Fu Maoling, Li Xinyu, Wang Zhuo, Yang Qiaoyue, Yu Genzhen
Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Wuhan, Hubei 430030, China; School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, 13 Aviation Road, Wuhan, Hubei 430030, China.
Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Wuhan, Hubei 430030, China.
Thromb Res. 2025 Mar;247:109276. doi: 10.1016/j.thromres.2025.109276. Epub 2025 Jan 28.
Identifying independent risk factors and implementing high-quality assessment tools for early detection of patients at high risk of central venous access device (CVAD)-related thrombosis (CRT) plays a critical role in delivering timely preventive interventions and reducing the incidence of CRT. Approaches for identifying the risk of CRT in children have not been well-researched.
To identify the critical risk factors for CRT in children and to construct machine learning-based prediction models tailored to this group, providing a theoretical basis and technical support for the prediction and prevention of CRT in these patients.
Retrospective data of pediatric patients receiving CVAD catheterization from January 1, 2018 to June 31, 2023 in Tongji Hospital were collected and divided into a training set and an internal validation set in a ratio of 7:3. Relevant data from July 1, 2023 to July 1, 2024 were prospectively collected for external validation of the model. LASSO regression was applied to determine CRT independent risk factors. Subsequently, four prediction models were constructed using logistic regression (LR), random forest, artificial neural network, and eXtreme Gradient Boosting.
A total of 1445 children were included in this study and the overall incidence of CRT was 17.4 %. The LASSO regression screened out 11 critical variables, including history of thrombosis, leukemia, number of catheters, history of catheterization, chemotherapy, parenteral nutrition, mechanical prophylaxis, dialysis, hypertonic liquid, anticoagulants, and post-catheterization D-dimer. The LR model outperformed the other models in both internal and external validation and was considered the best model for this study, which was transformed into a nomogram.
This study identified 11 independent risk factors for CRT in children. The prediction model developed using LR algorithm demonstrated excellent clinical applicability and may provide valuable support for early prediction of CRT.
识别独立危险因素并应用高质量评估工具对中心静脉置管装置（CVAD）相关血栓形成（CRT）高危患者进行早期检测，对于及时实施预防干预措施和降低CRT发生率至关重要。儿童CRT风险识别方法尚未得到充分研究。
识别儿童CRT的关键危险因素，并构建针对该群体的基于机器学习的预测模型，为这些患者CRT的预测和预防提供理论依据和技术支持。
收集2018年1月1日至2023年6月31日在同济医院接受CVAD置管的儿科患者的回顾性数据，并按7:3的比例分为训练集和内部验证集。前瞻性收集2023年7月1日至2024年7月1日的相关数据用于模型的外部验证。应用LASSO回归确定CRT独立危险因素。随后，使用逻辑回归（LR）、随机森林、人工神经网络和极端梯度提升构建四个预测模型。
本研究共纳入1445名儿童，CRT总体发生率为17.4%。LASSO回归筛选出11个关键变量，包括血栓形成史、白血病、导管数量、置管史、化疗、肠外营养、机械预防、透析、高渗液体、抗凝剂以及置管后D-二聚体。LR模型在内部和外部验证中均优于其他模型，被认为是本研究的最佳模型，并转化为列线图。
本研究确定了儿童CRT的11个独立危险因素。使用LR算法开发的预测模型具有出色的临床适用性，可为CRT的早期预测提供有价值的支持。
