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Updated: Jun 7, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Exploring heterogeneity in mild cognitive impairment
Zachary T Goodman1,2, Maria M Llabre2, Sonya Kaur1
1Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA.
Background:
Mild cognitive impairment (MCI) is a heterogeneous diagnostic entity, without a clear prognosis, often accompanied by psychiatric symptomatology and physical frailty.
Objective:
Understanding the heterogeneity within MCI is a critical step in improving the early detection of cognitive decline and developing effective interventions.
Methods:
Cross-sectional multivariate latent mixture analyses of data from patients evaluated between 2015 and 2019, who were routinely entered into a multidisciplinary database for research purposes. A sample of 538 community-dwelling older adults drawn from a large academic medical center, referred from within the Department of Neurology (63.7% Female, Mage = 67.8, SDage = 10.6). Participants completed comprehensive neuropsychological assessments, psychiatric symptom measures, and frailty evaluations.
Results:
Latent profile analyses supported five profiles of cognitive impairment: At-Risk, Pre-MCI, Amnestic MCI, Multiple Domain MCI, and Major Cognitive Impairment. The inclusion of concomitant psychiatric symptoms and frailty criteria revealed two additional profiles: Psychiatric/Frail, without cognitive impairments, and Multiple Cognitive Domains/Psychiatric/Frail. Critically, 55% of those classified as Healthy based on cognitive data alone were reclassified. Significant profile-wise differences emerged across auxiliary variables of brief cognitive screening, sociodemographics, and medical and psychosocial risk.
Conclusions:
Results highlight heterogeneity represented by neurologic patients referred for neuropsychological evaluation that include key physical and emotional symptoms known to increase the risk of cognitive decline. Findings are in alignment with more recent research suggesting that the traditional paradigm cognitive impairment may need to be expanded to improve diagnostic accuracy and to develop more tailored, precision-driven interventions.
Insights
Mild cognitive impairment (MCI) is highly varied. This study identified six distinct profiles, including those with psychiatric symptoms and frailty, improving diagnostic accuracy for cognitive decline.
Area of Science:
- Neurology
- Gerontology
- Psychiatry
Background:
- Mild cognitive impairment (MCI) is a complex diagnostic category with an unclear prognosis.
- MCI is frequently associated with psychiatric symptoms and physical frailty.
Purpose of the Study:
- To understand the heterogeneity within MCI to improve early detection of cognitive decline.
- To develop more effective interventions for cognitive impairment.
Main Methods:
- Cross-sectional multivariate latent mixture analyses of data from 538 older adults.
- Participants underwent comprehensive neuropsychological, psychiatric, and frailty evaluations.
- Data were collected between 2015 and 2019 from patients referred to a neurology department.
Main Results:
- Latent profile analyses identified five cognitive impairment profiles: At-Risk, Pre-MCI, Amnestic MCI, Multiple Domain MCI, and Major Cognitive Impairment.
- Two additional profiles were identified when including psychiatric symptoms and frailty: Psychiatric/Frail and Multiple Cognitive Domains/Psychiatric/Frail.
- 55% of individuals classified as healthy based on cognitive data alone were reclassified into different profiles.
Conclusions:
- Neurologic patients referred for evaluation exhibit significant heterogeneity, including physical and emotional symptoms that increase cognitive decline risk.
- Findings suggest expanding the traditional paradigm of cognitive impairment for improved diagnostic accuracy.
- Tailored, precision-driven interventions are needed for better patient outcomes.
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