贝塞特评分:一种新的简单生物识别评分,用于查和分级阻塞性睡眠呼吸暂停症
Abdelbaset M Saleh1, Magda A Ahmed1, Eman A El Said1
1Department of Chest Medicine, Sleep Disordered Breathing Unit, Faculty of Medicine, Mansoura University, Egypt.
Sleep medicine: X
|August 28, 2023
概括
贝塞特得分有效地选阻塞性睡眠呼吸暂停 (OSA),显示出比柏林和STOP-Bang.Bang等现有工具更高的准确性. 这种经过验证的评分系统有助于在初级保健机构诊断OSA及其严重程度.
科学领域:
- 睡眠医学 睡眠医学
- 诊断工具 诊断工具
- 呼吸系统疾病 呼吸系统疾病
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种普遍的疾病,由于有限的多睡眠学 (PSG) 可用性,显著低诊断.
- 缺乏用于预测和管理OSA的初级保健的验证,简单的诊断工具,影响临床结果.
- 目前的查方法在准确性和可访问性方面存在限制,无法广泛检测OSA.
研究的目的:
- 评估新的BASET评分系统的诊断有效性,用于查阻塞性睡眠呼吸暂停 (OSA).
- 评估BASET评分在分级OSA的严重程度的能力.
- 将BASET得分的预测准确度与已有的选工具 (柏林和STOP-Bang) 进行比较.
主要方法:
- 一组144名被怀疑患有OSA的受试者接受了病史,体检 (包括BASET组件:BMI,腹周,打,爱普沃思睡眠量表,舌牙印记) 和全夜PSG.
- 采用接收器运行特征 (ROC) 分析来确定最佳切断点,并比较BASET,柏林和STOP-Bang得分在预测OSA中的准确性.
- 使用克朗巴赫的alpha评估了BASET分数组件的内部一致性可靠性.
主要成果:
- 在BASET得分显示高内部一致性 (克朗巴赫的α=0.846) 和与呼吸暂停-呼吸暂停指数 (AHI) 的强相关性 (r=0.778,p<0.001).
- 与STOP-Bang (AUC=0.939) 和柏林 (AUC=0.901) 评分相比,BASET评分在预测OSA (AUC=0.984) 中表现出更高的准确性.
- 在预测准确性方面,BASET得分与OSA严重程度等级有显著的关联 (p<0.001),超过了柏林和STOP-Bang得分.
结论:
- 贝塞特评分是OSA诊断和严重程度分级的方便,可靠和有效工具.
- 与柏林和STOP-Bang分数相比,BASET分数为OSA预测提供了更高的准确性.
- BASET评分的高有效性和准确性支持其在初级保健中用于有效的OSA查和管理的潜在用途.
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