decline for enrichment of Alzheimer's disease clinical trials

TL;DR

This study develops machine learning models using baseline data to predict cognitive decline in early Alzheimer's disease, aiming to improve participant selection and trial efficiency.

Contribution

The paper introduces prognostic models that predict cognitive decline over 24 and 48 months, enabling better enrichment strategies for clinical trials.

Findings

Models achieved up to 79% AUC in predicting decliners.

Using models can reduce sample sizes by up to 51%.

Predictions can improve trial success and accelerate drug development.

Abstract

A key issue to Alzheimer's disease clinical trial failures is poor participant selection. Participants have heterogeneous cognitive trajectories and many do not decline during trials, which reduces a study's power to detect treatment effects. Trials need enrichment strategies to enroll individuals who will decline. We developed machine learning models to predict cognitive trajectories in participants with early Alzheimer's disease (n=1342) and presymptomatic individuals (n=756) over 24 and 48 months respectively. Baseline magnetic resonance imaging, cognitive tests, demographics, and APOE genotype were used to classify decliners, measured by an increase in CDR-Sum of Boxes, and non-decliners with up to 79% area under the curve (cross-validated and out-of-sample). Using these prognostic models to recruit enriched cohorts of decliners can reduce required sample sizes by as much as 51%, while maintaining the same detection power, and thus may improve trial quality, derisk endpoint failures, and accelerate therapeutic development in Alzheimer's disease.