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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.
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