Evaluation of a multimodal diagnostic algorithm for prediction of cognitive impairment in elderly patients with dizziness

Background The current diagnostic workup for chronic dizziness in elderly patients often neglects neuropsychological assessment, thus missing a relevant proportion of patients, who perceive dizziness as a subjective chief complaint of a concomitant cognitive impairment. This study aimed to establish...

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Bibliographic Details
Published in:Journal of neurology 2024-07, Vol.271 (7), p.4485-4494
Main Authors: Felfela, K., Jooshani, N., Möhwald, K., Huppert, D., Becker-Bense, S., Schöberl, F., Schniepp, R., Filippopulos, F., Dieterich, M., Wuehr, M., Zwergal, A.
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Algorithms
Atrophy
Cognitive ability
Cognitive Dysfunction - diagnosis
Cognitive Dysfunction - etiology
Dizziness - diagnosis
Female
Gait
Humans
Male
Medicine
Medicine & Public Health
Neuroimaging
Neurology
Neuropsychological Tests
Neuroradiology
Neurosciences
Original Communication
Prediction models
Regression analysis
Substantia alba
Vestibular system
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Summary:Background The current diagnostic workup for chronic dizziness in elderly patients often neglects neuropsychological assessment, thus missing a relevant proportion of patients, who perceive dizziness as a subjective chief complaint of a concomitant cognitive impairment. This study aimed to establish risk prediction models for cognitive impairment in chronic dizzy patients based on data sources routinely collected in a dizziness center. Methods One hundred patients (age: 74.7 ± 7.1 years, 41.0% women) with chronic dizziness were prospectively characterized by (1) neuro-otological testing, (2) quantitative gait assessment, (3) graduation of focal brain atrophy and white matter lesion load, and (4) cognitive screening (MoCA). A linear regression model was trained to predict patients’ total MoCA score based on 16 clinical features derived from demographics, vestibular testing, gait analysis, and imaging scales. Additionally, we trained a binary logistic regression model on the same data sources to identify those patients with a cognitive impairment (i.e., MoCA 
ISSN:0340-5354
1432-1459
1432-1459
DOI:10.1007/s00415-024-12403-3