在接受重大手术的老年患者中预测术后肺部感染:基于后勤回归和机器学习模型的研究
Jie Liu1,2, Xia Li1, Yanting Wang1
1Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
后勤回归 (LR) 有效地预测老年手术患者的术后肺感染 (POI),优于机器学习模型. 一个开发出来的名录图有助于识别高风险个体,以便更好地进行外科手术期间的管理.
科学领域:
- 医疗信息学 医疗信息学
- 外科手术的结果
- 老年医学 老年医学
背景情况:
- 手术后肺部感染 (POI) 显著影响预后,特别是在接受重大手术的老年患者中.
- 对高风险人群中POI的物流回归 (LR) 和机器学习 (ML) 的预测能力需要进一步研究.
研究的目的:
- 评估LR和ML算法对老年手术患者POI的预测性能.
- 开发一种临床工具,用于识别高风险患者,指导术后管理.
主要方法:
- 一项回顾性队列研究包括9481名接受重大手术的老年患者.
- 对于LR和ML模型,使用了最小绝对收缩和选择操作员回归选择的特征.
- 为了ML模型的解释性,采用了随机森林分析.
主要成果:
- LR实现了最高的AUC (0.80),超过了决策树 (AUC 0.75) 等ML模型.
- 该LR模型表现出卓越的精度 (88.22%),特异性 (90.29%),精度 (44.42%) 和F1得分 (54.25%).
- 一个基于LR的基于网络的名录,将患者分为POI的不同风险区间.
结论:
- 后勤回归比流行的ML算法更有效地预测老年手术患者的POI.
- 开发的诺米图表有助于识别高风险的老年患者,并支持术后管理计划.
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