Ju Jianjie, Chen Jingjing, Wang Jingting, Yang Limei
The School of Pharmacy, Fujian Medical University, Fuzhou, Fujian, China.
Department of Pharmacy, Provincial Clinical College of Fujian Medical University/Fujian Provincial Hospital/Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.
Clin Appl Thromb Hemost. 2025 Jan-Dec;31:10760296251375842. doi: 10.1177/10760296251375842. Epub 2025 Sep 2.
Deep venous thrombosis (DVT) is a leading cause of cardiovascular-related mortality, with an increasing incidence in elderly patients. However, existing risk assessment tools remain limited for this population. This study aimed to develop and validate machine learning (ML)-based models for predicting DVT risk in elderly patients. We retrospectively analyzed data from 1226 elderly patients discharged from the cardiovascular surgery department between January 2022 and December 2023. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO), and seven ML models were subsequently trained on the selected features. Optimal hyperparameters for each model were selected through grid search with ten-fold cross-validation. Logistic regression (LR) and random forest (RF) demonstrated the best performance, with areas under the receiver operating characteristic curve (AUCs) of 0.835 and 0.819, respectively. SHapley Additive exPlanations (SHAP) revealed swelling, pain, albumin (ALB), and D-dimer as key predictors. These models may facilitate accurate risk stratification in elderly patients and provide clinical decision support through an interactive web-based tool.
深静脉血栓形成（DVT）是心血管相关死亡的主要原因，在老年患者中的发病率呈上升趋势。然而，现有的风险评估工具对此类人群的作用仍然有限。本研究旨在开发并验证基于机器学习（ML）的模型，以预测老年患者的DVT风险。我们回顾性分析了2022年1月至2023年12月间从心血管外科出院的1226例老年患者的数据。使用最小绝对收缩和选择算子（LASSO）识别风险因素，随后基于所选特征训练了七个ML模型。通过十折交叉验证的网格搜索为每个模型选择了最佳超参数。逻辑回归（LR）和随机森林（RF）表现最佳，受试者操作特征曲线（AUC）下面积分别为0.835和0.819。SHapley加性解释（SHAP）显示肿胀、疼痛、白蛋白（ALB）和D-二聚体是关键预测因素。这些模型可能有助于对老年患者进行准确的风险分层，并通过基于网络的交互式工具提供临床决策支持。
