Takegami Misa, Hayashino Yasuaki, Chin Kazuo, Sokejima Shigeru, Kadotani Hiroshi, Akashiba Tsuneto, Kimura Hiroshi, Ohi Motoharu, Fukuhara Shunichi
Department of Epidemiology and Healthcare Research, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan.
Sleep. 2009 Jul;32(7):939-48.
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.
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.
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.
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.
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。
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。
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。
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。