Wang Wei, Yan Zhu, Zhang Zhanshuo, Zhang Qing, Jia Yuanyuan
Department of Pain, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi People's Hospital, Wuxi, China.
Emergency Medicine Department, The Affiliated Huai'an Hospital of Yangzhou University, Huai'an Fifth People's Hospital, Huai'an, China.
Discov Oncol. 2024 Sep 27;15(1):483. doi: 10.1007/s12672-024-01355-9.
Gastroparesis is a major complication following complete mesocolic excision (CME) and significantly impacts patient outcomes. This study aimed to create a machine learning model to pinpoint key risk factors before, during, and after surgery, effectively predicting the risk of gastroparesis after CME.
The study involved 1146 patients with colon cancer, out of which 95 developed gastroparesis. Data were collected on 34 variables, including demographics, chronic conditions, pre-surgery test results, types of surgery, and intraoperative details. Four machine learning techniques were employed: extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). The evaluation involved k-fold cross-validation, receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and external validation.
XGBoost excelled in its performance for predictive models. ROC analysis showed high accuracy for XGBoost, with area under the curve (AUC) scores of 0.976 for the training set and 0.906 for the validation set. K-fold cross-validation confirmed the model's stability, and calibration curves indicated high predictive accuracy. Additionally, DCA highlighted XGBoost's superior patient benefits for intervention treatments. An AUC of 0.77 in external validation demonstrated XGBoost's strong generalization ability.
The XGBoost-fueled predictive model for post-surgery colon cancer patients proved highly effective. It underlined gastroparesis as a significant post-operative issue, associated with advanced age, prolonged surgeries, extensive intraoperative blood loss, surgical techniques, low serum protein levels, anemia, diabetes, and hypothyroidism.
胃轻瘫是完整结肠系膜切除术（CME）后的主要并发症，对患者预后有重大影响。本研究旨在创建一个机器学习模型，以确定手术前、手术中和手术后的关键风险因素，从而有效预测CME后胃轻瘫的风险。
该研究纳入了1146例结肠癌患者，其中95例发生了胃轻瘫。收集了34个变量的数据，包括人口统计学信息、慢性病情况、术前检查结果、手术类型和术中细节。采用了四种机器学习技术：极端梯度提升（XGBoost）、随机森林（RF）、支持向量机（SVM）和k近邻（KNN）。评估包括k折交叉验证、受试者工作特征（ROC）分析、校准曲线、决策曲线分析（DCA）和外部验证。
XGBoost在预测模型性能方面表现出色。ROC分析显示XGBoost具有较高的准确性，训练集的曲线下面积（AUC）得分为0.976，验证集为0.906。k折交叉验证证实了模型的稳定性，校准曲线表明预测准确性较高。此外，DCA突出了XGBoost在干预治疗方面对患者的优越益处。外部验证中的AUC为0.77，表明XGBoost具有很强的泛化能力。
以XGBoost为基础的结肠癌术后患者预测模型被证明非常有效。它强调胃轻瘫是一个重要的术后问题，与高龄、手术时间延长、术中大量失血、手术技术、低血清蛋白水平、贫血、糖尿病和甲状腺功能减退有关。
