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本文引用的文献

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STOP questionnaire: a tool to screen patients for obstructive sleep apnea.STOP问卷:一种用于筛查阻塞性睡眠呼吸暂停患者的工具。
Anesthesiology. 2008 May;108(5):812-21. doi: 10.1097/ALN.0b013e31816d83e4.
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Sleep-disordered breathing in the usual lifestyle setting as detected with home monitoring in a population of working men in Japan.在日本上班族人群中通过家庭监测在日常生活环境下检测到的睡眠呼吸障碍
Sleep. 2008 Mar;31(3):419-25. doi: 10.1093/sleep/31.3.419.
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Impaired performance in commercial drivers: role of sleep apnea and short sleep duration.商业驾驶员的表现受损:睡眠呼吸暂停和短睡眠时间的作用。
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Practice parameters for the indications for polysomnography and related procedures: an update for 2005.多导睡眠图及相关检查适应证的实践参数:2005年更新版
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Living close to automobile traffic and quality of life in Japan: a population-based survey.
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Adiposity as compared with physical activity in predicting mortality among women.在预测女性死亡率方面,肥胖与身体活动的比较。
N Engl J Med. 2004 Dec 23;351(26):2694-703. doi: 10.1056/NEJMoa042135.
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Prevalence of sleep-disordered breathing in middle-aged Korean men and women.韩国中年男性和女性睡眠呼吸障碍的患病率。
Am J Respir Crit Care Med. 2004 Nov 15;170(10):1108-13. doi: 10.1164/rccm.200404-519OC. Epub 2004 Sep 3.
8
Occupational screening for obstructive sleep apnea in commercial drivers.商业驾驶员阻塞性睡眠呼吸暂停的职业筛查。
Am J Respir Crit Care Med. 2004 Aug 15;170(4):371-6. doi: 10.1164/rccm.200307-968OC. Epub 2004 May 13.
9
Presentation of multivariate data for clinical use: The Framingham Study risk score functions.用于临床的多变量数据呈现:弗雷明汉研究风险评分函数。
Stat Med. 2004 May 30;23(10):1631-60. doi: 10.1002/sim.1742.
10
Practice parameters for the use of portable monitoring devices in the investigation of suspected obstructive sleep apnea in adults.成人疑似阻塞性睡眠呼吸暂停调查中使用便携式监测设备的实践参数。
Sleep. 2003 Nov 1;26(7):907-13. doi: 10.1093/sleep/26.7.907.

用于识别睡眠呼吸障碍患者的简单四变量筛查工具。

Simple four-variable screening tool for identification of patients with sleep-disordered breathing.

作者信息

Takegami Misa, Hayashino Yasuaki, Chin Kazuo, Sokejima Shigeru, Kadotani Hiroshi, Akashiba Tsuneto, Kimura Hiroshi, Ohi Motoharu, Fukuhara Shunichi

机构信息

Department of Epidemiology and Healthcare Research, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan.

出版信息

Sleep. 2009 Jul;32(7):939-48.

PMID:19639757
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2706898/
Abstract

OBJECTIVES

To aid in the identification of patients with moderate-to-severe sleep-disordered breathing (SDB), we developed and validated a simple screening tool applicable to both clinical and community settings.

METHODS

Logistic regression analysis was used to develop an integer-based risk scoring system. The participants in this derivation study included 132 patients visiting one of 2 hospitals in Japan, and 175 residents of a rural town. The participants in the present validation study included 308 employees of a company in Japan who were undergoing a health check.

RESULTS

The screening tool consisted of only 4 variables: sex, blood pressure level, body mass index, and self-reported snoring. This tool (screening score) gave an area under the receiver operating characteristic curve (ROC) of 0.90, sensitivity of 0.93, and specificity of 0.66, using a cutoff point of 11. Predicted and observed prevalence proportions in the validation dataset were in close agreement across the entire spectrum of risk scores. In the validation dataset, the area under the ROC for moderate-to-severe SDB and severe SDB were 0.78 and 0.85, respectively. The diagnostic performance of this tool did not significantly differ from that of previous, more complex tools.

CONCLUSION

These findings suggest that our screening scoring system is a valid tool for the identification and assessment of moderate-to-severe SDB. With knowledge of only 4 easily ascertainable variables, which are routinely checked during daily clinical practice or mass health screening, moderate-to-severe SDB can be easily detected in clinical and public health settings.

摘要

目的

为了帮助识别中重度睡眠呼吸紊乱(SDB)患者,我们开发并验证了一种适用于临床和社区环境的简单筛查工具。

方法

采用逻辑回归分析来开发基于整数的风险评分系统。这项推导研究的参与者包括132名就诊于日本两家医院之一的患者,以及175名一个乡村小镇的居民。本验证研究的参与者包括308名正在接受健康检查的日本一家公司的员工。

结果

该筛查工具仅由4个变量组成:性别、血压水平、体重指数和自我报告的打鼾情况。使用截断值11时,该工具(筛查分数)的受试者操作特征曲线(ROC)下面积为0.90,灵敏度为0.93,特异度为0.66。在整个风险评分范围内,验证数据集中预测患病率与观察患病率比例非常接近。在验证数据集中,中重度SDB和重度SDB的ROC下面积分别为0.78和0.85。该工具的诊断性能与之前更复杂的工具相比无显著差异。

结论

这些结果表明,我们的筛查评分系统是识别和评估中重度SDB的有效工具。仅了解4个易于确定的变量,这些变量在日常临床实践或大规模健康筛查中会常规检查,就可以在临床和公共卫生环境中轻松检测出中重度SDB。