Evaluation of Differential Diagnostics Potential of Uniform Data Set 2 Neuropsychology Battery Using Alzheimer’s Disease Biomarkers

Author:

Čihák Martin12ORCID,Horáková Hana345ORCID,Vyhnálek Martin12ORCID,Veverová Kateřina3467ORCID,Matušková Veronika34ORCID,Laczó Jan34ORCID,Hort Jakub34ORCID,Nikolai Tomáš34567ORCID

Affiliation:

1. Department of Neurology , First Faculty of Medicine, , 121 08 Prague , Czech Republic

2. Charles University , First Faculty of Medicine, , 121 08 Prague , Czech Republic

3. Department of Neurology , Second Faculty of Medicine and Motol University Hospital, , 150 06 Prague , Czech Republic

4. Charles University , Second Faculty of Medicine and Motol University Hospital, , 150 06 Prague , Czech Republic

5. Department of Clinical Psychology, Motol University Hospital , 150 06 Prague , Czech Republic

6. Department of Psychology , Faculty of Arts, , 116 38 Prague , Czech Republic

7. Charles University , Faculty of Arts, , 116 38 Prague , Czech Republic

Abstract

Abstract Objective This study aims to evaluate the efficacy of the Uniform Data Set (UDS) 2 battery in distinguishing between individuals with mild cognitive impairment (MCI) attributable to Alzheimer’s disease (MCI-AD) and those with MCI due to other causes (MCI-nonAD), based on contemporary AT(N) biomarker criteria. Despite the implementation of the novel UDS 3 battery, the UDS 2 battery is still used in several non-English-speaking countries. Methods We employed a cross-sectional design. A total of 113 Czech participants with MCI underwent a comprehensive diagnostic assessment, including cerebrospinal fluid biomarker evaluation, resulting in two groups: 45 individuals with prodromal AD (A+T+) and 68 participants with non-Alzheimer’s pathological changes or normal AD biomarkers (A−). Multivariable logistic regression analyses were employed with neuropsychological test scores and demographic variables as predictors and AD status as an outcome. Model 1 included UDS 2 scores that differed between AD and non-AD groups (Logical Memory delayed recall), Model 2 employed also Letter Fluency and Rey’s Auditory Verbal Learning Test (RAVLT). The two models were compared using area under the receiver operating characteristic curves. We also created separate logistic regression models for each of the UDS 2 scores. Results Worse performance in delayed recall of Logical Memory significantly predicted the presence of positive AD biomarkers. In addition, the inclusion of Letter Fluency RAVLT into the model significantly enhanced its discriminative capacity. Conclusion Our findings demonstrate that using Letter Fluency and RAVLT alongside the UDS 2 battery can enhance its potential for differential diagnostics.

Funder

Czech Science Foundation

Publisher

Oxford University Press (OUP)

Reference53 articles.

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