术前评估患有术后呼吸抑制风险的患者
Atousa Assadi1, Frances Chung2, Azadeh Yadollahi1
1Institute of Biomedical Engineering, University of Toronto, Canada; KITE-Toronto Rehabilitation Institute, University Health Network, Canada; Temerty Center for AI Research and Education in Medicine, University of Toronto, Canada.
确定患有术后呼吸抑制风险的患者至关重要. 使用手术前氧血球和度 (SpO2) 和人口统计数据的新模型准确地预测了这种风险,优于现有方法.
科学领域:
- 麻醉学和重症监护医学
- 呼吸系统医学 呼吸系统医学
- 医疗信息学 医疗信息学
背景情况:
- 用于术后疼痛管理的阿片类药物经常导致睡眠期间的呼吸抑制.
- 这种情况导致低氧化和高头,增加心血管事件,死亡率和医疗保健使用的风险.
- 风险人群的术前鉴定对于有针对性的术后护理至关重要.
研究的目的:
- 开发和验证一个预测模型,用于识别患有术后呼吸系统抑郁症高风险的患者.
- 为了利用手术前的人口统计和一夜间氧血球和 (SpO2) 数据进行风险评估.
主要方法:
- 对159名患者的SpO2信号的回顾性分析,记录在手术前和手术后的第三个晚上.
- 呼吸抑制的定义是SPO2 ≤85%超过3分钟.
- 从手术前的SpO2信号中提取特征,然后通过嵌套交叉验证进行系统的特征选择和模型开发.
主要成果:
- 机器学习模型,包括随机森林,XGBoost和Naïve Bayes,显示出卓越的预测性能.
- 开发的模型表现优于现有的PRODIGY风险评估工具.
- 关键预测因素包括人口因素和手术前一夜间SPO2和脱的特征.
结论:
- 人口统计和手术前的SpO2模式可以有效地识别患有术后呼吸抑制高风险的患者.
- 这种方法为手术前风险分层提供了一种非侵入性和具有成本效益的方法.
- 这些发现支持将这种模型整合到外科手术期间的护理途径中,以减轻不良呼吸道结果.
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