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Health and Cognition in Old AgeModels for Predicting Risk of Dementia: Predictive Accuracy and Model Complexity

Health and Cognition in Old Age: Models for Predicting Risk of Dementia: Predictive Accuracy and... [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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Health and Cognition in Old AgeModels for Predicting Risk of Dementia: Predictive Accuracy and Model Complexity

Part of the International Perspectives on Aging Book Series (volume 10)
Editors: Leist, Anja K.; Kulmala, Jenni; Nyqvist, Fredrica
Stephan, Blossom Christa Maree
Health and Cognition in Old Age — Jun 10, 2014
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References (75)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2014
ISBN
978-3-319-06649-3
Pages
141 –159
DOI
10.1007/978-3-319-06650-9_10
Publisher site
See Chapter on Publisher Site

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.]

Published: Jun 10, 2014

Keywords: Mild Cognitive Impairment; Risk Score; Framingham Risk Score; Dementia Risk; Integrate Discrimination Improvement

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