Forecasting individual progression trajectories in Alzheimer’s disease
By Etienne Maheux, Igor Koval, Juliette Ortholand, Colin Birkenbihl, Damiano Archetti, Vincent Bouteloup, Stéphane Epelbaum, Carole Dufouil, Martin Hofmann-Apitius & Stanley Durrleman
Keywords: Disease Progression Modelling Alzheimer's disease Prognostic Enrichment Forecast of cognitive decline
This work is two-fold: it first evaluates performance, generalization and fairness of single-subject predictions during the course of Alzheimer’s disease, in a pool of 4,600+ individuals from 5 independent cohorts, using different predictive models including the AD Course Map, a versatile and interpretable disease progression model, and a state-of-the-art recurrent neural network. The use of these single-subject predictions in a prognostic enrichment strategy is then validated in 6 simulated clinical trials mimicking recent or on-going AD trials. Authors show that enriching the population with the likely decliners 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.
Trial participants are selected first using standard inclusion criteria and undergo a series of exams. A disease progression model, such as AD Course Map, then forecasts the progression of each participant's data and predicts if the participant is likely to progress significantly during the trial, as measured by the predicted outcome change, which is the mini-mental state examination (MMSE) in this example. The treatment effect (e.g., a 25% reduction of the change of the MMSE during trial) leads to a greater effect size, and therefore a smaller sample size, on the group of predicted fast progressors compared to the group of predicted slow progressors or the two groups combined. As a result, one may demonstrate the treatment efficacy with fewer participants by monitoring only the group of predicted fast progressors.
Full title Forecasting individual progression trajectories in Alzheimer's disease
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.