Prediction of Obstructive Sleep Apnea with Craniofacial Photographic Analysis

• Published in: Sleep (New York, N.Y.) Vol. 32; no. 1; pp. 46 - 52
• Main Authors: Lee, Richard W W, Petocz, Peter, Prvan, Tania, Chan, Andrew S L, Grunstein, Ronald R, Cistulli, Peter A
• Format: Journal Article
• Language: English
• Published: United States Associated Professional Sleep Societies, LLC 01.01.2009
• Subjects: Adult; Aged; Cephalometry - methods; Cephalometry - statistics & numerical data; Cohort Studies; Craniofacial Phenotyping of OSA; Female; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - statistics & numerical data; Logistic Models; Male; Middle Aged; Photogrammetry - methods; Photogrammetry - statistics & numerical data; Polysomnography; Predictive Value of Tests; Prospective Studies; Risk Assessment - statistics & numerical data; Sleep Apnea, Obstructive - diagnosis
• ISSN: 1550-9109; 0161-8105; 1550-9109
• DOI: 10.5665/sleep/32.1.46

To develop models based on craniofacial photographic analysis for the prediction of obstructive sleep apnea (OSA). Prospective cohort study. Sleep investigation unit in a university teaching hospital. One hundred eighty subjects (95.6% Caucasian) referred for the initial investigation of OSA were recruited consecutively. 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). 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. Craniofacial photographic analysis provides detailed anatomical data useful in the prediction of OSA. This method allows OSA risk stratification by craniofacial morphological phenotypes.