Development and Validation of a Nomogram Model to Predict Obstructive Sleep Apnea.

Polysomnography was class I test for who was suspected of obstructive sleep apnea (OSA) which would cost lots of time and money. This study aimed to develop a nomogram model mainly based on oxygen and blood routine indicators to predict OSA. We retrospectively analyzed 685 patients with suspected OSA at our hospital. Multivariate analysis was used to construct a nomogram. The performance of the nomogram was assessed using calibration and discrimination. The multivariate analysis identified age, gender, body mass index, mean pulse oxygen saturation, percent nighttime with oxygen saturation less than 90%, red blood cell, hematocrit, and red blood cell distribution width SD as significant factors ( < .05). A nomogram was created for the prediction of OSA using these clinical parameters and was internally validated using a bootstrapping method. Our nomogram model showed good discrimination and calibration in terms of predicting OSA, and had a C-index of 0.935 [95% confidence interval (CI), 0.917-0.954] according to the internal validation. Discrimination and calibration in the validation group were also good (C-index, 0.957; 95% CI, 0.930-0.984). The newly developed nomogram can effectively help physicians make better clinical decisions, which may save a lot of time and costs.