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oai:repisalud.isciii.es:20.500.12105/234082024-10-04T13:16:35Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967col_20.500.12105_16978

00925njm 22002777a 4500 dc Gool, Jari K author Zhang, Zhongxing author Oei, Martijn SSL author Mathias, Stephanie author Dauvilliers, Yves author Mayer, Geert author Plazzi, Giuseppe author del Rio-Villegas, Rafael author Cano, Joan Santamaria author Sonka, Karel author Partinen, Markku author Overeem, Sebastiaan author Peraita-Adrados, Rosa author Heinzer, Raphael author Martins da Silva, Antonio author Högl, Birgit author Wierzbicka, Aleksandra author Heidbreder, Anna author Feketeova, Eva author Manconi, Mauro author Bušková, Jitka author Cañellas, Francesca author Bassetti, Claudio L author Barateau, Lucie author Pizza, Fabio author Schmidt, Markus H author Fronczek, Rolf author Khatami, Ramin author Lammers, Gert Jan author 2022 Background and objectives: Recent 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. Methods: We 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. Results: We 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. Discussion: Using 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. Gool JK, Zhang Z, Oei MS, Mathias S, Dauvilliers Y, Mayer G, et al. Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering. Neurology. 2022 Apr 18;10.1212/WNL.0000000000200519. 10.1212/WNL.0000000000200519 1526-632X Neurology http://hdl.handle.net/20.500.13003/18095 35437263 L2018699687 2-s2.0-85131544358 https://hdl.handle.net/20.500.12105/23408 804246800020 Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering