Dementia risk prediction in the population: Are screening models accurate?

Stephan, Blossom C.M.; Kurth, Tobias; Matthews, Fiona E.; Brayne, Carol; Dufouil, Carole

Abstract

Early identification of individuals at risk of dementia will become crucial when effective preventative strategies for this condition are developed. Various dementia prediction models have been proposed, including clinic-based criteria for mild cognitive impairment, and more-broadly constructed algorithms, which synthesize information from known dementia risk factors, such as poor cognition and health. Knowledge of the predictive accuracy of such models will be important if they are to be used in daily clinical practice or to screen the entire older population (individuals aged 65 years). This article presents an overview of recent progress in the development of dementia prediction models for use in population screening. In total, 25 articles relating to dementia risk screening met our inclusion criteria for review. Our evaluation of the predictive accuracy of each model shows that most are poor at discriminating at-risk individuals from not-at-risk cases. The best models incorporate diverse sources of information across multiple risk factors. Typically, poor accuracy is associated with single-factor models, long follow-up intervals and the outcome measure of all-cause dementia. A parsimonious and cost-effective consensus model needs to be developed that accurately identifies individuals with a high risk of future dementia. © 2010 Macmillan Publishers Limited. All rights reserved.

Citation

Stephan, B. C., Kurth, T., Matthews, F. E., Brayne, C., & Dufouil, C. (2010). Dementia risk prediction in the population: Are screening models accurate?. Nature Reviews Neurology, 6(6), 318-326. https://doi.org/10.1038/nrneurol.2010.54

• Journal Article Type: Review
• Publication Date: Jun 1, 2010
• Deposit Date: Dec 8, 2023
• Journal: Nature Reviews Neurology
• Print ISSN: 1759-4758
• Electronic ISSN: 1759-4766
• Publisher: Nature Research
• Volume: 6
• Issue: 6
• Pages: 318-326
• DOI: https://doi.org/10.1038/nrneurol.2010.54
• Public URL: https://hull-repository.worktribe.com/output/4455036