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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Proposed diagnostic criteria for mild cognitive impairment in Down syndrome population

Susanna Esteba-Castillo^1,2, Javier Garcia-Alba³, Emili Rodríguez-Hildago¹

¹Specialized Service in Mental Health and Intellectual Disability, Institute of Health Assistance, Girona, Spain.

Journal of Applied Research in Intellectual Disabilities : JARID

|October 25, 2021

Abstract

Background:

Despite presenting higher risk of dementia, mild cognitive impairment (MCI) is not well defined in Down syndrome population.

Objective:

We aimed to describe cognitive and neuropsychological patterns associated with MCI in Down syndrome individuals.

Method:

Two groups of adults with Down syndrome (control and prodromal) were studied throughout 3 years. Two linear mixed models and a model including the variables that best predicted group membership were built.

Results:

Behavioural Regulation Index (BRI) (Behaviour Rating Inventory of Executive Function test) and the model composed of BRI, abstraction and delayed verbal memory were the variable and model best predicting group membership, respectively.

Conclusion:

Suggest a diagnosis of MCI when BRI is the earliest change perceived by caregivers and this is combined with low scores in abstract thinking, and when an amnesic pattern in delayed verbal memory is observed, but adaptive skills are preserved.

Keywords:

Down syndrome cognition dementia mild cognitive impairment prodromal stages

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