Liu Yuan, Zhao Songyun, Du Wenyi, Shen Wei, Zhou Ning
Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Front Med (Lausanne). 2025 Jan 6;11:1467565. doi: 10.3389/fmed.2024.1467565. eCollection 2024.
Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinder postoperative recovery and diminish the patient's quality of life. In the present study, four advanced machine learning algorithms-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and -nearest neighbor (KNN)-were employed to develop predictive models. The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.
We gathered 34 feature variables from a cohort of 1,097 colon cancer patients, including 87 individuals who developed gastroparesis post-surgery, across multiple hospitals, and applied a range of machine learning algorithms to construct the predictive model. To assess the model's generalization performance, we employed 10-fold cross-validation, while the receiver operating characteristic (ROC) curve was utilized to evaluate its discriminative capacity. Additionally, calibration curves, decision curve analysis (DCA), and external validation were integrated to provide a comprehensive evaluation of the model's clinical applicability and utility.
Among the four predictive models, the XGBoost algorithm demonstrated superior performance. As indicated by the ROC curve, XGBoost achieved an area under the curve (AUC) of 0.939 in the training set and 0.876 in the validation set, reflecting exceptional predictive accuracy. Notably, in the -fold cross-validation, the XGBoost model exhibited robust consistency across all folds, underscoring its stability. The calibration curve further revealed a favorable concordance between the predicted probabilities and the actual outcomes of the XGBoost model. Additionally, the DCA highlighted that patients receiving intervention under the XGBoost model experienced significantly greater clinical benefit.
The onset of postoperative gastroparesis in colon cancer patients remains an elusive challenge to entirely prevent. However, the prediction model developed in this study offers valuable assistance to clinicians in identifying key high-risk factors for gastroparesis, thereby enhancing the quality of life and survival outcomes for these patients.
完整结肠系膜切除术（CME）后发生的胃轻瘫可引发一系列严重并发症，这可能会显著阻碍术后恢复并降低患者的生活质量。在本研究中，采用了四种先进的机器学习算法——极端梯度提升（XGBoost）、随机森林（RF）、支持向量机（SVM）和K近邻（KNN）——来开发预测模型。对CME术后转入重症监护病房（ICU）的危重症患者的临床数据进行了细致分析，以确定与胃轻瘫发生相关的关键风险因素。
我们从多家医院的1097例结肠癌患者队列中收集了34个特征变量，其中包括87例术后发生胃轻瘫的患者，并应用一系列机器学习算法构建预测模型。为评估模型的泛化性能，我们采用了10折交叉验证，同时利用受试者工作特征（ROC）曲线评估其判别能力。此外，还结合了校准曲线、决策曲线分析（DCA）和外部验证，以全面评估模型的临床适用性和实用性。
在四种预测模型中，XGBoost算法表现出卓越的性能。如ROC曲线所示，XGBoost在训练集中的曲线下面积（AUC）为0.939，在验证集中为0.876，反映出其出色的预测准确性。值得注意的是，在10折交叉验证中，XGBoost模型在所有折次中均表现出稳健的一致性，凸显了其稳定性。校准曲线进一步显示，XGBoost模型的预测概率与实际结果之间具有良好的一致性。此外，DCA表明，在XGBoost模型下接受干预的患者获得了显著更大的临床益处。
结肠癌患者术后胃轻瘫的发生仍然是一个难以完全预防的挑战。然而，本研究中开发的预测模型为临床医生识别胃轻瘫的关键高危因素提供了有价值的帮助，从而提高了这些患者的生活质量和生存结局。
