Machine learning-based prediction model for arteriovenous fistula thrombosis risk: a retrospective cohort study from 2017 to 2024

作者：Peng Shu, Ling Huang, Xia Wang, Zhuping Wen, Ruben Yiqi Luo, Fang Xu · 发表于：BMC Nephrology · 年份：2025 · DOI：10.1186/s12882-025-04201-4 · 被引用次数：6 · 研究领域：Central Venous Catheters and Hemodialysis、Artificial Intelligence in Healthcare、Vascular Anomalies and Treatments

BACKGROUND: Thrombosis of arteriovenous fistulas represents a prevalent complication among patients undergoing hemodialysis, characterized by a notably high incidence rate. Presently, there is an absence of robust assessment tools capable of predicting thrombosis occurrence. This study seeks to develop an interpretable machine learning model to forecast the risk of arteriovenous fistula thrombosis. METHODS: Clinical data were retrospectively collected from 1,168 patients who received hemodialysis via arteriovenous fistulas at The Central Hospital of Wuhan between January 2017 and October 2024. A comprehensive analysis of 55 features was conducted utilizing Python. The dataset was partitioned into a training set and a test set, comprising 70% and 30% of the samples, respectively. Six distinct machine learning models-namely, Random Forest, Extreme Gradient Boosting, Decision Tree, Logistic Regression, K-Nearest Neighbors, and Naive Bayes-were constructed to predict the risk of thrombosis in arteriovenous fistulas. The performance of these models was assessed utilizing several metrics, including the F1 score, precision, specificity, accuracy, area under the receiver operating characteristic curve, and recall rate. The contribution of each feature within the most effective model was evaluated using SHAP values, and a specific case was selected to demonstrate the model's predictive capability. RESULTS: The study encompassed a cohort of 974 patients, each characterized by 55 clinic...