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Updated: Jul 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mild cognitive impairment in the older population: Who is missed and does it matter?
Blossom C M Stephan1, Carol Brayne, Ian G McKeith
1Department of Public Health and Primary Care, Institute of Public Health, Cambridge UK. bcms2@cam.ac.uk <bcms2@cam.ac.uk>
Objectives:
Classifications of mild cognitive impairment (MCI) vary in the precision of the defining criteria. Their value in clinical settings is different from population settings. This difference depending on setting is to be expected, but must be well understood if population screening for dementia and pre-dementia states is to be considered. Of importance is the impact of missed diagnosis. The magnitude of missed 'at-risk' cases in the application of different MCI criteria in the population is unknown.
Methods:
Data were from the Medical Research Council Cognitive Function and Ageing Study, a large population based study of older aged individuals in the UK. Prevalence and two-year progression to dementia in individuals whose impairment failed to fulfil published criteria for MCI was evaluated.
Results:
Prevalence estimates of individuals not classified from current MCI definitions were extremely variable (range 2.5-41.0%). Rates of progression to dementia in these non-classified groups were also very variable (3.7-30.0%), reflecting heterogeneity in MCI classification requirements.
Conclusions:
Narrow definitions of MCI developed for clinical settings when applied in the population result in a large proportion of individuals who progress to dementia being excluded from MCI classifications. More broadly defined criteria would be better for selection of individuals at risk of dementia in population settings, but at the possibility of high false positive rates. While exclusion may be a good thing in the population since most people are presumably 'normal', over-inclusion is more likely to be harmful. Further work needs to investigate the best classification system for application in the population.
Insights
Different definitions for mild cognitive impairment (MCI) significantly impact dementia risk identification in the general population. Narrow criteria miss many at-risk individuals, while broad criteria may over-identify cases, highlighting the need for optimized population screening tools.
Area of Science:
- Gerontology
- Neurology
- Public Health
Background:
- Mild cognitive impairment (MCI) classification criteria vary, impacting their utility in different settings.
- Understanding the implications of these varying criteria is crucial for effective population screening of dementia and pre-dementia states.
- The extent of missed diagnoses of at-risk individuals using different MCI criteria in population studies remains unclear.
Purpose of the Study:
- To evaluate the prevalence and two-year progression to dementia in individuals not meeting published MCI criteria.
- To assess the impact of different MCI classification systems on identifying at-risk populations for dementia.
Main Methods:
- Utilized data from the Medical Research Council Cognitive Function and Ageing Study, a large UK population-based cohort.
- Analyzed prevalence and progression to dementia in individuals whose cognitive impairment did not meet established MCI definitions.
Main Results:
- Prevalence estimates for individuals not classified under current MCI definitions showed extreme variability (2.5-41.0%).
- Rates of progression to dementia in these non-classified groups were also highly variable (3.7-30.0%), indicating heterogeneity in MCI classification requirements.
Conclusions:
- Narrow MCI definitions used in clinical settings exclude a substantial proportion of individuals who later develop dementia when applied to the general population.
- Broader criteria may improve the selection of at-risk individuals in population settings but risk higher false positive rates.
- Further research is needed to determine the optimal MCI classification system for population-based dementia risk assessment.
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