Optimization of anesthetic decision-making in ERAS using Bayesian network - Chen, Yuwen; Zhu, Yiziting; Zhong, Kunhua; Yang, Zhiyong; Li, Yujie; Shu, Xin; Wang, Dandan; Deng, Peng; Bai, Xuehong; Gu, Jianteng; Lu, Kaizhi; Zhang, Ju; et al. (Frontiers in Medicine, 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. Bayesian Network (BN) is a graphical model that describes the dependencies between variables and also a model for uncertainty reasoning. In this study, we aimed to develop a method for anesthesia decision optimization in ERAS, and then investigate the relationship between anesthetic decisions and outcomes. First, assuming that the indicators used were independent of each other, the effects of combinations of single indicators were analyzed based on BN. And further the impact indicators for outcomes were selected with statistical tests. Then, based on the previously selected indicators, the Bayesian network was constructed through the proposed structure learning method based on Strongly Connected Components (SCC) Local Structure determination by Hill Climbing Twice (LSHCT) and adjusted according to expert’s knowledge. Finally, the relationship is analyzed. The proposed method is validated on the real clinical data of patients with benign gynecological tumor from 3 hospitals in China, and 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 that actually influence LOS and TC. Identifying the relationship between these indicators can help anesthesiologist to optimize ERAS protocol and make individualized decisions.