Fan Dan, Yin Qiyuan, Li Danni, Yao Yuchen, Wu Xingwei
Department of Anesthesiology, Southwest Medical University, Luzhou, China
Department of Anesthesiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.
BMJ Open. 2025 Oct 20;15(10):e093884. doi: 10.1136/bmjopen-2024-093884.
The purpose of the study is to construct a postoperative nausea and vomiting (PONV) risk prediction model for day-case laparoscopic cholecystectomy (LC) using a machine learning combination algorithm and evaluate its performance.
A retrospective cohort study.
The Hospital Information System (HIS) and the Surgical Anaesthesia Information Management System (SAIMS).
Patient data are collected from the day surgery ward of Sichuan Provincial People's Hospital from February 2023 to April 2024. The research subjects are adult patients (18-75) who underwent day-case LC, excluding patients with unexpected termination of the day surgery plan, such as the patient who was transferred to hepatobiliary surgery due to intraoperative conversion to laparotomy.
MAIN OUTCOMES/MEASURES: The study employed two data filling methods, two data sampling methods, two variable screening methods and six machine learning algorithms to construct 48 predictive models. Area under curve (AUC), accuracy, precision, recall rate and F1 value were used to evaluate the predictive performance of the model. The AUC of the test set is mainly used to evaluate the prediction performance, and the Shapley weighted explanatory value is used to determine the weight of the variable's prediction contribution. We will collect patient data from this unit in July 2025 to evaluate the model's performance.
A total of 2709 patients were selected for model construction in the study. 20 input variables were retained for developing the predictive model. The combined model of KNN, BSMOTE, RFEL and GBM shows the best AUC performance (0.9600). The five most important variables in the prediction model were postoperative pain, LESS method, citraturia dosage, gender and sufentanil dosage. An additional 211 patients were collected to validate the model performance with an AUC of 0.79.
The study finds that postoperative pain, LESS method and cisatracurium dosage are closely related to the occurrence of PONV in day-case LC. However, these three variables have rarely been reported in the previous literature and worth further research. The prediction model obtained in this study provides a meaningful reference for the perioperative prevention and treatment of PONV in day surgery.
本研究旨在使用机器学习组合算法构建日间腹腔镜胆囊切除术（LC）术后恶心呕吐（PONV）风险预测模型，并评估其性能。
一项回顾性队列研究。
医院信息系统（HIS）和手术麻醉信息管理系统（SAIMS）。
收集四川省人民医院日间手术病房2023年2月至2024年4月的患者数据。研究对象为接受日间LC的成年患者（18 - 75岁），不包括日间手术计划意外终止的患者，如因术中转为开腹手术而转至肝胆外科的患者。
主要结局/指标：本研究采用两种数据填充方法、两种数据抽样方法、两种变量筛选方法和六种机器学习算法构建48个预测模型。采用曲线下面积（AUC）、准确率、精确率、召回率和F1值评估模型的预测性能。测试集的AUC主要用于评估预测性能，使用Shapley加权解释值确定变量预测贡献的权重。我们将于2025年7月收集该单位的患者数据以评估模型性能。
本研究共选择2709例患者进行模型构建。保留20个输入变量用于开发预测模型。KNN、BSMOTE、RFEL和GBM的组合模型显示出最佳的AUC性能（0.9600）。预测模型中五个最重要的变量是术后疼痛、LESS方法、枸橼酸剂量、性别和舒芬太尼剂量。另外收集211例患者以验证模型性能，AUC为0.79。
本研究发现术后疼痛、LESS方法和顺阿曲库铵剂量与日间LC中PONV的发生密切相关。然而，这三个变量在以往文献中鲜有报道，值得进一步研究。本研究获得的预测模型为日间手术中PONV的围手术期防治提供了有意义的参考。
