• Richard W W Lee, Peter Petocz, Tania Prvan, Andrew S L Chan, Ronald R Grunstein, and Peter A Cistulli.
• Centre for Sleep Health and Research, Department of Respiratory Medicine, Royal North Shore Hospital, NSW, Australia.
• Sleep. 2009 Jan 1; 32 (1): 46-52.

Study ObjectivesTo develop models based on craniofacial photographic analysis for the prediction of obstructive sleep apnea (OSA).DesignProspective cohort study.SettingSleep investigation unit in a university teaching hospital.PatientsOne hundred eighty subjects (95.6% Caucasian) referred for the initial investigation of OSA were recruited consecutively.InterventionsClinical assessment and frontal-profile craniofacial photographic analyses were performed prior to polysomnography. Prediction models for determining the presence of OSA (apnea-hypopnea index [AHI] > or =10) were developed using logistic regression analysis and classification and regression trees (CART).Measurements And ResultsObstructive sleep apnea was present in 63.3% of subjects. Using logistic regression, a model with 4 photographic measurements (face width, eye width, cervicomental angle, and mandibular length 1) correctly classified 76.1% of subjects with and without OSA (sensitivity 86.0%, specificity 59.1%, area under the receiver operating characteristics curve [AUC] 0.82). Combination of photographic and other clinical data improved the prediction (AUC 0.87), whereas prediction based on clinical assessment alone was lower (AUC 0.78). The optimal CART model provided a similar overall classification accuracy of 76.7%. Based on this model, 59.4% of the subjects were classified as either high or low risk with positive predictive value of 90.9% and negative predictive value of 94.7%, respectively. The remaining 40.6% of subjects have intermediate risk of OSA.ConclusionsCraniofacial photographic analysis provides detailed anatomical data useful in the prediction of OSA. This method allows OSA risk stratification by craniofacial morphological phenotypes.

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