Predictive value of pulmonary function parameters for sleep apnea syndrome

Nocturnal polysomnography is the standard diagnostic test for sleep apnea s yndrome (SAS) but is both expensive and time-consuming. We developed a pred ictive index for SAS based on pulmonary function data, including respirator y resistance determined by the forced oscillation technique, from 168 obese snorers with suspected SAS. Our model used logistic regression to obtain c ase-by-case predictions of the probability of SAS, defined as an apneahypop nea index (AHI) greater than or equal to 15 during overnight polysomnograph y. We then tested our model in a prospective group of 101 similar patients. Specific respiratory conductance and daytime oxygen saturation contributed significantly to the model. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the index computed from these parameters were 98%, 86% 90%, and 97%, respectively. in the prospect ive group, the model proved repeatable, with 100% sensitivity, 84% specific ity, 86% PPV, and 100% NPV. The high NPV may help to identify obese snorers with a SAS risk that is so low as to make polysomnography unnecessary. Bas ed on the 50% prevalence of SAS in our study and on the fact that polysomno graphy is required in all patients with daytime somnolence, we calculated t hat using our model would have obviated the need for polysomnography in 38% of our patients.