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Brief Visuospatial Memory Test-Revised: normative data and clinical utility of learning indices in Parkinson's disease

F. Havlík, J. Mana, P. Dušek, R. Jech, E. Růžička, M. Kopeček, H. Georgi, O. Bezdicek

. 2020 ; 42 (10) : 1099-1110. [pub] 20201116

Jazyk angličtina Země Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc21019738

Introduction: The Brief Visual Memory Test-Revised (BVMT-R) is a frequently used visuospatial declarative memory test, but normative data in the Czech population are lacking. Moreover, the BVMT-R includes promising learning indexes that can be used to detect learning deficits in Parkinson's disease (PD). However, its clinical usefulness has not yet been thoroughly examined. Early detection of memory impairment in PD is essential for effective treatment. Therefore, this study aimed to provide BVMT-R Czech normative data for clinical use and to find the detection potential of the principal BVMT-R scores, including new learning indices, to capture the cognitive deficit in PD. Method: The BVMT-R were administered to a normative sample of 920 participants aged 17 to 95 years and to a clinical sample of 60 PD patients; 25 with mild cognitive impairment (PD-MCI) and 35 with normal cognition (PD-NC). In order to provide normative values, multiple regression analyses were employed, and to compare the clinical and control sample, Bayesian Hierarchical Linear Models were used. Results: The best model for regression-based norms showed to be with age + age2 + education + sex as predictors. From all learning indexes, L6 (sum of trials 1-3), followed by, L4 (sum of trials 1-3 multiplied by the difference between the highest and the lowest score) best differentiated between controls or PD-NC and PD-MCI. Conclusions: We provide regression-based normative values for BVMT-R that could be used in clinical settings and meta-analytic efforts. Furthermore, we revealed visuospatial learning and memory deficit in PD-MCI. We have also identified the most discriminative learning index adapted to BVMT-R.

Citace poskytuje Crossref.org

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