title: Composite risk scores for predicting dementia.
abstract: OBJECTIVE
  A key priority in dementia research is the development of tools to identify individuals at high risk of dementia. This is important to prevent or delay dementia onset and as we move towards personalized medicine.
  
  
  RESULTS
  Numerous models (n > 50) for predicting dementia have been developed. These vary in the number (0 to 20+) and type (e.g. demographics, health, neuropsychological, and genetic) of predictor variables used for risk calculation, follow-up length (1-20 years) and age at screening (mid vs laterlife). Evaluation of the models shows that most have moderate-to-poor predictive accuracy. Few have been externally validated, raising questions about their generalizability outside the cohorts from which they were developed. The results highlight that if additional models are proposed the field will be overwhelmed with many competing risk models, making it difficult to reach consensus on which is best.
  
  
  CONCLUSIONS
  Numerous models for predicting dementia have been proposed but are limited by a lack of external validation and evaluation of economic impact. Innovative methods and data designs may be needed to improve derivation of dementia risk scores. Having a method for predicting dementia risk could transform medical research and allow for earlier testing of intervention strategies.
authors.affiliation: aNewcastle University Institute for Ageing and Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK bCentre for Dementia Prevention, University of Edinburgh, Edinburgh, Scotland.
authors.forename: Blossom C M
authors.initials: BC
authors.lastname: Stephan
authors.forename: Eugene
authors.initials: E
authors.lastname: Tang
authors.forename: Graciela
authors.initials: G
authors.lastname: Muniz-Terrera
mesh_terms: D017668:Age of Onset; D003704:Dementia / Q000175:diagnosis / Q000517:prevention & control*; D006801:Humans; D011237:Predictive Value of Tests; D012306:Risk