The progression of dementia prevalence over the years and the lack of efficient treatments to stop or reverse the cognitive decline make dementia a major public health challenge in the developed world. Identifying people at high risk of developing dementia could improve the treatment of these patients and help select the target population for preventive clinical trials. We used joint modeling to build a dynamic prediction tool of dementia based on the change over time of 2 neurocognitive tests (the Mini-Mental State Examination and the Isaacs Set Tests) as well as an autonomy scale (the Instrumental Activities of Daily Living). The model was estimated with data from the French cohort Personnes Agées QUID (1988-2015) and validated both by cross-validation and externally with data from the French Three City cohort (1999-2018). We evaluated its predictive abilities through area under the receiver operating characteristics curve and Brier score, accounting for right censoring and competing risk of death, and obtained an average area under the curve value of 0.95 for the risk of dementia in the next 5 or 10 years. This tool is able to discriminate a high-risk group of people from the rest of the population. This could be of help in clinical practice and research.
Keywords: Alzheimer disease; cognition; dementia; dependency; joint model; prediction.
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