ArticleJMIR aging2025
Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study.
Article in JMIR aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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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.
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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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Association of a Healthy Lifestyle With All-Cause and Cause-Specific Mortality Among Individuals With Probable Sarcopenia: Population-Based Cohort Study.JMIR aging · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundThe atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASCVD remain unclear, necessitating the development of accurate prediction models.
objectiveThe aim of the study is to develop a machine learning model for use in patients with ASCVD to predict dementia risk using available clinical and sociodemographic data.
methodsThis prognostic study included patients with ASCVD between 2006 and 2010, with registration of follow-up data ending on April 2023 based on the UK Biobank. We implemented a data-driven strategy, identifying predictors from 316 variables and developing a machine learning model to predict the risk of incident dementia, Alzheimer disease, and vascular dementia within 5, 10, and longer-term follow-up in patients with ASCVD.
resultsA total of 29,561 patients with ASCVD were included, and 1334 (4.51%) developed dementia during a median follow-up time of 10.3 (IQR 7.6-12.4) years. The best prediction model (UK Biobank ASCVD risk prediction model) was light gradient boosting machine, comprising 10 predictors including age, time to complete pairs matching tasks, mean time to correctly identify matches, mean sphered cell volume, glucose levels, forced expiratory volume in 1 second z score, C-reactive protein, forced vital capacity, time engaging in activities, and age first had sexual intercourse. This model achieved the following performance metrics for all incident dementia: area under the receiver operating characteristic curve: mean 0.866 (SD 0.027), accuracy: mean 0.883 (SD 0.010), sensitivity: mean 0.637 (SD 0.084), specificity: mean 0.914 (SD 0.012), precision: mean 0.479 (SD 0.031), and F
conclusionsThe findings of this study suggest that predictive modeling could inform patients and clinicians about ASCVD at risk for dementia.
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