在患有阻塞性睡眠呼吸暂停的女性患者中建立和验证预测模型
Wenxuan Yu1, Shuwen Yang2, Qinhan Wu1
1Department of Respiratory and Critical Care, Zhongshan Hospital affiliated to Fudan University, Shanghai, China.
Women's health reports (New Rochelle, N.Y.)
|October 27, 2025
概括
一个新的诺莫格拉姆模型准确地预测了使用临床标记的妇女的阻塞性睡眠呼吸暂停 (OSA). 这种诊断工具的性能优于女性早期OSA识别的STOP-Bang得分.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 睡眠医学 睡眠医学
- 临床流行病学临床流行病学
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种影响女性健康的普遍疾病.
- 准确的,非侵入性诊断工具对于OSA妇女的及时干预至关重要.
研究的目的:
- 在女性患者中开发和验证阻塞性睡眠呼吸暂停 (OSA) 的临床诊断模型.
- 与已建立的STOP-Bang得分相比,评估这个新型模型的预测效果.
主要方法:
- 对317名女性患者进行了回顾性分析,并提供了完整的睡眠监测数据.
- 利用LASSO和多变量物流回归来识别风险因素并构建一个名ogram预测模型.
- 验证了模型的性能,并将其预测准确度与STOP-Bang得分进行了比较.
主要成果:
- 确定了年龄,打,脑血管疾病和爱普沃思睡眠度量表得分作为女性OSA的独立风险因素.
- 诺莫格拉姆模型显示出强烈的歧视,C指数为0.881 (培训) 和0.815 (验证).
- 与STOP-Bang得分相比,开发的诺莫格拉姆模型显示出更高的预测效率.
结论:
- 诺莫格拉姆预测模型提供了高准确性,一致性和临床实用性,用于识别女性OSA患者.
- 该工具支持早期识别高风险个体,促进迅速的预防和干预策略.
- 该模型通过提供可靠的,非侵入性的方法来评估女性OSA风险,从而增强临床实践.
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