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

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.

Key Points

  • Strategies are needed to identify individuals at risk of dementia long before disease onset, so that resources, treatment and prevention efforts can be efficiently targeted

  • Numerous models of dementia risk have been proposed but their relative merits in terms of predictive accuracy are unknown

  • Our evaluation of the literature suggests that current risk models are poor at distinguishing people at risk of developing dementia from not-at-risk individuals

  • A consensus model for the prediction of dementia is needed that is applicable to both population and clinical settings

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Acknowledgements

Désirée Lie, Univesity of California, Orange, CA, is the author of and is solely responsible for the content of the learning objectives, questions and answers of the MedscapeCME-accredited continuing medical education activity associated with this article.

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Correspondence to Blossom C. M. Stephan.

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Supplementary information

Supplementary Table 1

Studies of mild cognitive impairment models reviewed (DOC 180 kb)

Supplementary Table 2

Studies of non-mild cognitive impairment models reviewed (DOC 464 kb)

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Stephan, B., Kurth, T., Matthews, F. et al. Dementia risk prediction in the population: are screening models accurate?. Nat Rev Neurol 6, 318–326 (2010). https://doi.org/10.1038/nrneurol.2010.54

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