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Updated: Sep 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Incidence and outcome of mild cognitive impairment in a population-based prospective cohort
S Larrieu1, L Letenneur, J M Orgogozo
1Epidemiology Research Unit, Institut National de la Santé et de la Recherche Médicale (INSERM) U 330, France. Sophie.Larrieu@isped.u-bordeaux2.fr
Objective:
To estimate the age-specific incidence rate of mild cognitive impairment (MCI) according to sex and educational level and to explore the course of MCI, particularly its progression to AD, in a population-based cohort.
Methods:
A community-based cohort of nondemented elderly people (Personnes Agées QUID [PAQUID]) was followed longitudinally for 5 years. MCI was defined as memory complaints with objective memory impairment, without dementia, impairment of general cognitive functioning, or disability in activities of daily living. Incidence rates were calculated using the person-years method. A descriptive analysis at the different follow-up times was performed to study the course of MCI.
Results:
At baseline, there were 58 prevalent cases of MCI (2.8% of the sample). During a 5-year follow-up, 40 incident cases of MCI occurred in 1,265 subjects at risk. The global incidence rate of MCI was 9.9/1,000 person-years. MCI was a good predictor of AD with an annual conversion rate of 8.3% and a good specificity, but it was very unstable over time: Within 2 to 3 years, only 6% of the subjects continued to have MCI, whereas >40% reverted to normal.
Conclusions:
Conventionally defined MCI has reasonable predictive value and specificity for AD. However, MCI was very unstable across time in this study. Furthermore, the definition of MCI seems to be too restrictive and should probably be extended to other categories of individuals also at high risk of developing AD.
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