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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Using Varying Diagnostic Criteria to Examine Mild Cognitive Impairment Prevalence and Predict Dementia Incidence in a
Kayela Robertson1, Eric B Larson2, Paul K Crane3
1Geriatric Research, Education, and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA.
Abstract:
Lack of a unitary operational definition of mild cognitive impairment (MCI) has resulted in mixed prevalence rates and unclear predictive validity regarding conversion to dementia and likelihood of reversion. We examined 1,721 nondemented participants aged 65 and older from the Adult Changes in Thought (ACT) community-based cohort. Participants were followed longitudinally through biennial visits (average years assessed = 5.38). Categorization of MCI was based on: 1) deviation of neuropsychological test scores from a benchmark based on either standard or individualized expectations of a participant's mean premorbid cognitive ability, and 2) cutoff for impairment (1.0 versus 1.5 standard deviations [sd] below benchmark). MCI prevalence ranged from 56-92%; using individualized benchmarks and less stringent cutoffs produced higher rates. During follow-up, 17% of the cohort developed dementia. Examination of sensitivity, specificity, and predictive validity revealed that the criterion of 1.5 sd from the standardized benchmark was optimal, but still had limited predictive validity. Participants meeting this criterion at their first visit were three times more likely to develop dementia and this increased to seven times if participants had this diagnosis at the second timepoint as well. Those who did not have an MCI diagnosis at their first visit, but did at their second, had a significant increase of risk (but to a lesser extent than those diagnosed at both visits), while those who had an MCI diagnosis at their first visit, but not their second, did not have a significantly increased risk. These results highlight how assessing MCI stability greatly improves prediction of risk.
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