Universität zu Köln

Sulzer, Patricia, Becker, Sara, Maetzlerz, Walter, Kalbe, Elke, van Nueten, Luc, Timmers, Maarten, Machetanz, Gerrit, Streffer, Johannes, Salvadore, Giacomo, Scholz, Erich, Tkaczynska, Zuzanna, Brockmann, Kathrin, Berg, Daniela and Liepelt-Scarfone, Inga (2018). Validation of a novel Montreal Cognitive Assessment scoring algorithm in non-demented Parkinson's disease patients. J. Neurol., 265 (9). S. 1976 - 1985. HEIDELBERG: SPRINGER HEIDELBERG. ISSN 1432-1459

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Abstract

Introduction The early diagnosis of mild cognitive impairment (PD-MCI) in Parkinson's disease (PD) is essential as it increases the future risk for PD dementia (PDD). Recently, a novel weighting algorithm for the Montreal Cognitive Assessment (MoCA) subtests has been reported, to best discriminate between those with and without cognitive impairment in PD. The aim of our study was to validate this scoring algorithm in a large sample of non-demented PD patients, hypothesizing that the weighted MoCA would have a higher diagnostic accuracy for PD-MCI than the original MoCA. Methods In 202 non-demented PD patients, we evaluated cognitive status, clinical and demographic data, as well as the MoCA with a weighted and unweighted score. Receiver operating characteristic (ROC) curve analysis was used to evaluate discriminative ability of the MoCA. Group comparisons and ROC analysis were performed for PD-MCI classifications with a cut-off <= 1, 1.5, and 2 standard deviation (SD) below appropriate norms. Results PD-MCI patients scored lower on the weighted than the original MoCA version (p < 0.001) compared to PD patients with normal cognitive function. Areas under the curve only differed significantly for the 2 SD cut-off, leading to an increased sensitivity of the weighted MoCA score (72.9% vs. 70.5%) and specificity compared to the original version (79.0% vs. 65.4%). Conclusions Our results indicate better discriminant power for the weighted MoCA compared to the original for more advanced stages of PD-MCI (2 SD cut-off). Future studies are needed to evaluate the predictive value of the weighted MoCA for PDD.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Sulzer, Patricia UNSPECIFIED UNSPECIFIED UNSPECIFIED Becker, Sara UNSPECIFIED UNSPECIFIED UNSPECIFIED Maetzlerz, Walter UNSPECIFIED UNSPECIFIED UNSPECIFIED Kalbe, Elke UNSPECIFIED UNSPECIFIED UNSPECIFIED van Nueten, Luc UNSPECIFIED UNSPECIFIED UNSPECIFIED Timmers, Maarten UNSPECIFIED UNSPECIFIED UNSPECIFIED Machetanz, Gerrit UNSPECIFIED UNSPECIFIED UNSPECIFIED Streffer, Johannes UNSPECIFIED UNSPECIFIED UNSPECIFIED Salvadore, Giacomo UNSPECIFIED UNSPECIFIED UNSPECIFIED Scholz, Erich UNSPECIFIED UNSPECIFIED UNSPECIFIED Tkaczynska, Zuzanna UNSPECIFIED UNSPECIFIED UNSPECIFIED Brockmann, Kathrin UNSPECIFIED UNSPECIFIED UNSPECIFIED Berg, Daniela UNSPECIFIED UNSPECIFIED UNSPECIFIED Liepelt-Scarfone, Inga UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-175276
DOI:	10.1007/s00415-018-8942-4
Journal or Publication Title:	J. Neurol.
Volume:	265
Number:	9
Page Range:	S. 1976 - 1985
Date:	2018
Publisher:	SPRINGER HEIDELBERG
Place of Publication:	HEIDELBERG
ISSN:	1432-1459
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language SOCIETY TASK-FORCE; IMPAIRMENT; DEMENTIA; DEPRESSION; COHORT; MOCA; ACCURACY; DEFICITS Multiple languages Clinical Neurology Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/17527