Predicting sleep apnea and excessive day sleepiness in the severely obese: indicators for polysomnography

Chest. 2003 Apr;123(4):1134-41. doi: 10.1378/chest.123.4.1134.

Abstract

Background: Obstructive sleep apnea (OSA) is common in severely obese subjects (body mass index [BMI] > 35). Overnight polysomnography (OPS) is the "gold standard" method of evaluating this condition; however, it is time-consuming, inconvenient, and expensive. Selection of patients for OPS would be enhanced if we could better predict those likely to have clinically significant OSA.

Study objective: To look for clinical and biochemical predictors of OSA in symptomatic patients presenting for obesity surgery.

Design and patients: Symptoms suggestive of OSA were sought in a structured interview. We report OPS results of 99 consecutive subjects in whom OSA was clinically suspected. Predictors of apnea-hypopnea index (AHI) were sought from an extensive preoperative data collection. Multivariate linear and logistic analysis was used to identify independent predictors of AHI.

Results: Symptoms were poor predictors of AHI, with observed sleep apnea the only positive predictor. Four clinical and two biochemical factors independently predicted AHI: observed sleep apnea, male sex, higher BMI, age, fasting insulin, and glycosylated hemoglobin A(Ic) (r(2) = 0.42). Neck circumference (the best single measure) could replace BMI and sex in the analysis (r(2) = 0.43). With cutoffs selected, a simple scoring system using these six factors provides a method of predicting those with moderate or severe OSA. A score > or = 3 provides a sensitivity and specificity of 89% and 81%, and 96% and 71% for AHIs of > or = 15 and > or = 30, respectively. None of the 31 subjects with scores of 0 or 1 were found to have an AHI > or = 15.

Conclusion: We explore sleep disturbance and report a simple method of predicting OSA in severely obese symptomatic subjects. This should assist in limiting the use of OPS to those with greater risk and provide a method of assessing risk in those not presenting primarily with a sleep problem.

MeSH terms

  • Adult
  • Female
  • Humans
  • Linear Models
  • Logistic Models
  • Male
  • Obesity / complications*
  • Obesity / epidemiology
  • Patient Selection*
  • Polysomnography*
  • Risk Assessment
  • Risk Factors
  • Sensitivity and Specificity
  • Sleep Apnea Syndromes / diagnosis*
  • Sleep Apnea Syndromes / epidemiology
  • Sleep Apnea Syndromes / etiology
  • Sleep Apnea, Obstructive / diagnosis*
  • Sleep Apnea, Obstructive / epidemiology
  • Sleep Apnea, Obstructive / etiology