Please use this identifier to cite or link to this item: https://hdl.handle.net/1/1061
Title: Craniofacial phenotyping for prediction of obstructive sleep apnoea in a Chinese population
Authors: Lee, Richard W ;Sutherland, K.;Petocz, P.;Chan, T.O.;Ng, S.;Hui, D.S.;Cistulli, P.A.
Affliation: Central Coast Local Health District
Gosford Hospital
The University of Newcastle
Issue Date: Aug-2016
Source: 21(6):1118-25
Journal title: Respirology
Department: Respiratory Medicine
Abstract: BACKGROUND AND OBJECTIVE: Craniofacial morphology is a risk factor for obstructive sleep apnoea (OSA). Facial photography has previously shown utility in predicting OSA in a Caucasian sleep clinic. However, ethnic differences in OSA risk factors may influence these facial predictors. Our aim was to assess phenotypical facial measurements for OSA prediction in a Chinese population. METHODS: Calibrated frontal and profile facial photographs were taken before polysomnography. Photographs were analysed to derive head, face and neck measurements. Demographical, anthropometrical and facial photographical variables were considered in prediction models for OSA. OSA prediction models were derived using logistic regression and classification and regression tree techniques. RESULTS: Two-hundred subjects were recruited (146 OSA, 54 controls). The OSA group contained more men (77% vs 61%) and were more obese. Logistic regression modelling found cervicomental angle (OR 1.06/degree, 95% CI: 1.03-1.09, P < 0.001) and face width (OR 1.7/cm, 95% CI: 1.1-2.7, P = 0.02) predicted OSA (area under the receiver operating characteristics curve 0.76). Classification and regression tree analysis identified cricomental space area, mandibular width, mandibular plane angle and neck soft tissue area as predictors (area under receiver operating characteristics curve 0.81). CONCLUSION: In a Hong Kong Chinese sleep clinic, facial photographical measurements had predictive utility for OSA. Prediction models had similar accuracy and included variables to a previous Caucasian population.
URI: https://elibrary.cclhd.health.nsw.gov.au/cclhdjspui/handle/1/1061
DOI: 10.1111/resp.12792
Pubmed: https://www.ncbi.nlm.nih.gov/pubmed/27083503
ISSN: 1323-7799
Publicaton type: Journal Article
Appears in Collections:Respiratory

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