C. Gauld, R. Lopez, C. Morin, P.A. Geoffroy, J. Maquet, P. Desvergnes, A. McGonigal, Y. Dauvilliers, P. Philip, G. Dumas, J-A. Micoulaud-Franchi
Journal of Sleep Research
Publication year: 2022

The third edition of the International Classification of Sleep Disorders (ICSD-3) is the authoritative clinical text for the diagnosis of sleep disorders. An important issue of sleep nosology is to better understand the relationship between symptoms found in conventional diagnostic manuals and to compare classifications. Nevertheless, to our knowledge, there is no specific exhaustive work on the general structure of the networks of symptoms of sleep disorders as described in diagnostic manuals. The general aim of the present study was to use symptom network analysis to explore the diagnostic criteria in the ICSD-3 manual. The ICSD-3 diagnostic criteria related to clinical manifestations were systematically identified, and the units of analysis (symptoms) were labelled from these clinical manifestation diagnostic criteria using three rules (“Conservation”, “Splitting”, “Lumping”). A total of 37 of the 43 main sleep disorders with 160 units of analysis from 114 clinical manifestations in the ICSD-3 were analysed. A symptom network representing all individual ICSD-3 criteria and connections between them was constructed graphically (network estimation), quantified with classical metrics (network inference with global and local measures) and tested for robustness. The global measure of the sleep symptoms network shows that it can be considered as a small world, suggesting a strong interconnection between symptoms in the ICSD-3. Local measures show the central role of three kinds of bridge sleep symptoms: daytime sleepiness, insomnia, and behaviour during sleep symptoms. Such a symptom network analysis of the ICSD-3 structure could provide a framework for better systematising and organising symptomatology in sleep medicine.

Keywords: International Classification of Sleep Disorders; biomarkers; classification; diagnosis; network approach; personalized medicine.

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