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Models for Predicting Risk of Dementia: Predictive Accuracy and Model Complexity

  • Blossom Christa Maree StephanEmail author
Chapter
Part of the International Perspectives on Aging book series (Int. Perspect. Aging, volume 10)

Abstract

As strategies to prevent dementia or delay disease progression are developed, it will be important to have risk prediction models to prioritize and target intervention to high-risk individuals. Targeting whole populations is not always cost-effective, particularly when intervention strategies are expensive or adherence rates are low. A complementary approach may be to develop a prediction algorithm to identify individuals at highest risk of dementia as early as possible without being too broad in risk selection. This chapter will present an overview of existing dementia risk prediction models with a focus on their predictive accuracy (e.g., sensitivity, specificity and discrimination), the types of variables incorporated (e.g., cognitive, neuroimaging, genetics and health assessment) and the cost of attaining the risk score (e.g., in terms of equipment and the need for specialist training for risk score attainment). A better understanding of available dementia risk models and their predictive accuracy has implications for improving diagnostics, targeting services and undertaking of more focused risk factor reduction in older aged populations.

Keywords

Mild Cognitive Impairment Risk Score Framingham Risk Score Dementia Risk Integrate Discrimination Improvement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Part of the work in this chapter was supported by a European Research Area in Ageing (ERA-Age) Future Leaders of Ageing Research in Europe Postdoctoral Fellowship (FLARE Fellowship; awarded through the Medical Research Council: MRC, UK), awarded to Dr. Blossom C.M. Stephan.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Health and SocietyNewcastle UniversityNewcastle upon TyneUK

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