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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

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The diagnostic value of cognitive assessment indicators for mild cognitive impairment (MCI)

Keyan Han1,2,3, Wei Liang1,2,3, Hao Geng1,2,3

  • 1Department of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, China.

Applied Neuropsychology. Adult
|February 5, 2024
PubMed
Abstract

Insights

Age, Montreal Cognitive Assessment (MoCA), and Clock Drawing Test (CDT) effectively diagnose mild cognitive impairment (MCI). AD-related blood markers and olfactory tests offer supplementary diagnostic value for MCI.

Area of Science:

  • Neurology
  • Geriatrics
  • Cognitive Science

Background:

  • Mild cognitive impairment (MCI) is a critical precursor to dementia, necessitating accurate early diagnostic methods.
  • Standard diagnostic tools for MCI vary in efficacy, prompting research into optimized assessment strategies.
  • Early detection of MCI is crucial for timely intervention and management of cognitive decline.

Purpose of the Study:

  • To evaluate and analyze the diagnostic accuracy of standard methods for mild cognitive impairment (MCI).
  • To identify key indicators for the early detection of MCI in elderly populations.
  • To assess the combined diagnostic utility of various cognitive, clinical, and biological markers for MCI.

Main Methods:

  • A prospective case-control study involving 240 participants (105 MCI, 135 healthy controls).
  • Assessment of diagnostic indicators including Montreal Cognitive Assessment (MoCA), Clock Drawing Test (CDT), Mini-Mental State Examination (MMSE), Pittsburgh Sleep Quality Index (PSQI), Memory Binding Inventory (MBI), Digit Span Test (DST), Hamilton Depression Rating Scale (HAMD), AD-related blood markers, and olfactory testing.
  • Receiver Operating Characteristic (ROC) curve analysis was employed to determine the diagnostic value of each indicator.

Main Results:

  • Significant baseline differences between MCI and healthy groups were observed in age, blood glucose, MMSE, CDT, MoCA, daily living abilities, AD-related blood indices, and olfactory tests (p < 0.05).
  • Logistic regression identified age, MoCA, and CDT as independent predictors of MCI (p < 0.05).
  • The combination of age, MoCA, and CDT achieved the highest diagnostic specificity (92.54%). AD-related blood and olfactory tests showed moderate diagnostic values (AUC: 0.7-0.8).

Conclusions:

  • Age, MoCA, and CDT are robust indicators for the early diagnosis of mild cognitive impairment.
  • AD-related blood indices and olfactory tests serve as valuable supplementary tools in MCI diagnosis.
  • The findings support a multi-indicator approach for accurate and specific MCI detection.
Keywords:
DementiaELISAMini-Mental State ExaminationMontreal Cognitive AssessmentSniffing testdiagnosismild cognitive impairment

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