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Updated: Aug 4, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Frailty prevalence and efficient screening in primary care-based memory clinics.
Linda Lee1,2,3, Aaron Jones4, Tejal Patel5
1Centre for Family Medicine Family Health Team, 10B Victoria Street South, Kitchener, ON N2G 1C5, Canada.
Frailty prevalence in memory clinic patients varies significantly by screening tool. Physical performance measures may be more efficient for identifying frailty in this population.
Area of Science:
- Gerontology
- Primary Care Medicine
- Cognitive Neurology
Background:
- Prevalence of frailty among patients with memory concerns in primary care settings is not well-established.
- Memory clinics serve a population at risk for both cognitive decline and physical frailty.
Purpose of the Study:
- To determine the prevalence of frailty in patients attending a primary care memory clinic.
- To compare frailty prevalence rates based on different screening tools.
Main Methods:
- Retrospective review of 258 patients' medical records from a primary care memory clinic.
- Frailty assessed using Fried frailty criteria (physical measures) and Clinical Frailty Scale (CFS) (functional status).
- Weighted kappa statistics used to compare agreement between Fried criteria and CFS.
Main Results:
- Frailty prevalence was 16% by Fried criteria and 48% by CFS.
- Fair agreement between Fried criteria and CFS for CFS 5+ (kappa = 0.22).
- Moderate agreement between Fried criteria and CFS for CFS 6+ (kappa = 0.47).
- Dual-trait measures (grip strength and gait speed) validated as proxies for Fried frailty.
Conclusions:
- Frailty prevalence estimates differ substantially depending on the screening measure used.
- Physical performance-based frailty screening may be more efficient in primary care memory clinic patients.
- Measure selection for frailty screening should align with study objectives and clinical context.
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