Deep learning to predict rapid progression of Alzheimer’s disease from pooled clinical trials: A retrospective study

The rate of progression of Alzheimer’s disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aβ) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79–0.82) to 0.82 (0.81–0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32–0.36) to 0.46 (0.44–0.49). External validation AUROCs ranged from 0.75 (0.70–0.81) to 0.83 (0.82–0.84) and AUPRCs from 0.27 (0.25–0.29) to 0.45 (0.43–0.48). Aβ plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.Author summary: Alzheimer’s Disease (AD), a progressive brain disorder that affects memory and cognitive skills, has different rates of progression in different individuals. Identifying rapid progressors (RPs) is vital for conducting clinical trials and determining effective treatments. RPs may exhibit distinctive characteristics and underlying pathophysiological differences compared to non-RPs, making it essential to identify RPs in randomized control trials (RCTs) to ensure balance between placebo and treated groups, or even develop their own trials if necessary. In this study, we aimed to develop deep learning models to detect rapid AD symptom progression and identify features contributing most to the model prediction using placebo-arm RCT data. This prediction can help refine subject selection in clinical trials for AD treatment. Clinical trialists could identify rapidly progressing patients in current trials and separate them within the trial(s), similar to the approach for patients with the APOEε4 allele. It could also enable clinical trials to develop RP-specific therapeutic interventions.