Ye Yanqing, Yan Ze-Lin, Huang Yuanshou, Li Li, Wang Shiming, Huang Xiaoxing, Zhou Jingmeng, Chen Liyi, Ou Chun-Quan, Chen Huaihong
Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
Otolaryngology Department, Foshan Nan Hai District People's Hospital, Foshan, People's Republic of China.
Nat Sci Sleep. 2023 Oct 17;15:839-850. doi: 10.2147/NSS.S418093. eCollection 2023.
Obstructive sleep apnea (OSA) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSA patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSA patients.
We performed a retrospective cohort study including 1920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSA. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSA. Moreover, we compared the performance of our model with the most commonly used screening tool, Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires.
Severe OSA was associated with male, BMI≥ 28 kg/m, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥9.5×10/L, hemoglobin ≥175g/L, triglycerides ≥1.7 mmol/L. The AUC of the final model was 0.76 (95% CI: 0.74-0.78), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 73% and 66%. Among patients having the information of SBQ, the AUC of our model was statistically significantly greater than that of SBQ (0.78 vs 0.66, P = 0.002).
Based on common clinical examination of admission, we develop a novel model and a nomogram for identifying severe OSA from inpatient with suspected OSA, which provides physicians with a visual and easy-to-use tool for screening severe OSA.
阻塞性睡眠呼吸暂停（OSA）是一种发病率高且与不良健康后果相关的疾病。筛查潜在的重度OSA患者将改善患者管理质量和预后，然而现有筛查工具的准确性和可行性并不令人满意。本研究的目的是开发并验证一种用于筛查潜在重度OSA患者的可行性良好的临床预测模型。
我们进行了一项回顾性队列研究，纳入1920名接受过夜多导睡眠监测的成年人，其中979例被诊断为重度OSA。基于人口统计学、症状和血液学数据，构建并交叉验证多变量逻辑回归模型，然后开发列线图以识别重度OSA。此外，我们在完成问卷的患者中比较了我们模型与最常用筛查工具即阻塞性睡眠呼吸暂停筛查问卷（SBQ）的性能。
重度OSA与男性、体重指数（BMI）≥28kg/m²、高血压、窒息感、嗜睡、呼吸暂停、白细胞计数≥9.5×10⁹/L、血红蛋白≥175g/L、甘油三酯≥1.7mmol/L相关。最终模型的曲线下面积（AUC）为0.76（95%置信区间：0.74 - 0.78），在通过最大化约登指数选择的最佳阈值下，敏感性和特异性分别为73%和66%。在有SBQ信息的患者中，我们模型的AUC在统计学上显著大于SBQ的AUC（0.78对0.66，P = 0.002）。
基于入院时的常规临床检查，我们开发了一种用于从疑似OSA住院患者中识别重度OSA的新型模型和列线图，为医生提供了一种直观且易于使用的筛查重度OSA的工具。
