Zhang Xianhu, Huang Yong, Wang Yulong, Jiang Yongjun, Liu Bin, Ren Jun, Wang Daorong
Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, China.
Northern Jiangsu People's Hospital, Yangzhou, 225001, China.
J Robot Surg. 2025 Apr 21;19(1):167. doi: 10.1007/s11701-025-02259-8.
Postsurgical gastroparesis syndrome (PGS) significantly diminishes the quality of life for patients following surgery. With the evolution of robotic surgery, there is a debate on whether it can offer a novel treatment modality for gastric cancer and reduce the incidence of gastric paralysis syndrome. This study utilizes machine learning techniques and traditional logistic regression to construct and validate predictive models, with the aim of providing guidance for clinical practitioners. This study included two cohorts from one medical centers based on the surgical timing for division (Cohort 1: n = 619 for model building and internal validation; Cohort 2: n = 312 for external validation). In Cohort 1, a 3:1 ratio was used for training and validation in random forest and a 7:3 ratio for logistic regression. After analyzing the Receiver Operating Characteristic curves (ROC), we chose classical logistic regression to build the prediction model followed by evaluation with calibration and decision curve analysis (DCA). Finally, we performed external validation on Cohort 2. The model incorporated 7 factors including: Pre-operative TBIL (OR = 2.99), Pre-operative DBIL (OR = 2.35), Pre-operative potassium (OR = 6.8), Surgical type (OR = 3.76), Gastric tube removal time (OR = 3.48), Reconstruction mode (OR = 4.63) and Operative time (OR = 2.21). The model performed well in ROC, with AUC values of 0.892 in the training set, 0.858 in the inner validation set (Cohort 1), and 0.849 in the exterior validation set (Cohort 2). All three datasets' calibration curves revealed a high level of agreement between projected and actual probability. DCA suggested that the model had great clinical usefulness. We have established a more reliable predictive model for PGS which can provide guidance for clinical practitioners. Robotic surgery is also considered to be one of the factors that can reduce the occurrence of PGS.
术后胃轻瘫综合征（PGS）显著降低了手术患者的生活质量。随着机器人手术的发展，关于其是否能为胃癌提供一种新的治疗方式并降低胃瘫综合征的发生率存在争议。本研究利用机器学习技术和传统逻辑回归构建并验证预测模型，旨在为临床医生提供指导。本研究纳入了来自一个医疗中心的两个队列，根据手术分期时间划分（队列1：n = 619用于模型构建和内部验证；队列2：n = 312用于外部验证）。在队列1中，随机森林采用3:1的比例进行训练和验证，逻辑回归采用7:3的比例。在分析了受试者工作特征曲线（ROC）后，我们选择经典逻辑回归构建预测模型，随后通过校准和决策曲线分析（DCA）进行评估。最后，我们对队列2进行了外部验证。该模型纳入了7个因素，包括：术前总胆红素（OR = 2.99）、术前直接胆红素（OR = 2.35）、术前血钾（OR = 6.8）、手术类型（OR = 3.76）、胃管拔除时间（OR = 3.48）、重建方式（OR = 4.63）和手术时间（OR = 2.21）。该模型在ROC中表现良好，训练集的AUC值为0.892，内部验证集（队列1）为0.858，外部验证集（队列2）为0.849。所有三个数据集的校准曲线显示预测概率与实际概率之间高度一致。DCA表明该模型具有很大的临床实用性。我们建立了一个更可靠的PGS预测模型，可为临床医生提供指导。机器人手术也被认为是可降低PGS发生率的因素之一。
