Acta oto-laryngologica · 2022
Classification of facial phenotypes in Asian patients with obstructive sleep apnea.
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Zhang Zishanbai, Wang Huijun, Sun Dance, Fei Nanxi, Li Yanru, Han Demin
Аннотация
BACKGROUD: The facial phenotypes of Asian obstructive sleep apnea (OSA) patients remain unclear.
OBJECTIVES: (1) To describe the facial features of OSA patients. (2) To develop a model based on facial contour indicators to predict OSA. (3) To classify the facial phenotypes of Asian OSA patients.
MATERIALS AND METHODS: 110 patients with OSA (apnea-hypopnea index [AHI] ≥ 10/h) and 50 controls (AHI< 10/h) were selected to measure facial contour indicators. Indicators were compared between OSA patients and the control group. We used multivariable linear regression analysis to predict OSA severity and K-means cluster analysis to classify OSA patients into different phenotypes.
RESULTS: We built a model to predict OSA which explained 49.1% of its variance and classified OSA patients into four categories. Cluster 1 (Skeletal type) had the narrowest facial width indicators with narrowing of the retroglossal airway. Cluster 2 (Obese type) had the widest face, and narrowest hard palate, retropalatal, and hypopharyngeal airways. Cluster 3 (Nose type) had the narrowest nasal cavity. Cluster 4 (Long type) had the longest airway length.
CONCLUSIONS AND SIGNIFICANCE: Patients with OSA were classified into four categories, each of which identified different anatomic risk factors that can be used to select the treatment.
OBJECTIVES: (1) To describe the facial features of OSA patients. (2) To develop a model based on facial contour indicators to predict OSA. (3) To classify the facial phenotypes of Asian OSA patients.
MATERIALS AND METHODS: 110 patients with OSA (apnea-hypopnea index [AHI] ≥ 10/h) and 50 controls (AHI< 10/h) were selected to measure facial contour indicators. Indicators were compared between OSA patients and the control group. We used multivariable linear regression analysis to predict OSA severity and K-means cluster analysis to classify OSA patients into different phenotypes.
RESULTS: We built a model to predict OSA which explained 49.1% of its variance and classified OSA patients into four categories. Cluster 1 (Skeletal type) had the narrowest facial width indicators with narrowing of the retroglossal airway. Cluster 2 (Obese type) had the widest face, and narrowest hard palate, retropalatal, and hypopharyngeal airways. Cluster 3 (Nose type) had the narrowest nasal cavity. Cluster 4 (Long type) had the longest airway length.
CONCLUSIONS AND SIGNIFICANCE: Patients with OSA were classified into four categories, each of which identified different anatomic risk factors that can be used to select the treatment.