title: Prediction of obstructive sleep apnea with craniofacial photographic analysis.
abstract: 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.
authors.affiliation: Centre for Sleep Health and Research, Department of Respiratory Medicine, Royal North Shore Hospital, NSW, Australia.
authors.forename: Richard W W
authors.initials: RW
authors.lastname: Lee
authors.forename: Peter
authors.initials: P
authors.lastname: Petocz
authors.forename: Tania
authors.initials: T
authors.lastname: Prvan
authors.forename: Andrew S L
authors.initials: AS
authors.lastname: Chan
authors.forename: Ronald R
authors.initials: RR
authors.lastname: Grunstein
authors.forename: Peter A
authors.initials: PA
authors.lastname: Cistulli
mesh_terms: D000328:Adult; D000368:Aged; D002508:Cephalometry / Q000379:methods* / Q000706:statistics & numerical data; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010780:Photogrammetry / Q000379:methods* / Q000706:statistics & numerical data; D017286:Polysomnography; D011237:Predictive Value of Tests; D011446:Prospective Studies; D018570:Risk Assessment / Q000706:statistics & numerical data; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*