PT - JOURNAL ARTICLE AU - Xiao-He Hou AU - Lei Feng AU - Can Zhang AU - Xi-Peng Cao AU - Lan Tan AU - Jin-Tai Yu TI - Models for predicting risk of dementia: a systematic review AID - 10.1136/jnnp-2018-318212 DP - 2019 Apr 01 TA - Journal of Neurology, Neurosurgery & Psychiatry PG - 373--379 VI - 90 IP - 4 4099 - http://jnnp.bmj.com/content/90/4/373.short 4100 - http://jnnp.bmj.com/content/90/4/373.full SO - J Neurol Neurosurg Psychiatry2019 Apr 01; 90 AB - 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.