Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering
| العنوان: | Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering |
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| المؤلفون: | Jari K. Gool, Zhongxing Zhang, Martijn S.S.L. Oei, Stephanie Mathias, Yves Dauvilliers, Geert Mayer, Giuseppe Plazzi, Rafael del Rio-Villegas, Joan Santamaria Cano, Karel Šonka, Markku Partinen, Sebastiaan Overeem, Rosa Peraita-Adrados, Raphael Heinzer, Antonio Martins da Silva, Birgit Högl, Aleksandra Wierzbicka, Anna Heidbreder, Eva Feketeova, Mauro Manconi, Jitka Bušková, Francesca Canellas, Claudio L. Bassetti, Lucie Barateau, Fabio Pizza, Markus H. Schmidt, Rolf Fronczek, Ramin Khatami, Gert Jan Lammers |
| المساهمون: | Anatomy and neurosciences, Gool J.K., Zhang Z., Oei M.S.S.L., Mathias S., Dauvilliers Y., Mayer G., Plazzi G., Del Rio-Villegas R., Cano J.S., Sonka K., Partinen M., Overeem S., Peraita-Adrados R., Heinzer R., Martins Da Silva A., Hogl B., Wierzbicka A., Heidbreder A., Feketeova E., Manconi M., Buskova J., Canellas F., Bassetti C.L., Barateau L., Pizza F., Schmidt M.H., Fronczek R., Khatami R., Lammers G.J., Signal Processing Systems |
| المصدر: | Gool, Jari K; Zhang, Zhongxing; Oei, Martijn Ssl; Mathias, Stephanie; Dauvilliers, Yves; Mayer, Geert; Plazzi, Giuseppe; Del Rio-Villegas, Rafael; Cano, Joan Santamaria; Šonka, Karel; Partinen, Markku; Overeem, Sebastiaan; Peraita-Adrados, Rosa; Heinzer, Raphael; Martins da Silva, Antonio; Högl, Birgit; Wierzbicka, Aleksandra; Heidbreder, Anna; Feketeova, Eva; Manconi, Mauro; ... (2022). Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering. Neurology, 98(23), e2387-e2400. American Academy of Neurology 10.1212/WNL.0000000000200519 <http://dx.doi.org/10.1212/WNL.0000000000200519> Gool, J K, Zhang, Z, Oei, M S S L, Mathias, S, Dauvilliers, Y, Mayer, G, Plazzi, G, Del Rio-Villegas, R, Cano, J S, Šonka, K, Partinen, M, Overeem, S, Peraita-Adrados, R, Heinzer, R, Martins Da Silva, A, Högl, B, Wierzbicka, A, Heidbreder, A, Feketeova, E, Manconi, M, Bušková, J, Canellas, F, Bassetti, C L, Barateau, L, Pizza, F, Schmidt, M H, Fronczek, R, Khatami, R & Lammers, G J 2022, ' Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering ', Neurology, vol. 98, no. 23, pp. E2387-E2400 . https://doi.org/10.1212/WNL.0000000000200519 Neurology, 98(23), E2387-E2400. Lippincott Williams and Wilkins Neurology, 98(23), e2387-e2400. Ovid Technologies (Wolters Kluwer Health) Neurology, 98(23), e2387-e2400. Lippincott Williams and Wilkins Ltd. Neurology, vol. 98, no. 23, pp. e2387-e2400 Neurology, 98(23), E2387-E2400. LIPPINCOTT WILLIAMS & WILKINS |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Cluster Analysi, Adolescent, Cluster Analysis, Humans, Cataplexy, Disorders of Excessive Somnolence, Idiopathic Hypersomnia, Narcolepsy, 610 Medicine & health, Cataplexy/diagnosis, Disorders of Excessive Somnolence/diagnosis, Disorders of Excessive Somnolence/epidemiology, Idiopathic Hypersomnia/diagnosis, Narcolepsy/diagnosis, Narcolepsy/drug therapy, Neurology (clinical), Human |
| الوصف: | Background and ObjectivesRecent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.MethodsWe used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups.ResultsWe included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.DiscussionUsing a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features. |
| وصف الملف: | application/pdf; ELETTRONICO |
| اللغة: | English |
| تدمد: | 0028-3878 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ffe6e1874ab5ce5586d50f76cdc6ca32 https://doi.org/10.1212/wnl.0000000000200519 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....ffe6e1874ab5ce5586d50f76cdc6ca32 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 00283878 |
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