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Title:Napovedni dejavniki razvoja nemotoričnih fenotipov parkinsonove bolezni
Authors:ID Petrijan, Timotej (Author)
ID Menih, Marija (Mentor) More about this mentor... New window
Files:.pdf DOK_Petrijan_Timotej_2024.pdf (6,11 MB)
MD5: A026474DBC428801588156F383B6BD31
 
Language:Slovenian
Work type:Dissertation
Typology:2.08 - Doctoral Dissertation
Organization:MF - Faculty of Medicine
Abstract:Cilj te raziskave je bil preučiti dejavnike tveganja, prodromalne simptome, nemotorične simptome (NMS) in motorične simptome (MS) kot napovedne dejavnike za različne nemotorične fenotipe Parkinsonove bolezni (PB). Skupno 168 bolnikov je opravilo celovite preglede NMS in MS. Bolniki so bili na podlagi novo zasnovanih vključitvenih kriterijev razvrščeni v skupine treh NMS fenotipov (kortikalni, limbični in možgansko-debelni). Identificirali smo 38 (22,6%) bolnikov s kortikalnim fenotipom, 48 (28,6%) z limbičnim in 82 (48,8%) bolnikov z možgansko-debelnim fenotipom. Nadalje je bilo izvedeno podatkovno vodeno združevanje kot alternativni pristop klasifikacije, ki temelji na metodah strojnega učenja. Primerjali smo oba klasifikacijska pristopa za doslednost. Pearsonov hi-kvadrat test neodvisnosti je pokazal, da sta bila oba pristopa povezana z veliko velikostjo učinka (ꭓ2(8) = 175.001, p < 0.001, Cramerjev V = 0.722). Demografski in klinični profili so se pomembno razlikovali med NMS fenotipi in nam lahko predstavljajo diagnostične napovedne dejavnike za razvoj posameznega fenotipa. Novo zasnovani kriteriji imajo potencial kot poenostavljeno orodje za prihodnje klinične raziskave NMS fenotipov PB.
Keywords:Parkinsonova bolezen, nemotorični fenotipi, a priori klasifikacija, analiza grozdov, napovedni dejavniki
Place of publishing:Maribor
Publisher:[T. Petrijan]
Year of publishing:2024
PID:20.500.12556/DKUM-87721 New window
UDC:616.858-071.2(043.3)
COBISS.SI-ID:213656835 New window
Publication date in DKUM:22.11.2024
Views:0
Downloads:12
Metadata:XML DC-XML DC-RDF
Categories:MF
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:26.03.2024

Secondary language

Language:English
Title:Predictive factors for the development of non-motor phenotypes in Parkinson's disease
Abstract:The aim of this study was to examine the risk factors, prodromal symptoms, non-motor symptoms (NMS), and motor symptoms (MS) as predictive factors for different Parkinson’s disease (PD) non-motor phenotypes, classified using newly established criteria. A total of 168 patients with PD underwent comprehensive NMS and MS examinations. Patients were classified into groups of three NMS phenotypes (cortical, limbic, and brainstem) based on the newly designed inclusion criteria. We identified 38 (22.6%) patients with the cortical subtype, 48 (28.6%) with the limbic, and 82 (48.8%) patients with the brainstem NMS PD subtype. Further, data-driven clustering was performed as an alternative, statistical learning-based classification approach. The two classification approaches were compared for consistency. Pearson chi-square test of independence revealed that a priori classification and cluster membership were significantly related to one another with a large effect size (ꭓ2(8) = 175.001, p < 0.001, Cramer’s V = 0.722). The demographic and clinical profiles significantly differed between NMS phenotypes and can be used as a diagnostic predictive factors for different NMS phenotypes. The newly established criteria have potential as a simplified tool for future clinical research of NMS phenotypes of Parkinson’s disease.
Keywords:Parkinson’s disease, non-motor phenotypes, a priori classification, cluster analysis, predictive factors


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