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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Medical burden and cognition in older patients in primary care: selective deficits in attention
Kevin Duff1, James W Mold, Michelle M Roberts
1University of Iowa, Department of Psychiatry, MEB 1-308, Iowa City, IA 52242-1000, USA. kevin-duff@uiowa.edu
Abstract:
Cognitive deficits have been associated with several chronic medical conditions, but the additive effects of multiple conditions on cognition have less studied. Six hundred ninety-two community dwelling older adults were enrolled through their primary care physicians and evaluated for medical burden and cognition. Medical burden was assessed by self-report questionnaire. Cognition was assessed with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). After adjusting for age and gender, there was a strong inverse relationship between medical burden and cognition, with individuals with more medical comorbidities performing worse on cognitive measures. Attention was selectively poor, especially for a speeded, divided attention task, in patients with five or more comorbid medical conditions. These findings could have clinical implications, as poorer attention, especially in patients with multiple medical problems, could lead to poorer medical compliance and worse outcomes.
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