20.500.12556/DKUM-87721 Napovedni dejavniki razvoja nemotoričnih fenotipov parkinsonove bolezni Predictive factors for the development of non-motor phenotypes in Parkinson's disease 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. 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. Parkinsonova bolezen nemotorični fenotipi a priori klasifikacija analiza grozdov napovedni dejavniki Parkinson’s disease non-motor phenotypes a priori classification cluster analysis predictive factors true true false [T. Petrijan] Slovenski jezik Angleški jezik Doktorska disertacija 2024-03-26 08:41:49 2024-11-22 09:13:16 2024-11-23 03:21:52 0000-00-00 00:00:00 2024 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616.858-071.2(043.3) 213656835 DOK_Petrijan_Timotej_2024.pdf DOK_Petrijan_Timotej_2024.pdf 1 A026474DBC428801588156F383B6BD31 fa764962a59a1ad93c91ee5252f8a326d96a77138e0b9d6020dcf855b534e0d9 ef7947e9-8a4b-11ef-a9b1-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=186493 Medicinska fakulteta 0 0 0