Prediction of obstructive sleep apnea syndrome in a large Greek population

We aimed to evaluate the predictive value of anthropometric measurements and self-reported symptoms of obstructive sleep apnea syndrome (OSAS) in a large number of not yet diagnosed or treated patients. Commonly used clinical indices were used to derive a prediction formula that could identify patients at low and high risk for OSAS. Two thousand six hundred ninety patients with suspected OSAS were enrolled. We obtained weight; height; neck, waist, and hip circumference; and a measure of subjective sleepiness (Epworth sleepiness scale-ESS) prior to diagnostic polysomnography. Excessive daytime sleepiness severity (EDS) was coded as follows: 0 for ESS a parts per thousand currency signaEuro parts per thousand 3 (normal), 1 for ESS score 4-9 (normal to mild sleepiness), 2 for score 10-16 (moderate to severe sleepiness), and 3 for score > 16 (severe sleepiness). Multivariate linear and logistic regression analysis was used to identify independent predictors of apnea-hypopnea index (AHI) and derive a prediction formula. Neck circumference (NC) in centimeters, body mass index (BMI) in kilograms per square meter, sleepiness as a code indicating EDS severity, and gender as a constant were significant predictors for AHI. The derived formula was: The probability that this equation predicts AHI greater than 15 correctly was 78%. Gender, BMI, NC, and sleepiness were significant clinical predictors of OSAS in Greek subjects. Such a prediction formula can play a role in prioritizing patients for PSG evaluation, diagnosis, and initiation of treatment.