title: Derivation and validation of a simple perioperative sleep apnea prediction score.
abstract: BACKGROUND
  Obstructive sleep apnea (OSA) is a largely underdiagnosed, common condition, which is important to diagnose preoperatively because it has implications for perioperative management. Our purpose in this study was to identify independent clinical predictors of a diagnosis of OSA in a general surgical population, develop a perioperative sleep apnea prediction (P-SAP) score based on these variables, and validate the P-SAP score against standard overnight polysomnography.
  
  
  METHODS
  A retrospective, observational study was designed to identify patients with a known diagnosis of OSA. Independent predictors of a diagnosis of OSA were derived by logistic regression, based on which prediction tool (P-SAP score) was developed. The P-SAP score was then validated in patients undergoing overnight polysomnography.
  
  
  RESULTS
  The P-SAP score was derived from 43,576 adult cases undergoing anesthesia. Of these, 3884 patients (7.17%) had a documented diagnosis of OSA. Three demographic variables: age > 43 years, male gender, and obesity; 3 history variables: history of snoring, diabetes mellitus Type 2, and hypertension; and 3 airway measures: thick neck, modified Mallampati class 3 or 4, and reduced thyromental distance were identified as independent predictors of a diagnosis of OSA. A diagnostic threshold P-SAP score > or = 2 showed excellent sensitivity (0.939) but poor specificity (0.323), whereas for a P-SAP score > or = 6, sensitivity was poor (0.239) with excellent specificity (0.911). Validation of this P-SAP score was performed in 512 patients with similar accuracy.
  
  
  CONCLUSIONS
  The P-SAP score predicts diagnosis of OSA with dependable accuracy across mild to severe disease. The elements of the P-SAP score are derived from a typical university hospital surgical population.
authors.affiliation: Department of Anesthesiology, Ann Arbor, MI 48109-0048, USA. rsatyak@med.umich.edu
authors.forename: Satya Krishna
authors.initials: SK
authors.lastname: Ramachandran
authors.forename: Sachin
authors.initials: S
authors.lastname: Kheterpal
authors.forename: Flavia
authors.initials: F
authors.lastname: Consens
authors.forename: Amy
authors.initials: A
authors.lastname: Shanks
authors.forename: Tara M
authors.initials: TM
authors.lastname: Doherty
authors.forename: Michelle
authors.initials: M
authors.lastname: Morris
authors.forename: Kevin K
authors.initials: KK
authors.lastname: Tremper
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D000758:Anesthesia; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016343:Monitoring, Intraoperative; D019990:Perioperative Care; D017286:Polysomnography; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000453:epidemiology / Q000503:physiopathology; D020181:Sleep Apnea, Obstructive / Q000150:complications / Q000175:diagnosis; D012913:Snoring / Q000453:epidemiology