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Meta-Analysis
. 2017 Dec;27(4):328-353.
doi: 10.1007/s11065-017-9361-5. Epub 2017 Oct 10.

Neuropsychological Measures that Predict Progression from Mild Cognitive Impairment to Alzheimer's type dementia in Older Adults: a Systematic Review and Meta-Analysis

Sylvie Belleville  1   2 , Céline Fouquet  3 , Carol Hudon  4   5 , Hervé Tchala Vignon Zomahoun  6   7 , Jordie Croteau  6   7 ; Consortium for the Early Identification of Alzheimer’s disease-Quebec
Affiliations
Meta-Analysis

Neuropsychological Measures that Predict Progression from Mild Cognitive Impairment to Alzheimer's type dementia in Older Adults: a Systematic Review and Meta-Analysis

Sylvie Belleville et al. Neuropsychol Rev. 2017 Dec.

Abstract

This study aimed to determine the extent to which cognitive measures can predict progression from mild cognitive impairment (MCI) to Alzheimer's type dementia (AD), assess the predictive accuracy of different cognitive domain categories, and determine whether accuracy varies as a function of age and length of follow-up. We systematically reviewed and meta-analyzed data from longitudinal studies reporting sensitivity and specificity values for neuropsychological tests to identify individuals with MCI who will develop AD. We searched articles in Medline, Cochrane, EMBASE, PsycINFO, and the Web of Science. Methodological quality was assessed using the STARDem and QUADAS standards. Twenty-eight studies met the eligibility criteria (2365 participants) and reported predictive values from 61 neuropsychological tests with a 31-month mean follow-up. Values were pooled to provide combined accuracy for 14 cognitive domains. Many domains showed very good predictive accuracy with high sensitivity and specificity values (≥ 0.7). Verbal memory measures and many language tests yielded very high predictive accuracy. Other domains (e.g., executive functions, visual memory) showed better specificity than sensitivity. Predictive accuracy was highest when combining memory measures with a small set of other domains or when relying on broad cognitive batteries. Cognitive tests are excellent at predicting MCI individuals who will progress to dementia and should be a critical component of any toolkit intended to identify AD at the pre-dementia stage. Some tasks are remarkable as early indicators, whereas others might be used to suggest imminent progression.

Keywords: Alzheimer’s disease; Cognitive tests; Diagnosis; Mild cognitive impairment; Neuropsychology; Predictive accuracy.

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Conflict of interest statement

S. Belleville receives research grants from the Fonds d’Innovation Pfizer-FRQS for Alzheimer’s disease and related diseases for the Consortium pour l’identification Précoce de la Maladie d’Alzheimer - Québec (CIMA-Q). She has been a consultant for research development on the prevention of Alzheimer’s Disease for the Fondation IUGM (2016) and a consultant for the development of a cognitive stimulation program for the Centre de Promotion de la Santé AvantÂge (2015). She has intellectual property rights for the Programme de Stimulation pour une santé cognitive, Memoria, Batterie d’évaluation de la mémoire Côte-des-Neiges and MEMO, Programme pour une mémoire optimale.

C. Hudon receives funding from the Fonds d’Innovation Pfizer-FRQS for Alzheimer’s disease and related diseases for the Consortium pour l’identification Précoce de la Maladie d’Alzheimer - Québec (CIMA-Q) and from the Canadian Institutes on Health Research (MOP-119612). He also received a salary award (Chercheur-boursier Senior) from the FRQS. He received honoraria as a speaker at a Lundbeck Colloquium held in Québec City (March 2016). He is a consultant for Cogstate, Bracket and a rater for Roche and Lundbeck.

C. Fouquet, H.T.V Zomahoun, and J. Croteau report no conflict of interest.

Figures

Fig. 1
Fig. 1
PRISMA 2009 Flow Diagram
See this image and copyright information in PMC

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