Treatment usage patterns of oral appliances for obstructive sleep apnea over the first 60 days: a cluster analysis

2021 ◽  
Author(s):  
Kate Sutherland ◽  
Fernanda R. Almeida ◽  
Taiyun Kim ◽  
Elizabeth C. Brown ◽  
Fiona Knapman ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Oral Appliances ◽  
Obstructive Sleep ◽  
Usage Patterns

Obstructive Sleep Apnea: Diagnosis and Management

2020 ◽  
Author(s):  
Diane C Lim ◽  
Richard J Schwab
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Nerve Stimulation ◽  
Airway Pressure ◽  
Hypoglossal Nerve ◽  
Positive Airway Pressure ◽  
Oral Appliances ◽  
Craniofacial Abnormalities ◽  
Obstructive Sleep ◽  
Line Of Therapy

As part 2 of three chapters on sleep disordered breathing, this chapter reviews obstructive sleep apnea (OSA) diagnosis and management. OSA should be considered in all patients who have loud habitual snoring, excessive daytime sleepiness, and witnessed apneas. On physical examination, craniofacial abnormalities that can lead to sleep apnea include retrognathia, micrognathia, a narrow hard palate, nasal obstruction, an overjet, and an overbite. Enlargement of the upper airway soft tissue structures (the tongue, soft palate, lateral walls, and parapharyngeal fat pads) also increases the risk of OSA. The gold standard for making the diagnosis of OSA is overnight polysomnography, but home sleep apnea tests (HSAT) are rapidly gaining acceptance, especially in patients with a high probability of OSA. The first line of therapy for OSA remains positive airway pressure (PAP), with the second line of therapy being oral appliances. Another alternative to PAP therapy is hypoglossal nerve stimulation, which has been shown to decrease the Apnea-Hypopnea index by 67.4%. This review contains 6 figures, 3 tables, and 52 references. Key Words: craniofacial abnormalities, Epworth Sleepiness Scale, home sleep apnea test, hypoglossal nerve stimulation, obstructive sleep apnea, oral appliances, oral pharyngeal crowding, polysomnography, positive airway pressure, STOP-BANG

An Evaluation of a Titration Strategy for Prescription of Oral Appliances for Obstructive Sleep Apnea

CHEST Journal ◽  
2008 ◽  
Vol 133 (5) ◽  
pp. 1135-1141 ◽  
Author(s):  
Vidya Krishnan ◽  
Nancy A. Collop ◽  
Steven C. Scherr
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Oral Appliances ◽  
Obstructive Sleep

A clinic-based cluster analysis in patients with moderate-severe obstructive sleep apnea (OSA) in Chile

2020 ◽  
Vol 73 ◽  
pp. 16-22
Author(s):  
Gonzalo Labarca ◽  
Jorge Dreyse ◽  
Constanza Salas ◽  
Alexia Schmidt ◽  
Francisca Rivera ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Severe Obstructive Sleep Apnea ◽  
Obstructive Sleep

scholarly journals 0577 Cluster Analysis for the Association Between Obstructive Sleep Apnea Phenotypes: A Population-Based Longitudinal Study

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A221-A221
Author(s):  
P F Tempaku ◽  
L O Silva ◽  
T M Guimaraes ◽  
T A Vidigal ◽  
V D’Almeida ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Latent Class ◽  
Cluster Solution ◽  
Optimal Care ◽  
Sleep Study ◽  
Disturbed Sleep ◽  
Obstructive Sleep

Abstract Introduction The identification of subgroups of obstructive sleep apnea (OSA) is critical to understand disease causality and ultimately develop optimal care strategies customized for each subgroup. In this sense, we aimed to perform a cluster analysis to identify subgroups of individuals with OSA based on clinical parameters. Furthermore, we aimed to analyze whether subgroups remain after 8 years. Methods We used data derived from the Sao Paulo Epidemiologic Sleep Study (EPISONO) cohort, which was followed over 8 years. All individuals underwent polysomnography, answered questionnaires and had their blood collected for biochemical exams. OSA was defined according to an AHI equal or greater than 15 events per hour. Cluster analysis was performed using latent class analysis (LCA). Results Of the 1,042 individuals in the EPISONO baseline cohort, 68.3% accepted to participate in the follow-up study (n=712). We were able to replicate the OSA 3-cluster solution observed in previous studies: disturbed sleep, minimally symptomatic and excessively sleepy in both baseline (35.5%, 45.4% and 19.1%, respectively) and follow-up studies (41.9%, 43.4% and 14.8%, respectively). 44.8% of the participants migrated clusters between the two evaluations and the factor associated with this was a greater delta-AHI (B=-0.033, df=1, p=0.003). The optimal cluster solution for our sample based on Bayesian information criterion (BIC) was 2 clusters for baseline (disturbed sleep and excessively sleepy) and 3 clusters for follow-up (disturbed sleep, minimally symptomatic and excessively sleepy). Conclusion The results found replicate and confirm previously identified clinical clusters in OSA even in a longitudinal analysis. Support This work was supported by grants from AFIP, FAPESP and CAPES.

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