Evidence map›Paper›PMID 36186765›Full record

ArticleFrontiers in medicine2022

Optimization of anesthetic decision-making in ERAS using Bayesian network.

Yuwen Chen, Yiziting Zhu, Kunhua Zhong, Zhiyong Yang, Yujie Li, Xin Shu, Dandan Wang, Peng Deng, Xuehong Bai, Jianteng Gu and 6 more

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Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

16 authors.

Yuwen ChenChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China.
Yiziting ZhuDepartment of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Kunhua ZhongChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China.
Zhiyong YangDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Yujie LiDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Xin ShuDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Dandan WangDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Peng DengDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Xuehong BaiDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Jianteng GuDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Kaizhi LuDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Ju ZhangChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China.
Lei ZhaoDepartment of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Tao ZhuDepartment of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China.
Ke WeiDepartment of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Bin YiDepartment of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Bayesian networkdecision-makingenhanced recovery after surgerygynecological tumormachine learning

Identifiers

PMID36186765
PMCPMC9519180

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.