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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Retrospective identification and characterization of mild cognitive impairment from a prospective population cohort

Karen Ritchie1, Marie-Laure Ancelin, Ephrem Beaino

  • 1Inserm U, Université de Montpellier, France. karen.ritchie@inserm.fr

The American Journal of Geriatric Psychiatry : Official Journal of the American Association for Geriatric Psychiatry
|April 15, 2011
PubMed
Abstract

Insights

Identifying mild cognitive impairment (MCI) in the general population is challenging. This study found gender-specific factors like depression and stroke history improve MCI case-finding specificity.

Area of Science:

  • Gerontology
  • Neurology
  • Epidemiology

Background:

  • Mild cognitive impairment (MCI) case-finding criteria often lack specificity in general population studies.
  • Accurate identification of MCI is crucial for early intervention and management.

Purpose of the Study:

  • To retrospectively identify MCI cases and determine baseline criteria with the highest discriminability.
  • To estimate the ability of these criteria to enhance current MCI case detection specificity.

Main Methods:

  • A population-based cohort of 8,919 individuals aged 65+ without dementia was recruited from three French cities.
  • Clinical and environmental characteristics were assessed at baseline and during 2- and 4-year follow-ups.
  • Retrospective analysis of clinical characteristics of incident dementia cases was performed.

Main Results:

  • 3.6% of participants were classified as MCI at baseline, showing poorer cognitive test performance and more white matter hyperintensities on MRI.
  • MCI was associated with ApoE ε4 genotype, daily living loss, and for women, depressive symptoms, anticholinergic drug use, and low BMI.
  • For men, MCI was linked to stroke history and elevated glycemia. Incorporating these factors improved MCI definition specificity.

Conclusions:

  • Gender-specific noncognitive clinical variables were identified that can enhance the specificity of MCI case-finding in general population studies.
  • These findings support the refinement of MCI diagnostic criteria using a broader range of clinical indicators.

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