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Updated: Sep 5, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A Comparison of Operational Definitions for Mild Cognitive Impairment
Alexandra Polcher1,2, Steffen Wolfsgruber2, Oliver Peters3,4
1Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany.
Background:
Consideration of many tests from different cognitive domains in defining mild cognitive impairment (MCI) is clinical routine, but guidelines for a neuropsychological operationalization of MCI are lacking.
Objective:
Among different operational MCI criteria, to identify those which are best in predicting either conversion to dementia, or a biomarker profile indicative for Alzheimer's disease (AD).
Methods:
Memory clinic patients without dementia (N = 558; mean age = 66; up to 3 years of follow-up; n = 360 with baseline CSF biomarkers) were included in an observational study using most liberal criteria of cognitive impairment. Four operational definitions of MCI were retrospectively applied: 1) amnestic MCI (CERAD word list delayed recall), 2) CERAD total score, 3) comprehensive criteria and 4) base rate corrected CERAD. We compared their accuracy in predicting incident all-cause dementia or AD dementia within three years, or a concurrent CSF Aβ42/tau-ratio indicative of AD.
Results:
The four definitions overlapped considerably, classified 35-58% of the original sample as impaired and were associated with markedly increased PPVs regarding incident all-cause dementia (39-46% versus 26% of the original sample), AD dementia and AD biomarker positivity. The base rate corrected MCI definition had the highest prognostic accuracy.
Conclusion:
he operational criteria examined seem suitable to specify MCI in memory clinic settings, as they identify subjects at high risk of clinical progression. Depending on the neuropsychological battery in use, one or several of these criteria could help to calibrate the clinical judgment of test results, reduce false-positive decisions, and define risk-enriched groups for clinical trials.
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