title: Surface cephalometric and anthropometric variables in OSA patients: statistical models for the OSA phenotype.
abstract: OBJECTIVE
  We used statistical modelling to probe the contributions of anthropometric and surface cephalometric variables to the OSA phenotype.
  
  
  METHODS
  The design is prospective cohort study.
  
  
  METHODS
  The setting is community-based and sleep disorder laboratory.
  
  
  METHODS
  Study #1-Model development study: 147 healthy asymptomatic volunteers (62.6 % Caucasian; age, 18-76 years; 81 females; median multivariable apnea prediction index=0.15) and 140 diagnosed OSA patients (84.3 % Caucasian; age, 18-83 years; 41 females; polysomnography [PSG] determined apnea-hypopnea index >10 events/h). Study #2-Model test study: 345 clinic patients (age, 18-86 years; 129 females) undergoing PSG for diagnosis of OSA.
  
  
  METHODS
  We measured 10 anthropometric and 34 surface cephalometric dimensions (calipers) and calculated mandibular enclosure volumes for study #1 and recorded age and neck circumference for study #2. Statistical modelling included principal component (PC), logistic regression, and receiver-operator curve analyses.
  
  
  RESULTS
  Model development study: A regression model incorporating three identified PC predicted OSA with 88 % sensitivity and specificity. However, a simplified model based on age and NC alone was equally effective (87 % sensitivity and specificity). Model test study: The simplified model predicted OSA with high sensitivity (93 %) but poor specificity (21 %).
  
  
  CONCLUSIONS
  We conclude that in our clinic-based cohort, craniofacial bony and soft tissue structures (excluding neck anatomy) do not play a substantial role in distinguishing patients with OSA from those without. This may be because craniofacial anatomy does not contribute greatly to the pathogenesis of OSA in this group or because referral bias has created a relatively homogeneous phenotypic population.
authors.affiliation: Ludwig Engel Centre for Respiratory Research, Department of Respiratory and Sleep Medicine, Sydney, Australia, rita.ginn@sydney.edu.au.
authors.forename: Rita A
authors.initials: RA
authors.lastname: Perri
authors.forename: Kristina
authors.initials: K
authors.lastname: Kairaitis
authors.forename: Peter
authors.initials: P
authors.lastname: Cistulli
authors.forename: John R
authors.initials: JR
authors.lastname: Wheatley
authors.forename: Terence C
authors.initials: TC
authors.lastname: Amis
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000886:Anthropometry; D002508:Cephalometry / Q000706:statistics & numerical data*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008334:Mandible / Q000502:physiology; D008875:Middle Aged; D015233:Models, Statistical; D010641:Phenotype; D017286:Polysomnography; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D011446:Prospective Studies; D012680:Sensitivity and Specificity; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology; D055815:Young Adult