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Aim: To evaluate previously developed classification models to make implementation in primary care possible and aid early identification of persons at risk for dementia., Methods: Data were drawn from the OCTO-Twin study. At baseline, 521 persons >or= 80 years of age were nondemented, and for 387 a blood sample was available. Predictors of dementia were collected and analyzed in initially nondemented persons using generalized estimating equations and Cox survival analyses., Results: In the basic model using predictors already known or easily obtained (basic set), the mean 2-year predictive value increased from 6.9 to 28.8% in persons with memory complaints and an MMSE score

Objective: To develop two classification models for use in primary care to aid early identification of persons at risk for persistent cognitive decline., Methods: Data were used from the Longitudinal Aging Study Amsterdam (LASA), an ongoing populationbased study. The study sample consisted of 2,021 non-demented men and women aged 58-88 years. Data on relevant predictors of persistent cognitive decline were collected at baseline., Results: The incidence of persistent cognitive decline after three years of follow-up was 4.0 %. In the first model, in which predictors already known or otherwise easily assessed (first set) were included, age was the strongest predictor of persistent cognitive decline, with an increased risk for persons > 75. In addition, having memory problems, low education, and a MMSE score of < or = 24, resulted in a predictive value for persistent cognitive decline of 43.5 %. In the second classification model, in addition to the first set, predictors requiring additional measurement (e.g. markers determined in blood) were included in the analyses. Age was again the strongest predictor of persistent cognitive decline. In persons > 75 years, having a low total cholesterol level (< 5.0 mmol/L) and a MMSE score of < or = 24 resulted in a predictive value of 30.0 %., Conclusions: Both models lead to a substantial increase of the predictive value for persistent cognitive decline, that is from 4.0 % to 43.5 % and 30.0 %, and may identify to a large extent a different subsample of persons who are at risk for persistent cognitive decline. The developed classification trees could be useful for case-finding of persons at risk for future persistent cognitive decline who are therefore at risk for dementia, in a feasible and cost-effective manner.

In order to identify persons who are at risk for dementia in an early phase, two classification trees were developed. Data were used from the Longitudinal Aging Study Amsterdam (LASA). The prevalence of dementia in the whole sample was 4.0%. In the first tree age seemed to be the strongest predictor, with an increased risk for persons older than 75. In this group the positive predictive value reached a maximum of 33.3% when the persons had memory complaints and a score on the Mini Mental State Examination (MMSE) <24. In a second classification tree, age was excluded as a predictor because of high association with the other potential predictors. In this tree functional limitations seemed the strongest predictor. In the group of persons with at least one functional limitations, the positive predictive value reached a maximum of 28.8% when the persons had memory complaints and a score <24 on the MMSE. In persons without memory complaints, persons with cardiovascular diseases or diabetes were at increased risk of dementia. Further research is necessary before these classification trees can be implemented in general health care.