Chen Yuwen, Zhu Yiziting, Zhong Kunhua, Yang Zhiyong, Li Yujie, Shu Xin, Wang Dandan, Deng Peng, Bai Xuehong, Gu Jianteng, Lu Kaizhi, Zhang Ju, Zhao Lei, Zhu Tao, Wei Ke, Yi Bin
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China.
Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Med (Lausanne). 2022 Sep 14;9:1005901. doi: 10.3389/fmed.2022.1005901. eCollection 2022.
Enhanced recovery after surgery (ERAS) can accelerate patient recovery. However, little research has been done on optimizing the ERAS-related measures and how the measures interact with each other. The Bayesian network (BN) is a graphical model that describes the dependencies between variables and is also a model for uncertainty reasoning. In this study, we aimed to develop a method for optimizing anesthetic decisions in ERAS and then investigate the relationship between anesthetic decisions and outcomes. First, assuming that the indicators used were independent, the effects of combinations of single indicators were analyzed based on BN. Additionally, the impact indicators for outcomes were selected with statistical tests. Then, based on the previously selected indicators, the Bayesian network was constructed using the proposed structure learning method based on Strongly Connected Components (SCC) Local Structure determination by Hill Climbing Twice (LSHCT) and adjusted according to the expert's knowledge. Finally, the relationship is analyzed. The proposed method is validated by the real clinical data of patients with benign gynecological tumors from 3 hospitals in China. Postoperative length of stay (LOS) and total cost (TC) were chosen as the outcomes. Experimental results show that the ERAS protocol has some pivotal indicators influencing LOS and TC. Identifying the relationship between these indicators can help anesthesiologists optimize the ERAS protocol and make individualized decisions.
术后加速康复（ERAS）可加速患者康复。然而，关于优化ERAS相关措施以及这些措施如何相互作用的研究较少。贝叶斯网络（BN）是一种描述变量之间依赖性的图形模型，也是一种不确定性推理模型。在本研究中，我们旨在开发一种在ERAS中优化麻醉决策的方法，然后研究麻醉决策与结果之间的关系。首先，假设所使用的指标是独立的，基于贝叶斯网络分析单一指标组合的效果。此外，通过统计检验选择影响结果的指标。然后，基于先前选择的指标，使用基于强连通分量（SCC）的局部结构确定的两次爬山法（LSHCT）提出的结构学习方法构建贝叶斯网络，并根据专家知识进行调整。最后，分析这种关系。所提出的方法通过来自中国3家医院的妇科良性肿瘤患者的真实临床数据进行验证。选择术后住院时间（LOS）和总成本（TC）作为结果。实验结果表明，ERAS方案有一些影响LOS和TC的关键指标。识别这些指标之间的关系可以帮助麻醉医生优化ERAS方案并做出个性化决策。
