An Tao, Han Han, Xie Junying, Wang Yifan, Zhao Yiqi, Jia Hao, Wang Yanfeng
Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Cardiac Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
BMC Cancer. 2025 Mar 27;25(1):552. doi: 10.1186/s12885-025-13946-y.
Catheter-related thrombosis (CRT) is a serious complication in cancer patients undergoing chemotherapy, yet existing risk prediction models demonstrate limited accuracy. This study aimed to evaluate the clinical utility of machine learning (ML) and Bayesian-learning models for CRT prediction in a large cohort of breast cancer patients undergoing catheterization.
A total of 3337 breast cancer patients with central venous catheters (Cohort 1) were included to develop and test ML models. Given the suboptimal clinical feasibility of ML models, the Bayesian-learning model was constructed using odds ratio analysis and Gaussian distribution. The hazard ratio for the high-risk and low-risk groups was calculated using Cox proportional hazards regression analysis, and the model was validated in an independent cohort of 1274 patients (Cohort 2).
In Cohort 1, 246 patients (7.37%) developed CRT. Among the eight ML algorithms tested, WeightedEnsemble model exhibited relatively stable performance, achieving area under the receiver operating characteristic curves of 0.89 in the training set and 0.69 in the test set. WeightedEnsemble improved generalization by integrating multiple base models. The odds ratio analysis and Bayesian-learning modeling identified 4 independent risk factors: hemoglobin (threshold point [TP]: 134.63 g/L), activated partial thromboplastin time (TP: 31.71 s), total cholesterol (TP: 11.19 mmol/L), and catheterization approach (TP: peripherally inserted central catheters). A simplified risk stratification system was developed, categorizing patients into low-risk (0-1 factors) and high-risk (2-4 factors) groups. This system exhibited strong CRT risk discriminative ability, as confirmed through survival analysis (P < 0.001 in both cohorts). In Cohort 1, cox regression analysis showed that the high-risk group had hazard ratio (HR) of 1.60 (95% confidence interval [CI], 1.15-2.22) for both catheter indwelling time and catheter use duration. In Cohort 2, the system maintained stable discriminative ability, with an HR of 5.63 (95% CI, 3.46-9.21) for catheter indwelling time and 5.62 (95% CI, 3.46-9.12) for catheter use duration.
While ML models demonstrated high predictive performance, their clinical applicability was limited due to complexity. The Bayesian-learning-based risk stratification model provided a simplified yet robust alternative, effectively predicting CRT risk and offering a clinically feasible tool for risk assessment in breast cancer patients with chemotherapy. Further validation in diverse cancer populations is warranted to refine its generalizability.
导管相关血栓形成（CRT）是接受化疗的癌症患者的一种严重并发症，但现有的风险预测模型准确性有限。本研究旨在评估机器学习（ML）和贝叶斯学习模型在一大群接受导管插入术的乳腺癌患者中预测CRT的临床效用。
共纳入3337例有中心静脉导管的乳腺癌患者（队列1）以开发和测试ML模型。鉴于ML模型临床可行性欠佳，使用比值比分析和高斯分布构建贝叶斯学习模型。使用Cox比例风险回归分析计算高风险和低风险组的风险比，并在1274例患者的独立队列（队列2）中对该模型进行验证。
在队列1中，246例患者（7.37%）发生了CRT。在所测试的8种ML算法中，加权集成模型表现出相对稳定的性能，在训练集中受试者操作特征曲线下面积为0.89，在测试集中为0.69。加权集成通过整合多个基础模型提高了泛化能力。比值比分析和贝叶斯学习建模确定了4个独立危险因素：血红蛋白（阈值点[TP]：134.63 g/L）、活化部分凝血活酶时间（TP：31.71 s）、总胆固醇（TP：11.19 mmol/L）和导管插入方法（TP：经外周静脉穿刺中心静脉置管）。开发了一种简化的风险分层系统，将患者分为低风险（0 - 1个因素）和高风险（2 - 4个因素）组。通过生存分析证实，该系统具有很强的CRT风险判别能力（两个队列中P均<0.001）。在队列1中，Cox回归分析显示，高风险组在导管留置时间和导管使用持续时间方面的风险比（HR）为1.60（95%置信区间[CI]，1.15 - 2.22）。在队列2中，该系统保持了稳定判别能力，导管留置时间的HR为5.63（95% CI，3.46 - 9.21），导管使用持续时间的HR为5.62（95% CI，3.46 - 9.12）。
虽然ML模型显示出较高的预测性能，但其临床适用性因复杂性而受限。基于贝叶斯学习构建的风险分层模型提供了一种简化但强大的替代方案，能有效预测CRT风险，并为接受化疗的乳腺癌患者提供了一种临床可行的风险评估工具。有必要在不同癌症人群中进一步验证以完善其通用性。
