Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026
Research priorities to improve classification of sleep-wake disorders.
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Sateia Michael J, Abbott Sabra, Auger R Robert, Chervin Ronald D, Donovan Lucas M, Hogl Birgit, Kimoff R John, Lichstein Kenneth L, Manber Rachel, Manconi Mauro, Maski Kiran P, Ondo William G, Patel Sanjay R, Patil Susheel P, Plante David T, Ross Kristie R, Schenck Carlos H, Stefani Ambra, Videnovic Aleksandar
Аннотация
PURPOSE: The objectives of this paper are to identify areas of data deficiencies most relevant to classification of sleep-wake disorders, present existing evidence that informs decisions, and suggest research goals to elucidate classification decisions. METHODS: A panel of experts in nosology, consisting of ICSD-3-TR task force members and work group chairs, identified key questions related to the classification of sleep disorders. The panel reviewed the literature based on systematic and targeted searches and developed research priorities to address these questions. RESULTS: Numerous unresolved issues were identified following preparation of ICSD-3-TR, including whether insomnia with objective short sleep duration should be a formal subtype of chronic insomnia disorder and nocturnal muscle cramps should be removed as a sleep–wake disorder. In addition, the panel identified the following issues for inclusion: (1) defining diagnostic metrics for COMISA, (2) refining metrics for obstructive sleep apnea and subtypes, (3) clarifying the pathological significance of central sleep apnea, (4) subtyping of idiopathic hypersomnia, (5) evaluating circadian biomarkers and subtyping of delayed sleep–wake phase disorder, (6) revising criteria for periodic limb movement disorder, and (7) establishing REM atonia loss thresholds for REM sleep behavior disorder Research directions and methodologies are outlined for each area. CONCLUSIONS: Accurate diagnosis is essential for effective treatment. Imprecise criteria risk unnecessary or missed treatments. Greater investment in diagnostic research is needed to advance data-driven classification systems.