Evžen Růžička

Head of department

General Faculty Hospital, 1st Medical Faculty of Charles University
Country flagCzech Republic

Research Interests

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About

Evžen Růžička is the Head of the Department at Charles University, 1st Medical Faculty and General University Hospital in the Czech Republic. His research primarily focuses on Parkinson's disease and essential tremor, exploring topics such as metallomics, pupillometry, and cognitive decline. Recent publications include studies on the alterations in brain morphology associated with these conditions and the predictive value of spoken language changes in early diagnosis.

Articles (10)

Year:

2025

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2025

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2025

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2025

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2025

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2024

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2024

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2024

Year:

2024

Objective This study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). Methods Patients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story‐tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow‐up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow‐up period of 5 years. Results Of 180 patients free of parkinsonism or dementia, 156 provided follow‐up information. After a mean follow‐up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia‐first (n = 16) and parkinsonism‐first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism‐first with normal cognition converters (n = 17). Interpretation Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530–543

Year:

2023

Collaborators (6)

Jan Rusz

-

CZECH REPUBLIC
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Karel Šonka

Professor, senior researcher

Charles University, First Faculty of Medicine

CZECH REPUBLIC
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Radim Krupicka

Czech Technical University in Prague

CZECH REPUBLIC
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Jan Kuta

Masaryk University

CZECH REPUBLIC
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Jan Švihlík

Associate Professor

University of Chemistry and Technology

CZECH REPUBLIC
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Ondrej Slanar

Charles University, First Faculty of Medicine

CZECH REPUBLIC
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