在propofol-remimazolam托西拉特麻醉中的不良事件预测
Minmin Zhai1, Fengqiang Sun1, Shengyong Liang1
1Department of Anesthesiology, Feicheng People's Hospital, Taian, China.
Frontiers in medicine
|February 2, 2026
概括
一个新的随机森林模型有效地预测了在propofol-remimazolam tosilate麻醉期间与麻醉相关的不良事件 (ARAEs). 关键预测因素包括手术持续时间,血液动力学稳定性和呼吸系统恢复,有助于患者安全.
科学领域:
- 麻醉学和外科手术期间的医学.
- 医疗信息学和机器学习
- 临床风险预测预测
背景情况:
- 麻醉相关的不良事件 (ARAE) 在手术过程中存在风险.
- 越来越多地使用与雷米马佐拉姆托西拉特结合的普罗波.
- 对ARAE的预测模型对于患者安全至关重要.
研究的目的:
- 开发和验证ARAEs的预测模型.
- 为了确定预测ARAEs的外科手术临床指标.
- 评估用于ARAE预测的机器学习算法.
主要方法:
- 对接受普罗波福-雷米马佐拉姆托西拉特麻醉的患者进行了回顾性研究.
- 数据收集包括人口统计,生命体征,实验室结果和恢复参数.
- 开发并与其他机器学习算法进行验证的随机森林 (RF) 模型.
主要成果:
- 手术持续时间,手术内低血压,呼吸恢复时间,血清肌素和PaCO2被确定为ARAE的独立风险因素.
- 射频模型实现了最高的区分能力 (AUC 0.814培训,0.777验证).
- 手术的持续时间和麻醉药物剂量比率是关键的预测因素.
结论:
- 开发的RF模型准确地预测了在接受普罗波福-雷米马佐拉姆托西拉特的患者中的ARAE.
- 术后的指标,如手术持续时间,血液动力学稳定性和呼吸系统恢复,都是重要的预测指标.
- 这种模型可以提高麻醉期间的患者安全.
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