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

Abstract

Objectives: 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. Methods: 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. Results: 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 ( P < .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). Conclusion: The newly developed nomogram can effectively help physicians make better clinical decisions, which may save a lot of time and costs.