术后静脉血栓栓塞事件（VTE），例如下肢深静脉血栓（DVT），是胃癌（GC）患者根治性胃切除术后的主要危险因素。准确预测和管理这些风险对于最佳患者护理至关重要。 这项回顾性病例对照研究纳入了我院693例接受根治性胃切除术的胃癌患者。我们收集了丰富而全面的临床指标，包括总共49项基线、术前、手术和病理临床数据。使用单变量逻辑回归，我们确定了潜在的风险因素，然后通过 Boruta 算法进行特征选择。然后，我们使用多元逻辑回归构建了最终的预测模型，并使用受试者工作特征（ROC）曲线分析、校准图、决策曲线分析和其他方法对其进行评估。此外，我们应用了各种机器学习技术，包括决策树和随机森林，来评估我们模型的预测强度。 这项回顾性病例对照研究纳入了我院693例接受根治性胃切除术的胃癌患者。我们收集了丰富而全面的临床指标，包括总共49项基线、术前、手术和病理临床数据。使用单变量逻辑回归，我们确定了潜在的风险因素，然后通过 Boruta 算法进行特征选择。然后，我们使用多元逻辑回归构建了最终的预测模型，并使用受试者工作特征（ROC）曲线分析、校准图、决策曲线分析和其他方法对其进行评估。此外，我们应用了各种机器学习技术，包括决策树和随机森林，来评估我们模型的预测强度。 单变量 Logistic 分析揭示了与术后下肢 DVT 相关的 14 个危险因素。基于Boruta算法，选择了6个重要的临床因素，即年龄、D-二聚体（D-D）水平、低密度脂蛋白、CA125以及钙和氯离子水平。使用多元逻辑回归分析的结果开发了列线图。预测模型显示出较高的准确性，训练集的曲线下面积为 0.936，验证集的曲线下面积为 0.875。各种机器学习算法证实了其强大的预测能力。 MR 分析揭示了关键临床因素与 DVT 风险之间有意义的因果关系。 基于各种机器学习方法，我们开发了一种有效的GC患者术后下肢DVT预测诊断模型。该模型在训练和验证集中都表现出了出色的预测价值。这种新颖的模型是临床医生识别和管理该患者群体血栓形成风险的宝贵工具。© 2024。作者。

Postoperative venous thromboembolic events (VTEs), such as lower extremity deep vein thrombosis (DVT), are major risk factors for gastric cancer (GC) patients following radical gastrectomy. Accurately predicting and managing these risks is crucial for optimal patient care. This retrospective case‒control study involved 693 GC patients from our hospital who underwent radical gastrectomy. We collected plentiful and comprehensive clinical indicators including a total of 49 baseline, preoperative, surgical and pathological clinical data. Using univariate logistic regression, we identified potential risk factors, followed by feature selection through the Boruta algorithm. We then constructed the final predictive model using multivariate logistic regression and evaluated it using receiver operating characteristic (ROC) curve analysis, calibration plots, decision curve analysis, and other methods. Additionally, we applied various machine learning techniques, including decision trees and random forests, to assess our model's predictive strength. This retrospective case‒control study involved 693 GC patients from our hospital who underwent radical gastrectomy. We collected plentiful and comprehensive clinical indicators including a total of 49 baseline, preoperative, surgical and pathological clinical data. Using univariate logistic regression, we identified potential risk factors, followed by feature selection through the Boruta algorithm. We then constructed the final predictive model using multivariate logistic regression and evaluated it using receiver operating characteristic (ROC) curve analysis, calibration plots, decision curve analysis, and other methods. Additionally, we applied various machine learning techniques, including decision trees and random forests, to assess our model's predictive strength. Univariate logistic analysis revealed 14 risk factors associated with postoperative lower limb DVT. Based on the Boruta algorithm, six significant clinical factors were selected, namely, age, D-dimer (D-D) level, low-density lipoprotein, CA125, and calcium and chloride ion levels. A nomogram was developed using the outcomes from the multivariate logistic regression analysis. The predictive model showed high accuracy, with an area under the curve of 0.936 in the training set and 0.875 in the validation set. Various machine learning algorithms confirmed its strong predictive capacity. MR analysis revealed meaningful causal relationships between key clinical factors and DVT risk. Based on various machine learning methods, we developed an effective predictive diagnostic model for postoperative lower extremity DVT in GC patients. This model demonstrated excellent predictive value in both the training and validation sets. This novel model is a valuable tool for clinicians to use in identifying and managing thrombotic risks in this patient population.© 2024. The Author(s).