Prediction of obstructive sleep apnea with craniofacial photographic analysis.

Objective: To develop models based on craniofacial photographic analysis for the prediction of obstructive sleep apnea (OSA).

Methods: Prospective cohort study. Methods: Sleep investigation unit in a university teaching hospital. Methods: One hundred eighty subjects (95.6% Caucasian) referred for the initial investigation of OSA were recruited consecutively. Methods: Clinical 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).

Results: Obstructive 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.

Conclusions: Craniofacial photographic analysis provides detailed anatomical data useful in the prediction of OSA. This method allows OSA risk stratification by craniofacial morphological phenotypes.