Automatic prediction of cognitive and functional decline can significantly decrease the number of subjects required for clinical trials in early Alzheimer's disease

TL;DR

This study develops a prognostic model using baseline cognitive scores and MRI features to predict cognitive decline in early Alzheimer's disease, potentially reducing clinical trial sizes and accelerating treatment development.

Contribution

The paper introduces a new predictive tool combining cognitive and MRI data to identify at-risk patients, improving trial efficiency in early Alzheimer's disease.

Findings

Achieves 77% accuracy in predicting 2-year decline

Reduces required sample size by 3.8-fold for 2-year trials

Improves trial power with combined baseline features

Abstract

INTRODUCTION: Heterogeneity in the progression of Alzheimer's disease makes it challenging to predict the rate of cognitive and functional decline for individual patients. Tools for short-term prediction could help enrich clinical trial designs and focus prevention strategies on the most at-risk patients. METHOD: We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects with mild cognitive impairment remained stable and which functionally declined (measured by a two-point increase in CDR-SB) over 2 and 3-year follow-up periods, periods typical of the length of clinical trials. RESULTS: Combining both sets of features yields 77% accuracy (81% sensitivity and 75% specificity) to predict cognitive decline at 2 years (74% accuracy at 3 years with 75% sensitivity and 73% specificity). Using this tool to select trial participants yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25% treatment effect to reduce cognitive decline. DISCUSSION: This cohort enrichment tool could accelerate treatment development by increasing power in clinical trials.