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睡眠呼吸障碍患者的识别:四变量筛查工具、STOP、STOP-Bang 和 Epworth 嗜睡量表的比较。

Identification of patients with sleep disordered breathing: comparing the four-variable screening tool, STOP, STOP-Bang, and Epworth Sleepiness Scales.

机构信息

College of Nursing & Health Innovation, Arizona State University, Phoenix, AZ 85004-0698, USA.

出版信息

J Clin Sleep Med. 2011 Oct 15;7(5):467-72. doi: 10.5664/JCSM.1308.

Abstract

STUDY OBJECTIVE

The Epworth Sleepiness Scale (ESS) has been used to detect patients with potential sleep disordered breathing (SDB). Recently, a 4-Variable screening tool was proposed to identify patients with SDB, in addition to the STOP and STOP-Bang questionnaires. This study evaluated the abilities of the 4-Variable screening tool, STOP, STOP-Bang, and ESS questionnaires in identifying subjects at risk for SDB.

METHODS

A total of 4,770 participants who completed polysomnograms in the baseline evaluation of the Sleep Heart Health Study (SHHS) were included. Subjects with RDIs ≥ 15 and ≥ 30 were considered to have moderate-to-severe or severe SDB, respectively. Variables were constructed to approximate those in the questionnaires. The risk of SDB was calculated by the 4-Variable screening tool according to Takegami et al. The STOP and STOP-Bang questionnaires were evaluated including variables for snoring, tiredness/sleepiness, observed apnea, blood pressure, body mass index, age, neck circumference, and gender. Sleepiness was evaluated using the ESS questionnaire and scores were dichotomized into < 11 and ≥ 11.

RESULTS

The STOP-Bang questionnaire had higher sensitivity to predict moderate-to-severe (87.0%) and severe (70.4%) SDB, while the 4-Variable screening tool had higher specificity to predict moderate-to-severe and severe SDB (93.2% for both).

CONCLUSIONS

In community populations such as the SHHS, high specificities may be more useful in excluding low-risk patients, while avoiding false positives. However, sleep clinicians may prefer to use screening tools with high sensitivities, like the STOP-Bang, in order to avoid missing cases that may lead to adverse health consequences and increased healthcare costs.

摘要

研究目的

Epworth 嗜睡量表(ESS)已被用于检测潜在的睡眠呼吸障碍(SDB)患者。最近,提出了一种四变量筛查工具,除了 STOP 和 STOP-Bang 问卷外,还可以识别 SDB 患者。本研究评估了四变量筛查工具、STOP、STOP-Bang 和 ESS 问卷在识别 SDB 风险患者中的能力。

方法

共有 4770 名参与者在睡眠心脏健康研究(SHHS)的基线评估中完成了多导睡眠图。呼吸暂停低通气指数(RDI)≥15 和≥30 的受试者分别被认为患有中重度或重度 SDB。构建了近似于问卷中的变量。根据 Takegami 等人的方法,使用四变量筛查工具计算 SDB 的风险。评估了 STOP 和 STOP-Bang 问卷,包括打鼾、疲劳/嗜睡、观察到的呼吸暂停、血压、体重指数、年龄、颈围和性别等变量。使用 ESS 问卷评估嗜睡,得分分为<11 和≥11。

结果

STOP-Bang 问卷对预测中重度(87.0%)和重度(70.4%)SDB 的敏感性较高,而四变量筛查工具对预测中重度和重度 SDB 的特异性较高(两者均为 93.2%)。

结论

在 SHHS 等社区人群中,高特异性可能更有助于排除低风险患者,避免假阳性。然而,睡眠临床医生可能更愿意使用敏感性较高的筛查工具,如 STOP-Bang,以避免遗漏可能导致不良健康后果和增加医疗保健成本的病例。

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