ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Dynamic lifetime risk prediction of Alzheimer's disease with longitudinal cognitive assessment measurements.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.BMC medical research methodology · 2025Pooled it
- The value of machine learning models in differentiating alzheimer's disease from Moderate-to-Severe cerebral small vessel disease.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Lifetime risk of incident dementia and incident mild cognitive impairment in older adults.medRxiv : the preprint server for health sciences · 2025Article
- A Latent-Class Model for Time-To-Event Outcomes and High-Dimensional Imaging Data.Statistics in medicine · 2025Article
- Dynamic lifetime risk prediction of Alzheimer's disease with longitudinal cognitive assessment measurements.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
introductionThe progressive nature of Alzheimer's disease (AD) highlights the importance of predicting lifetime risk and updating assessments as new data emerge. This study aimed to develop a dynamic model using longitudinal cognitive assessments for updated risk predictions.
methodsThis study used data from the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) to develop a dynamic risk prediction model based on five cognitive domains, updated annually over 10 years.
resultsThe lifetime prediction models based on 2384 participants showed improved area under the curve (AUC) over time, rising from 0.578 at baseline to 0.765 with 10 years of data. The models predicting AD onset before ages 85 and 90 showed superior performance, with AUCs increasing from 0.761 to 0.932 and 0.658 to 0.876, respectively. DISCUSSION: Incorporating longitudinal cognitive assessments improves AD risk prediction as more data become available. Future research should integrate diverse data types to further boost predictive accuracy. HIGHLIGHTS: Developed a dynamic lifetime risk prediction model. The area under the curve (AUC) increased from 0.578 at baseline to 0.765 with 10 years of data. The models predicting pre-85 and pre-90 risks demonstrated superior performance.
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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.