Abstract:

Objective To develop and validate a risk warning model for autologous arteriovenous fistula（AVF） occlusion using multiple machine learning algorithms，aiming to provide a precise and efficient decision-support tool for early clinical identification of high-risk patients and guidance for nursing interventions. Methods A retrospective analysis was conducted using convenience sampling，including 403 patients undergoing hemodialysis at a tertiary Grade-A hospital in Jiangsu Province from August 2021 to January 2024. Patients were divided into an occlusion group（n=97） and a non-occlusion group（n=306） based on the occurrence of AVF occlusion during follow-up. Independent risk factors for AVF occlusion were first identified using univariable and binary logistic regression analyses. Subsequently，seven machine learning algorithms，including Random Forest，Support Vector Machine （SVM），and AdaBoost，were employed to construct prediction models. Model performance was evaluated and optimized using 10-fold cross-validation，and the best-performing model was interpreted using the SHAP（SHapley Additive exPlanations） algorithm. Results Logistic regression analysis revealed that AVF usage time（AVF time），a history of coronary artery disease（CAG），and AVF location were independent risk factors for occlusion（P<0.05）. Among the seven machine learning models，the AdaBoost and Random Forest models demonstrated the best overall predictive performance，with areas under the receiver operating characteristic（ROC） curve（AUC） of 0.882 and 0.876 on the test set，respectively. SHAP interpretability analysis indicated that AVF usage time，serum calcium level，history of diabetes mellitus（DM），and AVF location were the four most influential features for the model’s predictions. Conclusion The AVF occlusion risk warning models developed based on AdaBoost and Random Forest algorithms exhibit excellent discrimination and accuracy. They can effectively predict the risk of fistula occlusion in hemodialysis patients，providing a reliable basis for personalized nursing interventions.

Key words: Hemodialysis, Arteriovenous Fistula, Fistula Occlusion, Machine Learning, Prediction Model, Nursing Care