Feng Tong, Shan Guangliang, Hu Yaoda, He Huijing, Pei Guo, Zhou Ruohan, Ou Qiong
Sleep Center, Department of Geriatric Respiratory, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.
Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, People's Republic of China.
Nat Sci Sleep. 2025 Jan 25;17:167-182. doi: 10.2147/NSS.S492796. eCollection 2025.
Approximately 30% of patients with sleep-disordered breathing (SDB) present with masked hypertension, primarily characterized by elevated nighttime blood pressure. This study aimed to develop a hypertension prediction model tailored for primary care physicians, utilizing simple, readily available predictors derived from type IV sleep monitoring devices.
Participants were recruited from communities in Guangdong Province, China, between April and May 2021. Data collection included demographic information, clinical indicators, and results from type IV sleep monitors, which recorded oxygen desaturation index (ODI), average nocturnal oxygen saturation (MeanSpO2), and lowest recorded oxygen saturation (MinSpO2). Hypertension was diagnosed using blood pressure monitoring or self-reported antihypertensive medication use. A nomogram was constructed using multivariate logistic regression after Least Absolute Shrinkage and Selection Operator (LASSO) regression identified six predictors: waist circumference, age, ODI, diabetes status, family history of hypertension, and apnea. Model performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis (DCA).
The model, developed in a cohort of 680 participants and validated in 401 participants, achieved an AUC of 0.775 (95% CI: 0.730-0.820) in validation set. Calibration plots demonstrated excellent agreement between predictions and outcomes, while DCA confirmed significant clinical utility.
This hypertension prediction model leverages easily accessible indicators, including oximetry data from type IV sleep monitors, enabling effective screening during community-based SDB assessments. It provides a cost-effective and practical tool for prioritizing early intervention and management strategies in both primary care and clinical settings.
约30%的睡眠呼吸障碍（SDB）患者存在隐匿性高血压，其主要特征为夜间血压升高。本研究旨在开发一种针对基层医疗医生的高血压预测模型，利用从IV型睡眠监测设备获得的简单、易获取的预测指标。
2021年4月至5月期间，从中国广东省的社区招募参与者。数据收集包括人口统计学信息、临床指标以及IV型睡眠监测仪的结果，该监测仪记录了氧饱和度下降指数（ODI）、夜间平均氧饱和度（MeanSpO2）和最低记录氧饱和度（MinSpO2）。通过血压监测或自我报告的抗高血压药物使用情况来诊断高血压。在最小绝对收缩和选择算子（LASSO）回归确定了六个预测指标：腰围、年龄、ODI、糖尿病状态、高血压家族史和呼吸暂停后，使用多变量逻辑回归构建了列线图。使用曲线下面积（AUC）、校准图和决策曲线分析（DCA）评估模型性能。
该模型在680名参与者的队列中开发，并在401名参与者中进行验证，在验证集中的AUC为0.775（95%CI：0.730 - 0.820）。校准图显示预测与结果之间具有良好的一致性，而DCA证实了显著的临床实用性。
这种高血压预测模型利用了易于获取的指标，包括来自IV型睡眠监测仪的血氧饱和度数据，能够在基于社区的SDB评估中进行有效筛查。它为基层医疗和临床环境中优先开展早期干预和管理策略提供了一种经济高效且实用的工具。
