Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review.
BACKGROUND
Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance.


METHODS
Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included.


RESULTS
In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model.


CONCLUSIONS
There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.
Institute of Health and Society, Newcastle University Institute of Ageing, Newcastle University, Newcastle upon Tyne, NE2 4AX, United Kingdom.
Eugene Y H
EY
Tang
Institute of Health and Society, Newcastle University Institute of Ageing, Newcastle University, Newcastle upon Tyne, NE2 4AX, United Kingdom.
Stephanie L
SL
Harrison
Medical School, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom.
Linda
L
Errington
Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, Connecticut, 06877, United States of America.
Mark F
MF
Gordon
Maastricht University, Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands; VU University Medical Centre, Department of Neurology, Alzheimer Centre, Neuroscience Campus, Amsterdam, The Netherlands.
Pieter Jelle
PJ
Visser
Janssen Pharmaceutical Research and Development, 1125 Trenton-Harbourton Road, Titusville, New Jersey, 08560, United States of America.
Gerald
G
Novak
Inserm Research Centre (U897), Team Neuroepidemiology, F-33000, Bordeaux, France.
Carole
C
Dufouil
Department of Public Health and Primary Care, Cambridge University, Cambridge, CB2 0SR, United Kingdom.
Carol
C
Brayne
Institute of Health and Society, Newcastle University Institute of Ageing, Newcastle University, Newcastle upon Tyne, NE2 4AX, United Kingdom.
Louise
L
Robinson
Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, National Institutes of Health (NIH), Bethesda, Maryland, United States of America.
Lenore J
LJ
Launer
Institute of Health and Society, Newcastle University Institute of Ageing, Newcastle University, Newcastle upon Tyne, NE2 4AX, United Kingdom.
Blossom C M
BC
Stephan
D003704:Dementia / Q000453:epidemiology*; D006801:Humans; D008962:Models, Theoretical; D012307:Risk Factors