title: Identification of patients at risk for postoperative respiratory complications using a preoperative obstructive sleep apnea screening tool and postanesthesia care assessment.
abstract: BACKGROUND
  Patients with obstructive sleep apnea are at risk for perioperative morbidity. The authors used a screening prediction model for obstructive sleep apnea to generate a sleep apnea clinical score (SACS) that identified patients at high or low risk for obstructive sleep apnea. This was combined with postanesthesia care unit (PACU) monitoring with the aim of identifying patients at high risk of postoperative oxygen desaturation and respiratory complications.
  
  
  METHODS
  In this prospective cohort study, surgical patients with a hospital stay longer than 48 h who consented were enrolled. The SACS (high or low risk) was calculated; all patients were monitored in the PACU for recurrent episodes of bradypnea, apnea, desaturations, and pain-sedation mismatch. All patients underwent pulse oximetry postoperatively; complications were documented. Chi-square, two-sample t test, and logistic regression were used for analysis. The oxygen desaturation index (number of desaturations per hour) was calculated. Oxygen desaturation index and incidence of postoperative cardiorespiratory complications were primary endpoints.
  
  
  RESULTS
  Six hundred ninety-three patients were enrolled. From multivariable logistic regression analysis, the likelihood of a postoperative oxygen desaturation index greater than 10 was increased with a high SACS (odds ratio = 1.9, P < 0.001) and recurrent PACU events (odds ratio = 1.5, P = 0.036). Postoperative respiratory events were also associated with a high SACS (odds ratio = 3.5, P < 0.001) and recurrent PACU events (odds ratio = 21.0, P < 0.001).
  
  
  CONCLUSIONS
  Combination of an obstructive sleep apnea screening tool preoperatively (SACS) and recurrent PACU respiratory events was associated with a higher oxygen desaturation index and postoperative respiratory complications. A two-phase process to identify patients at higher risk for perioperative respiratory desaturations and complications may be useful to stratify and manage surgical patients postoperatively.
authors.affiliation: Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota 55905, USA. gali.bhargavi@mayo.edu
authors.forename: Bhargavi
authors.initials: B
authors.lastname: Gali
authors.forename: Francis X
authors.initials: FX
authors.lastname: Whalen
authors.forename: Darrell R
authors.initials: DR
authors.lastname: Schroeder
authors.forename: Peter C
authors.initials: PC
authors.lastname: Gay
authors.forename: David J
authors.initials: DJ
authors.lastname: Plevak
mesh_terms: D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010092:Oximetry / Q000706:statistics & numerical data; D010100:Oxygen / Q000097:blood; D011182:Postoperative Care / Q000706:statistics & numerical data*; D011183:Postoperative Complications / Q000175:diagnosis* / Q000209:etiology; D011300:Preoperative Care / Q000379:methods*; D011446:Prospective Studies; D012044:Regression Analysis; D012107:Research Design; D012120:Respiration Disorders / Q000175:diagnosis* / Q000209:etiology; D012129:Respiratory Function Tests; D018570:Risk Assessment; D012720:Severity of Illness Index; D020181:Sleep Apnea, Obstructive / Q000097:blood / Q000175:diagnosis* / Q000601:surgery
chemical_list: D010100:Oxygen