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 () and validation () 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, ; IDI: 0.088, 95% CI: 0.045–0.133, ) 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.

中文翻译：

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结合 Epworth 嗜睡量表和其他临床参数预测高血压患者阻塞性睡眠呼吸暂停的预测列线图

背景。阻塞性睡眠呼吸暂停 (OSA) 在高血压患者中很常见。尽管如此，尽管有大量证据表明 OSA 与不良健康结果之间存在关联，但 OSA 仍未得到充分诊断。本研究开发并验证了基于 Epworth 嗜睡量表 (ESS) 评分和 OSA 相关参数预测高血压患者 OSA 的临床列线图。方法。回顾性招募了 347 名疑似 OSA 的高血压患者，并在 70 : 30 时随机分配到训练集和验证集（N = 242/N = 105) 比率。OSA 通过睡眠监测诊断，定义为呼吸暂停低通气指数≥5 事件/小时。使用最小绝对收缩和选择算子回归模型，我们确定了 OSA 的潜在预测因子，并在训练集中构建了列线图模型。通过鉴别和校准评估和验证列线图的预测性能。根据决策曲线分析 (DCA)、综合辨别指数 (IDI) 和净重分类指数 (NRI)，还将列线图与 ESS 分数进行比较。结果. 考虑了 ESS 评分、体重指数、颈围、打鼾和观察到的呼吸暂停预测 OSA。列线图显示训练集 (AUC: 0.799, 95% CI: 0.743–0.847) 和验证集 (AUC: 0.766, 95% CI: 0.673–0.843) 和训练中的良好校准 ()和验证 ()集。与 ESS 的预测值相比，列线图在临床上有用并显着提高了重新分类的准确性（NRI：0.552，95% CI：0.282-0.822，; IDI: 0.088, 95% CI: 0.045–0.133,)的概率阈值 > 42%。结论。我们开发了一种基于 ESS 分数和 OSA 相关参数的新型 OSA 预测列线图。该列线图可能有助于改善临床决策，尤其是在无法使用多导睡眠图的社区和初级诊所。