| Literature DB >> 33675267 |
Gilles Lavigne1, Takafumi Kato2, Alberto Herrero Babiloni3,4, Nelly Huynh5, Cibele Dal Fabbro1, Peter Svensson6,7, Ghizlane Aarab8, Jari Ahlberg9, Kazuyoshi Baba10, Maria Clotilde Carra11, Thays Crosara A Cunha12, Daniela A G Gonçalves13, Daniele Manfredini14, Juliana Stuginski-Barbosa15, Mieszko Wieckiewicz16, Frank Lobbezoo8.
Abstract
A recent report from the European Sleep Research Society's task force "Beyond AHI" discussed an issue that has been a long-term subject of debate - what are the best metrics for obstructive sleep apnoea (OSA) diagnosis and treatment outcome assessments? In a similar way, sleep bruxism (SB) metrics have also been a recurrent issue for >30 years and there is still uncertainty in dentistry regarding their optimisation and clinical relevance. SB can occur alone or with comorbidities such as OSA, gastroesophageal reflux disorder, insomnia, headache, orofacial pain, periodic limb movement, rapid eye movement behaviour disorder, and sleep epilepsy. Classically, the diagnosis of SB is based on the patient's dental and medical history and clinical manifestations; electromyography is used in research and for complex cases. The emergence of new technologies, such as sensors and artificial intelligence, has opened new opportunities. The main objective of the present review is to stimulate the creation of a collaborative taskforce on SB metrics. Several examples are available in sleep medicine. The development of more homogenised metrics could improve the accuracy and refinement of SB assessment, while moving forward toward a personalised approach. It is time to develop SB metrics that are relevant to clinical outcomes and benefit patients who suffer from one or more possible negative consequences of SB.Entities:
Keywords: dental sleep medicine; electromyography; obstructive sleep apnoea; phenotype; sleep bruxism; tooth-grinding
Year: 2021 PMID: 33675267 DOI: 10.1111/jsr.13320
Source DB: PubMed Journal: J Sleep Res ISSN: 0962-1105 Impact factor: 3.981