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Updated: Nov 30, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Application of neuropsychological criteria to classify mild cognitive impairment in the active study
Kelsey R Thomas1, Sarah E Cook2, Mark W Bondi1
1Veterans Affairs San Diego Healthcare System.
Abstract:
Comprehensive neuropsychological criteria (NP criteria) for mild cognitive impairment (MCI) has reduced diagnostic errors and better predicted progression to dementia than conventional MCI criteria that rely on a single impaired score and/or subjective report. This study aimed to implement an actuarial approach to classifying MCI in the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study. ACTIVE study participants (N = 2,755) were classified as cognitively normal (CN) or as having MCI using NP criteria. Estimated proportion of MCI participants and reversion rates were examined as well as baseline characteristics by MCI subtype. Mixed effect models examined associations of MCI subtype with 10-year trajectories of self-reported independence and difficulty performing instrumental activities of daily living (IADLs). The proportion of MCI participants was estimated to be 18.8%. Of those with MCI at baseline, 19.2% reverted to CN status for all subsequent visits. At baseline, the multidomain-amnestic MCI group generally had the greatest breadth and depth of cognitive impairment and reported the most IADL difficulty. Longitudinally, MCI participants showed faster IADL decline than CN participants (multidomain-amnestic MCI > single domain-amnestic MCI > nonamnestic MCI). NP criteria identified a proportion of MCI and reversion rate within ACTIVE that is consistent with prior studies involving community-dwelling samples. The pattern of everyday functioning change suggests that being classified as MCI, particularly amnestic MCI, is predictive of future loss of independence. Future work will apply these classifications in ACTIVE to better understand the relationships between MCI and health, social, and cognitive intervention-related factors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Insights
Comprehensive neuropsychological (NP) criteria accurately identify mild cognitive impairment (MCI) and predict functional decline. This study applied NP criteria in the ACTIVE trial, finding MCI predicts future loss of independence, especially amnestic MCI.
Area of Science:
- Gerontology
- Neuropsychology
- Cognitive Science
Background:
- Mild cognitive impairment (MCI) diagnosis impacts dementia risk prediction.
- Conventional MCI criteria have limitations in accuracy and predictive power.
- Neuropsychological (NP) criteria offer a more comprehensive approach to MCI classification.
Purpose of the Study:
- To implement and evaluate actuarial NP criteria for MCI classification within the ACTIVE study.
- To examine MCI prevalence, reversion rates, and baseline characteristics by MCI subtype.
- To assess the longitudinal association between MCI subtypes and functional independence.
Main Methods:
- Classified 2,755 ACTIVE study participants as cognitively normal (CN) or MCI using NP criteria.
- Estimated MCI prevalence and reversion rates.
- Utilized mixed-effects models to analyze MCI subtype associations with 10-year trajectories of self-reported independence and instrumental activities of daily living (IADLs).
Main Results:
- An estimated 18.8% of participants met MCI criteria, with a 19.2% reversion rate to CN status.
- The multidomain-amnestic MCI group exhibited the most significant cognitive impairment and IADL difficulties at baseline.
- MCI classification predicted faster IADL decline over 10 years, with multidomain-amnestic MCI showing the steepest decline.
Conclusions:
- NP criteria yield MCI prevalence and reversion rates consistent with community-dwelling samples.
- MCI classification, particularly amnestic MCI, is predictive of future declines in functional independence.
- Further research will explore the relationship between MCI subtypes and intervention-related factors in the ACTIVE cohort.
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