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心血管疾病患者的睡眠呼吸紊乱无法通过 ESS、STOP-BANG 和柏林问卷检测到。

Sleep-disordered breathing in patients with cardiovascular diseases cannot be detected by ESS, STOP-BANG, and Berlin questionnaires.

机构信息

Clinic III for Internal Medicine, Heart Center, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.

Evangelisches Klinikum Köln-Weyertal, Weyertal 76, 50931, Cologne, Germany.

出版信息

Clin Res Cardiol. 2018 Nov;107(11):1071-1078. doi: 10.1007/s00392-018-1282-7. Epub 2018 May 29.

Abstract

Sleep-disordered breathing (SDB) is highly prevalent in patients with cardiovascular diseases (CVD) and associated with poor outcome. At least 50% of heart failure (HF) patients present with SDB, equally divided in obstructive sleep apnea (OSA) and central sleep apnea (CSA). CVD patients with SDB do not always present with typical SDB symptoms. Therefore, we asked whether established questionnaires allow for the reliable detection of SDB. In this prospective cohort study, 89 CVD patients (54 male, 59 ± 15 years, BMI 30 ± 6 kg/m) in stable clinical state underwent an ambulatory polygraphy. SDB was defined as an apnea-hypopnea index (AHI) ≥ 15/h. We evaluated the Epworth Sleepiness Scale (ESS), STOP-BANG and Berlin questionnaires as well as anthropometric data and comorbidities regarding their ability to predict SDB. The ESS showed no correlation with SDB. The sensitivity of the Berlin Questionnaire to detect SDB was 73%, specificity was 42%. The STOP-BANG questionnaire showed a sensitivity of 97% while specificity was 13%. Coronary heart disease and/or history of myocardial infarction, hyperuricemia and age significantly contributed to a logistic regression model predicting presence of SDB. However, our regression model explains only 36% of the variance regarding the presence or absence of SDB. The approach to find variables, which would allow an early and reliable differentiation between patients with CVD and coexistence or absence of SDB, failed. Thus, as CVD patients show a high SDB prevalence and poor outcome, only a systematic screening based on measures of respiration-related parameters (i.e., respiratory flow, blood oxygen saturation, etc.) allows for a reliable SDB assessment.

摘要

睡眠呼吸障碍(SDB)在心血管疾病(CVD)患者中非常普遍,并与不良预后相关。至少有 50%的心力衰竭(HF)患者存在 SDB,其中阻塞性睡眠呼吸暂停(OSA)和中枢性睡眠呼吸暂停(CSA)各占一半。患有 SDB 的 CVD 患者并不总是出现典型的 SDB 症状。因此,我们想知道已建立的问卷是否能够可靠地检测 SDB。在这项前瞻性队列研究中,89 名 CVD 患者(54 名男性,59±15 岁,BMI 30±6kg/m²)在稳定的临床状态下接受了动态多导睡眠图检查。SDB 定义为呼吸暂停低通气指数(AHI)≥15/h。我们评估了 Epworth 嗜睡量表(ESS)、STOP-BANG 和柏林问卷以及人体测量数据和合并症,以评估它们预测 SDB 的能力。ESS 与 SDB 无相关性。柏林问卷检测 SDB 的敏感性为 73%,特异性为 42%。STOP-BANG 问卷的敏感性为 97%,特异性为 13%。冠心病和/或心肌梗死史、高尿酸血症和年龄显著有助于预测 SDB 的逻辑回归模型。然而,我们的回归模型仅解释了 SDB 存在或不存在的 36%的方差。寻找能够早期、可靠地区分 CVD 患者与 SDB 共存或不存在的变量的方法失败了。因此,由于 CVD 患者的 SDB 患病率高且预后不良,只有基于呼吸相关参数(即呼吸流量、血氧饱和度等)的系统筛查才能可靠地评估 SDB。

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