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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Multiple chronic conditions: Implications for cognition - Findings from the Wisconsin Registry for Alzheimer's
Lisa C Bratzke1, Beverly A Carlson2, Chooza Moon3
1University of Wisconsin - Madison, School of Nursing, United States of America.
Introduction:
Several chronic illnesses have demonstrated relationships to cognitive decline in the context of aging. However, researchers have largely ignored the effects of multi-morbidity in the context of Alzheimer's disease and related dementias (ADRD) risk. The purpose of this study is to examine the relationship between multiple chronic conditions (MCC) and cognitive decline.
Methods:
Latent class analysis (LCA) was completed to identify different subgroups of the 1285 participants from the Wisconsin Registry for Alzheimer's Prevention who were recognized based on their self-reported chronic illnesses. Differences between variables of interest (i.e., biomarkers and depressive symptom scores) and each of the individual classes were then explored. Chi-square tests were used to examine the association between MCC and cognitive status.
Results:
LCA revealed a four-class model best fit solution. Participants in the sleep class had the highest incidence of new onset cognitive decline.
Discussion:
Findings offer evidence of an association between specific MCC groups and the development of cognitive decline. Nurses should monitor and screen for cognitive decline in the presence of MCC in order to better target self-management interventions.
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