后勤回归与响应表面模型在视频辅助胸腔镜手术患者响应预测中的不劣
Hui-Yu Huang1, Shih-Pin Lin1, Hsin-Yi Wang1
1Department of Anesthesiology, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University, Taipei 112201, Taiwan.
Pharmaceuticals (Basel, Switzerland)
|January 23, 2024
概括
后勤回归 (LR) 有效地预测视频辅助胸切除术 (VATS) 患者的麻醉出现,类似于响应表面模型 (RSM). LR提供了一个更简单,更易于使用的工具,以提高患者的安全性和恢复.
科学领域:
- 麻醉学和外科手术期间的医学.
- 医疗信息学和预测建模
背景情况:
- 响应表面模型 (RSM) 是麻醉中的新兴工具,但它们与逻辑回归 (LR) 进行预测麻醉出现的比较是有限的.
- 经常使用全静脉麻醉 (TIVA),需要可靠的方法来预测患者的康复.
研究的目的:
- 为了比较RSM和LR在预测视频辅助胸腔切除手术 (VATS) 患者麻醉出现的疗效.
- 评估LR是否可以用于改善患者安全并支持手术后增强恢复 (ERAS) 协议.
主要方法:
- 展望性,观察性研究与数据再分析,涉及29名患者 (ASA类II/III) 接受TIVA下的VATS.
- 监测麻醉的出现,并记录恢复反应 (RR) 的精确时间点.
- 在RSM和LR模型中检查不同麻醉剂度的影响.
主要成果:
- 在预测恢复反应的概率方面,RSM和LR都表现出很高的准确性,ROC曲线面积分别为0.979和0.989.
- 两种模型的预测性能之间没有发现显著差异.
- 在TIVA下,LR模型在预测VATS患者的兴奋方面被证明是有效的.
结论:
- 后勤回归 (LR) 是预测VATS患者麻醉出现的可行和有效工具.
- 虽然RSM更复杂,但LR提供了可比的准确性和更大的可访问性,使其成为临床应用的更简单的选择.
- 在像VATS这样的手术中,LR可以帮助提高患者的安全性,并促进手术后更快的恢复 (ERAS).
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