Forecasting individual progression trajectories in Alzheimer’s disease

• Published in: Nature communications Vol. 14; no. 1; pp. 761 - 15
• Main Authors: Maheux, Etienne, Koval, Igor, Ortholand, Juliette, Birkenbihl, Colin, Archetti, Damiano, Bouteloup, Vincent, Epelbaum, Stéphane, Dufouil, Carole, Hofmann-Apitius, Martin, Durrleman, Stanley
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.02.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/2779; 692/53/2422; 692/617/375/132/1283; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - psychology; Alzheimer's disease; Applications; Artificial Intelligence; Biomarkers; Clinical trials; Cognitive Dysfunction; Cognitive science; Computer Science; Disease prevention; Disease Progression; Evaluation; Geographical locations; Humanities and Social Sciences; Humans; Life Sciences; Machine Learning; Mathematical models; Missing data; Modeling and Simulation; multidisciplinary; Neurodegenerative diseases; Neuroimaging - methods; Neurons and Cognition; Neuroscience; Patients; Research Design; Science; Science (multidisciplinary); Statistical models; Statistics; Enrichment; Mixed-effects; Longitudinal; Prediction; Forecast; Clinical trial; Alzheimer; Prognostic
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-35712-5

The anticipation of progression of Alzheimer’s disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials. Accurate prediction of disease progression in Alzheimer’s disease (AD) is necessary for optimal recruitment of patients to clinical trials. Here, the authors present AD Course Map, a statistical model which helps to predict disease progression in participants, thus decreasing the required sample size for a hypothetical trial.