INTRODUCTION Autogenous arteriovenous fistula (AVF) is the preferred vascular access in patients undergoing maintenance hemodialysis (MHD). However, complications such as thrombosis may occur. This study aimed to construct and validate a machine learning-based risk-prediction model for AVF thrombosis, hypothesizing that such a model can effectively predict occurrences, providing a foundation for early clinical intervention. METHODS The retrospective longitudinal study included a total of 270 patients who underwent MHD at the Hemodialysis Center of the Second Affiliated Hospital of Harbin Medical University between March 2021 and December 2022. During this study, baseline data and scale information of patients between March 2020 and December 2021 were collected. We recorded outcome indicators between March 2021 and December 2022 for subsequent analyses. Five machine learning models were developed (artificial neural network, logistic regression, ridge classification, random forest, and adaptive boosting). The sensitivity (recall), specificity, accuracy, and precision of each model were evaluated. The effect size of each variable was analyzed and ranked. Models were assessed using the area under the receiver-operating characteristic (AUROC) curve. RESULTS Among the 270 included patients, 105 had AVF thrombosis (55 male and 50 female patients; age range, 29-79 years; mean age, 56.72 years; standard deviation [SDs], ±13.10 years). Conversely, 165 patients did not have AVF thrombosis (99 male and 66 female patients; age range, 23-79 years; mean age, 53.58 years; SD, ± 13.33 years). During the observation period, approximately 52.6% of patients with AVF experienced long-term complications. The most common complications associated with AVF were thrombosis (105; 38.9%), aneurysm formation (27; 10%), and excessively high output flow (10; 3.7%). Fifty-four (20%) patients with AVF required intervention because of complications associated with vascular access. The AUROC curve of the testing set was between 0.858 and 0.903. CONCLUSION In this study, we developed five machine learning models to predict the risk of AVF thrombosis, providing a reference for early clinical intervention.

中文翻译：

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维持性血液透析患者自体动静脉瘘血栓形成风险预测模型的构建。


简介 自体动静脉内瘘 (AVF) 是维持性血液透析 (MHD) 患者的首选血管通路。但可能会出现血栓等并发症。本研究旨在构建并验证基于机器学习的 AVF 血栓形成风险预测模型，假设该模型可以有效预测发生情况，为早期临床干预提供基础。方法采用回顾性纵向研究，纳入2021年3月至2022年12月在哈尔滨医科大学第二附属医院血液透析中心接受MHD的患者270例。研究中，采用2020年3月至12月患者的基线数据和量表信息。 2021 年已收集。我们记录了 2021 年 3 月至 2022 年 12 月期间的结果指标，以供后续分析。开发了五种机器学习模型（人工神经网络、逻辑回归、岭分类、随机森林和自适应提升）。评估了每个模型的敏感性（召回率）、特异性、准确性和精密度。对每个变量的效应大小进行了分析和排序。使用接受者操作特征（AUROC）曲线下面积评估模型。结果 在 270 名患者中，105 名患有 AVF 血栓（55 名男性和 50 名女性患者；年龄范围，29-79 岁；平均年龄，56.72 岁；标准差 [SD]，±13.10 岁）。相反，165 名患者没有 AVF 血栓形成（99 名男性和 66 名女性患者；年龄范围，23-79 岁；平均年龄，53.58 岁；SD，± 13.33 岁）。观察期间，约 52.6% 的 AVF 患者出现长期并发症。与 AVF 相关的最常见并发症是血栓形成 (105; 38.9%）、动脉瘤形成（27；10%）和输出流量过高（10；3.7%）。 54 名 (20%) AVF 患者由于与血管通路相关的并发症而需要干预。测试集的AUROC曲线在0.858到0.903之间。结论 在本研究中，我们开发了五种机器学习模型来预测 AVF 血栓形成的风险，为早期临床干预提供参考。