  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.
