Models for predicting risk of dementia: a systematic review

Abstract

Background: Information from well-established dementia risk models can guide targeted intervention to prevent dementia, in addition to the main purpose of quantifying the probability of developing dementia in the future.

Methods: We conducted a systematic review of published studies on existing dementia risk models. The models were assessed by sensitivity, specificity and area under the curve (AUC) from receiver operating characteristic analysis.

Results: Of 8462 studies reviewed, 61 articles describing dementia risk models were identified, with the majority of the articles modelling late life risk (n=39), followed by those modelling prediction of mild cognitive impairment to Alzheimer's disease (n=15), mid-life risk (n=4) and patients with diabetes (n=3). Age, sex, education, Mini Mental State Examination, the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological assessment battery, Alzheimer's Disease Assessment Scale-cognitive subscale, body mass index, alcohol intake and genetic variables are the most common predictors included in the models. Most risk models had moderate-to-high predictive ability (AUC>0.70). The highest AUC value (0.932) was produced from a risk model developed for patients with mild cognitive impairment.

Conclusion: The predictive ability of existing dementia risk models is acceptable. Population-specific dementia risk models are necessary for populations and subpopulations with different characteristics.

Keywords: alzheimer’s disease; dementia; prediction; risk model; systematic review.