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

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Published on: January 11, 2020

Mini-mental State Examination performance in mild cognitive impairment subtypes.

Breno S O Diniz1, Monica S Yassuda, Paula V Nunes

  • 1Laboratory of Neuroscience, Department and Institute of Psychiatry, Faculty of Medicine, University of Sao Paulo, Brazil.

International Psychogeriatrics
|May 16, 2007
PubMed
Summary

Screening for mild cognitive impairment (MCI) using standard dementia tests is challenging. Analyzing Mini-Mental State Examination (MMSE) subtest scores improves detection sensitivity for MCI and its subtypes.

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Area of Science:

  • Neurology
  • Gerontology
  • Cognitive Science

Background:

  • Diagnosing mild cognitive impairment (MCI) and its subtypes can be challenging in primary care settings.
  • Standard dementia screening tests often lack sensitivity, particularly in highly educated individuals, due to ceiling effects.
  • Early and accurate diagnosis of MCI is crucial for timely intervention and management.

Purpose of the Study:

  • To evaluate the diagnostic utility of Mini-Mental State Examination (MMSE) subtest scores for identifying mild cognitive impairment (MCI) in elderly subjects.
  • To compare the performance of MCI subtypes (amnestic, non-amnestic, multiple-domain) on specific neuropsychological tasks.
  • To determine if analyzing MMSE subtest scores enhances the sensitivity of the MMSE for MCI detection compared to total scores alone.

Main Methods:

  • A cross-sectional study involving 112 elderly participants (mean age 67.96 years, mean education 12.8 years).
  • Utilized a semi-structured interview and a comprehensive neuropsychological battery for assessment.
  • Compared cognitive performance between individuals with MCI and control groups.

Main Results:

  • Overall MMSE total scores did not significantly differentiate MCI patients from controls (p=0.212).
  • MCI patients demonstrated significantly poorer performance on verbal memory (p=0.012) and "drawing a pentagon" tasks (p=0.03).
  • Specific MCI subtypes showed distinct performance deficits: amnestic MCI on word recall (p=0.013), non-amnestic MCI on command following (p=0.001), and multiple-domain MCI on "drawing a pentagon" (p=0.001) and a trend in word recall (p=0.06).

Conclusions:

  • Analysis of individual MMSE subtest scores, beyond the total score, can significantly improve the sensitivity of the MMSE for detecting MCI.
  • This approach aids in identifying MCI and differentiating its subtypes, which may be missed by conventional screening methods.
  • Integrating subtest analysis into MMSE assessments offers a more nuanced tool for early cognitive impairment detection in primary care.