Prediction of Obstructive Sleep Apnea with Craniofacial Photographic Analysis

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.