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Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes

Prevalence rates for mild cognitive impairment in Parkinson’s disease (PD-MCI) remain variable, obscuring the diagnosis’ predictive utility of greater dementia risk. A primary factor of this variability is inconsistent operationalization of normative cutoffs for cognitive impairment. We aimed to det...

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Autores principales: Kenney, Lauren E., Ratajska, Adrianna M., Lopez, Francesca V., Price, Catherine C., Armstrong, Melissa J., Bowers, Dawn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773733/
https://www.ncbi.nlm.nih.gov/pubmed/35053799
http://dx.doi.org/10.3390/brainsci12010054
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author Kenney, Lauren E.
Ratajska, Adrianna M.
Lopez, Francesca V.
Price, Catherine C.
Armstrong, Melissa J.
Bowers, Dawn
author_facet Kenney, Lauren E.
Ratajska, Adrianna M.
Lopez, Francesca V.
Price, Catherine C.
Armstrong, Melissa J.
Bowers, Dawn
author_sort Kenney, Lauren E.
collection PubMed
description Prevalence rates for mild cognitive impairment in Parkinson’s disease (PD-MCI) remain variable, obscuring the diagnosis’ predictive utility of greater dementia risk. A primary factor of this variability is inconsistent operationalization of normative cutoffs for cognitive impairment. We aimed to determine which cutoff was optimal for classifying individuals as PD-MCI by comparing classifications against data-driven PD cognitive phenotypes. Participants with idiopathic PD (n = 494; mean age 64.7 ± 9) completed comprehensive neuropsychological testing. Cluster analyses (K-means, Hierarchical) identified cognitive phenotypes using domain-specific composites. PD-MCI criteria were assessed using separate cutoffs (−1, −1.5, −2 SD) on ≥2 tests in a domain. Cutoffs were compared using PD-MCI prevalence rates, MCI subtype frequencies (single/multi-domain, executive function (EF)/non-EF impairment), and validity against the cluster-derived cognitive phenotypes (using chi-square tests/binary logistic regressions). Cluster analyses resulted in similar three-cluster solutions: Cognitively Average (n = 154), Low EF (n = 227), and Prominent EF/Memory Impairment (n = 113). The −1.5 SD cutoff produced the best model of cluster membership (PD-MCI classification accuracy = 87.9%) and resulted in the best alignment between PD-MCI classification and the empirical cognitive profile containing impairments associated with greater dementia risk. Similar to previous Alzheimer’s work, these findings highlight the utility of comparing empirical and actuarial approaches to establish concurrent validity of cognitive impairment in PD.
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spelling pubmed-87737332022-01-21 Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes Kenney, Lauren E. Ratajska, Adrianna M. Lopez, Francesca V. Price, Catherine C. Armstrong, Melissa J. Bowers, Dawn Brain Sci Article Prevalence rates for mild cognitive impairment in Parkinson’s disease (PD-MCI) remain variable, obscuring the diagnosis’ predictive utility of greater dementia risk. A primary factor of this variability is inconsistent operationalization of normative cutoffs for cognitive impairment. We aimed to determine which cutoff was optimal for classifying individuals as PD-MCI by comparing classifications against data-driven PD cognitive phenotypes. Participants with idiopathic PD (n = 494; mean age 64.7 ± 9) completed comprehensive neuropsychological testing. Cluster analyses (K-means, Hierarchical) identified cognitive phenotypes using domain-specific composites. PD-MCI criteria were assessed using separate cutoffs (−1, −1.5, −2 SD) on ≥2 tests in a domain. Cutoffs were compared using PD-MCI prevalence rates, MCI subtype frequencies (single/multi-domain, executive function (EF)/non-EF impairment), and validity against the cluster-derived cognitive phenotypes (using chi-square tests/binary logistic regressions). Cluster analyses resulted in similar three-cluster solutions: Cognitively Average (n = 154), Low EF (n = 227), and Prominent EF/Memory Impairment (n = 113). The −1.5 SD cutoff produced the best model of cluster membership (PD-MCI classification accuracy = 87.9%) and resulted in the best alignment between PD-MCI classification and the empirical cognitive profile containing impairments associated with greater dementia risk. Similar to previous Alzheimer’s work, these findings highlight the utility of comparing empirical and actuarial approaches to establish concurrent validity of cognitive impairment in PD. MDPI 2021-12-30 /pmc/articles/PMC8773733/ /pubmed/35053799 http://dx.doi.org/10.3390/brainsci12010054 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kenney, Lauren E.
Ratajska, Adrianna M.
Lopez, Francesca V.
Price, Catherine C.
Armstrong, Melissa J.
Bowers, Dawn
Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes
title Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes
title_full Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes
title_fullStr Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes
title_full_unstemmed Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes
title_short Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes
title_sort mapping actuarial criteria for parkinson’s disease-mild cognitive impairment onto data-driven cognitive phenotypes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773733/
https://www.ncbi.nlm.nih.gov/pubmed/35053799
http://dx.doi.org/10.3390/brainsci12010054
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