ArticleNature communications2023
Forecasting individual progression trajectories in Alzheimer's disease.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Multimodal prognostic modeling of individual cognitive trajectories to enhance trial efficiency in preclinical Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Enhancing early Alzheimer's disease clinical trials through prognostic score covariate adjustment.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Robust Longitudinal Dementia Prediction under Systemic Missingness via Hierarchical Fusion and Test-Time Adaptation.medRxiv : the preprint server for health sciences · 2026Article
- Forecasting Alzheimer's disease progression via identity-preserved denoising diffusion generative adversarial network.NPJ digital medicine · 2026Article
- Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.Scientific reports · 2026Article
- Predicting rates of cognitive and functional decline in Alzheimer's disease and mild cognitive impairment.Communications medicine · 2026Article
- A review of evidence supporting amyloid beta reduction as a surrogate endpoint in Alzheimer's disease.The journal of prevention of Alzheimer's disease · 2026Review
- ShinyEvents: harmonizing longitudinal data for real-world survival estimation.NPJ precision oncology · 2026Article
- Prediction-powered inference for clinical trials: application to linear covariate adjustment.BMC medical research methodology · 2025Article
- Prediction-powered Inference for Clinical Trials: application to linear covariate adjustment.medRxiv : the preprint server for health sciences · 2025Article
- Cross-Dataset Evaluation of Dementia Longitudinal Progression Prediction Models.Human brain mapping · 2025Article
- Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models.medRxiv : the preprint server for health sciences · 2025Article
- Utilization of precision medicine digital twins for drug discovery in Alzheimer's disease.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Predicted natural progression as an Alzheimer's prognostic covariate improves the precision of lecanemab efficacy assessments and clinical trial efficiency.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer's disease.Frontiers in artificial intelligence · 2025Article
- Retrosplenial hypometabolism precedes the conversion from mild cognitive impairment to Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.JAMIA open · 2024Article
- Risk factors for the neurodegenerative dementias in the Western Pacific region.The Lancet regional health. Western Pacific · 2024Review
- Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer's type.Scientific reports · 2024Article
- Predicting clinical progression trajectories of early Alzheimer's disease patients.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.