Dong Zhiyong, Guo Lianrui, Tong Zhu, Cui Shijun, Gao Xixiang, Zhang Chengchao, Guo Jianming, Gu Yongquan
Department of Cardiothoracic Surgery, Beijing Shunyi Hospital, Beijing, China.
Vascular Department, Xuanwu Hospital, Capital Medical University, Beijing, China.
Sci Rep. 2025 Aug 25;15(1):31310. doi: 10.1038/s41598-025-17283-9.
Isolated atherosclerotic popliteal artery lesions (IAPAL) commonly require interventional treatment. This study aimed to develop a prediction model using procedure-related variables for one-year clinically driven target lesion revascularization (CDTLR) events after intervention. Clinical data were retrospectively collected from 217 patients who underwent endovascular treatment for isolated atherosclerotic popliteal artery lesions between 2017 and 2022. Based on inclusion and exclusion criteria, all patients were randomly divided into training and testing sets at a ratio of 7:3. In the training set, LASSO regression, logistic regression, and random forest were used to identify the most significant variables for outcome events. These variables were then incorporated into a multivariate logistic regression model. The prediction model was visualized using a nomogram and validated using training and testing sets. The final nomogram consisted of three independent predictors: body weight, drug-coating balloon angioplasty, and post-procedural outflow score. The regression equation was: Y = 3.65–0.0645×weight − 1.04×(DCB = use) − 1.21×(post-procedural outflow score = 2) − 0.465×(post-procedural outflow score = 3). The prediction model demonstrated C-indices of 0.756 and 0.689 in the training and validation cohorts, respectively. Calibration curves showed satisfactory agreement in both cohorts. The prediction model incorporating body weight, drug-coating balloon angioplasty, and post-procedural outflow score may assist in predicting one-year clinically driven target lesion revascularization in patients with isolated atherosclerotic popliteal artery lesions, providing valuable information for individualized treatment strategies.
The online version contains supplementary material available at 10.1038/s41598-025-17283-9.
孤立性动脉粥样硬化性腘动脉病变（IAPAL）通常需要介入治疗。本研究旨在利用与手术相关的变量开发一个预测模型，以预测介入治疗后一年基于临床驱动的靶病变血运重建（CDTLR）事件。回顾性收集了2017年至2022年间接受孤立性动脉粥样硬化性腘动脉病变血管内治疗的217例患者的临床数据。根据纳入和排除标准，所有患者按7:3的比例随机分为训练集和测试集。在训练集中，使用LASSO回归、逻辑回归和随机森林来确定结局事件的最显著变量。然后将这些变量纳入多变量逻辑回归模型。使用列线图对预测模型进行可视化，并使用训练集和测试集进行验证。最终的列线图由三个独立预测因素组成：体重、药物涂层球囊血管成形术和术后流出道评分。回归方程为：Y = 3.65 - 0.0645×体重 - 1.04×（DCB = 使用） - 1.21×（术后流出道评分 = 2） - 0.465×（术后流出道评分 = 3）。该预测模型在训练队列和验证队列中的C指数分别为0.756和0.689。校准曲线在两个队列中均显示出良好的一致性。纳入体重、药物涂层球囊血管成形术和术后流出道评分的预测模型可能有助于预测孤立性动脉粥样硬化性腘动脉病变患者一年基于临床驱动的靶病变血运重建，为个体化治疗策略提供有价值的信息。
在线版本包含可在10.1038/s41598-025-17283-9获取的补充材料。
