ArticleJournal of Alzheimer's disease : JAD2021
Automatic Prediction of Cognitive and Functional Decline Can Significantly Decrease the Number of Subjects Required for Clinical Trials in Early Alzheimer's Disease.
Article in Journal of Alzheimer's disease : JAD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 1 citations in OpenAlex.
- CAREUP: An Integrated Care Platform with Intrinsic Capacity Monitoring and Prediction Capabilities.Sensors (Basel, Switzerland) · 2025Article
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- The higher benefit of lecanemab in males compared to females in CLARITY AD is probably due to a real sex effect.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWhile both cognitive and magnetic resonance imaging (MRI) data has been used to predict progression in Alzheimer's disease, heterogeneity between patients makes it challenging to predict the rate of cognitive and functional decline for individual subjects.
objectiveTo investigate prognostic power of MRI-based biomarkers of medial temporal lobe atrophy and macroscopic tissue change to predict cognitive decline in individual patients in clinical trials of early Alzheimer's disease.
methodsData used in this study included 312 patients with mild cognitive impairment from the ADNI dataset with baseline MRI, cerebrospinal fluid amyloid-β, cognitive test scores, and a minimum of two-year follow-up information available. We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects remain stable and which functionally decline over 2 and 3-year follow-up periods.
resultsCombining 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). When used to select trial participants, this tool 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.
conclusionWhen used in clinical trials for cohort enrichment, this tool could accelerate development of new treatments by significantly increasing statistical power to detect differences in cognitive decline between arms. In addition, detection of future decline can help clinicians improve patient management strategies that will slow or delay symptom progression.
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Registered trials
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