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Updated: Nov 1, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Intrusion Errors and Progression of Cognitive Deficits in Older Adults with Mild Cognitive Impairment and PreMCI
Elizabeth A Crocco1, Rosie Curiel Cid1, Marcela Kitaigorodsky1
1Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA.
Introduction:
Among persons with amnestic mild cognitive impairment (aMCI), intrusion errors on subscales that measure proactive semantic interference (PSI) may be among the earliest behavioral markers of elevated Alzheimer's disease brain pathology. While there has been considerable cross-sectional work in the area, it is presently unknown whether semantic intrusion errors are predictive of progression of cognitive impairment in aMCI or PreMCI (not cognitively normal but not meeting full criteria for MCI).
Methods:
This study examined the extent to which the percentage of semantic intrusion errors (PIE) based on total responses on a novel cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L), could predict clinical/cognitive outcomes over an average 26-month period in older adults initially diagnosed with aMCI, PreMCI, and normal cognition.
Results:
On the LASSI-L subscale sensitive to PSI, a PIE cut point of 44% intrusion errors distinguished between those at-risk individuals with PreMCI who progressed to MCI over time compared to individuals with PreMCI who reverted to normal on longitudinal follow-up. Importantly, PIE was able to accurately predict 83.3% of aMCI individuals who later progressed to dementia.
Discussion:
These preliminary findings indicate that PIE on LASSI-L subscales that measure PSI may be a useful predictor of clinical progression overtime in at-risk older adults.
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