A Nomogram for Predicting the Likelihood of Obstructive Sleep Apnea to Reduce the Unnecessary Polysomnography Examinations

Background: The currently available polysomnography (PSG) equipments and operating personnel are facing increasing pressure, such situation may result in the problem that a large number of obstructive sleep apnea (OSA) patients cannot receive timely diagnosis and treatment, we sought to develop a nomogram quantifying the risk of OSA for a better decision of using PSG, based on the clinical syndromes and the demographic and anthropometric characteristics. Methods: The nomogram was constructed through an ordinal logistic regression procedure. Predictive accuracy and performance characteristics were assessed with the area under the curve (AUC) of the receiver operating characteristics and calibration plots, respectively. Decision curve analyses were applied to assess the net benefit of the nomogram. Results: Among the 401 patients, 73 (18.2%) were diagnosed and grouped as the none OSA (apnea-hypopnea index [AHI] <5), 67 (16.7%) the mild OSA (5 ≤ AHI < 15), 82 (20.4%) the moderate OSA (15 ≤ AHI < 30), and 179 (44.6%) the severe OSA (AHI ≥ 30). The multivariable analysis suggested the significant factors were duration of disease, smoking status, difficulty of falling asleep, lack of energy, and waist circumference. A nomogram was created for the prediction of OSA using these clinical parameters and was internally validated using bootstrapping method. The discrimination accuracies of the nomogram for any OSA, moderate-severe OSA, and severe OSA were 83.8%, 79.9%, and 80.5%, respectively, which indicated good calibration. Decision curve analysis showed that using nomogram could reduce the unnecessary polysomnography (PSG) by 10% without increasing the false negatives. Conclusions: The established clinical nomogram provides high accuracy in predicting the individual risk of OSA. This tool may help physicians better make decisions on PSG arrangement for the patients referred to sleep centers