开发和验证用于预测儿童阻塞性睡眠呼吸暂停严重程度的诺米克图
Yue Liu1, Shi Qi Xie1, Xia Yang1
1School of Nursing, Chongqing Medical University, Chongqing, People's Republic of China.
Nature and science of sleep
|February 27, 2024
概括
早期识别患有阻塞性睡眠呼吸暂停 (OSA) 的儿童至关重要. 一个新的风险预测模型有效地识别了中度至重度OSA的儿童,有助于临床决策和改善结果.
科学领域:
- 儿科肺病学 儿科肺病学
- 睡眠医学 睡眠医学
- 医疗信息学 医疗信息学
背景情况:
- 儿童的阻塞性睡眠呼吸暂停 (OSA) 潜伏地呈现,需要早期识别才能有效管理.
- 识别患有严重OSA风险较高的儿童对于及时的临床干预和改善长期健康结果至关重要.
研究的目的:
- 开发和验证中国儿童OSA严重程度的风险预测模型.
- 在临床环境中准确识别患有中度至重度OSA的儿童.
主要方法:
- 对367名通过多睡眠学 (PSG) 诊断出OSA的儿童进行了回顾性分析.
- 使用LASSO回归和后勤回归进行变量选择,以构建用于OSA严重性预测的诺莫грам.
- 使用ROC曲线,校准曲线,DCA和CIC进行验证,以评估模型性能.
主要成果:
- 子周长,ANB,性别,学习问题和障碍水平被确定为中度至重度OSA的独立风险因素.
- 开发的名图显示了预测和观察概率之间的强烈一致性 (一致性指数在训练中为0.841,在验证中为0.75).
- 该模型表现出强大的预测效率和临床效用,将儿童分为高/低风险组,最佳截止值为0.39.
结论:
- 使用LASSO回归的验证预测模型可以识别患有不同程度的OSA风险的儿童.
- 这种模型有助于早期识别患有OSA的儿童,从而能够迅速进行临床管理.
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