A Prediction Nomogram Combining Epworth Sleepiness Scale and Other Clinical Parameters to Predict Obstructive Sleep Apnea in Patients with Hypertension. (5th August 2022)

• Record Type: Journal Article
• Authors: Wang, Lin; Sun, Dongsheng; Xie, Jianhong; Zhang, Li; Lao, Dibo; Xu, Shaokun
• Other Names: D Elia Lanfranco Academic Editor.
• Is Part Of: International journal of hypertension. Volume 2022(2022)
• Publication Date: 2022-08-05
• Subjects: Hypertension -- Periodicals; Hypertension; Hypertension; Periodicals; 616.132005
• DOI: 10.1155/2022/3861905
• Languages: English
• ISSNs: 2090-0384

Abstract

Abstract : Background . Obstructive sleep apnea (OSA) is common in patients with hypertension. Nonetheless, OSA is underdiagnosed despite considerable evidence of the association between OSA and adverse health outcomes. This study developed and validated a clinical nomogram to predict OSA in patients with hypertension based on the Epworth Sleepiness Scale (ESS) score and OSA-related parameters. Methods . A total of 347 hypertensive patients with suspected OSA were retrospectively enrolled and randomly assigned to a training set and a validation set at 70 : 30 ( N = 242/N = 105) ratio. OSA was diagnosed through sleep monitoring and was defined as an apnea-hypopnea index ≥5 events/h. Using the least absolute shrinkage and selection operator regression model, we identified potential predictors of OSA and constructed a nomogram model in the training set. The predictive performance of the nomogram was assessed and validated by discrimination and calibration. The nomogram was also compared with ESS scores according to decision curve analysis (DCA), integrated discrimination index (IDI), and net reclassification index (NRI). Results . ESS scores, body mass index, neck circumference, snoring, and observed apnea predicted OSA are considered. The nomogram showed similar discrimination between the training set (AUC: 0.799, 95% CI: 0.743–0.847) and validation set (AUC: 0.766, 95% CI: 0.673–0.843) and good calibration in the training (P = 0.925 > 0.05 ) and validation (P = 0.906 > 0.05 ) sets. Compared with the predictive value of the ESS, the nomogram was clinically useful and significantly improved reclassification accuracy (NRI: 0.552, 95% CI: 0.282–0.822, P < 0.001 ; IDI: 0.088, 95% CI: 0.045–0.133, P < 0.001 ) at a probability threshold of >42%. Conclusions . We developed a novel OSA prediction nomogram based on ESS scores and OSA-related parameters. This nomogram may help improve clinical decision-making, especially in communities and primary clinics, where polysomnography is unavailable.