Work

Development of a Machine Learning-Based Predictive Model for Arteriovenous Fistula Occlusion After Surgery: A Retrospective Cohort Study from 2015 to 2025

Year: 2025

Type: article

Abstract: Background and Objectives: Arteriovenous fistula (AVF) occlusion remains a major cause of vascular access failure in hemodialysis patients. Early identification of high-risk patients may help prevent complications and improve outcomes. Materials and Methods: This retrospective cohort study included 1498 adult patients who underwent AVF creation between 2015 and 2025 at Daegu Catholic University Medical Center. Clinical, surgical, and laboratory variables were used to develop machine learning (ML) models for predicting AVF occlusion. Five algorithms—LightGBM, CatBoost, XGBoost, Random Forest, and Logistic Regression—were trained and evaluated using stratified five-fold cross-validation. Model performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and calibration. SHAP (Shapley Additive Explanations) analysis was used to interpret variable importance. Results: Among the 1498 patients, 381 (25.4%) experienced AVF occlusion. LightGBM achieved the best performance (AUC = 0.887, accuracy = 0.858, specificity = 0.950), followed by CatBoost (AUC = 0.882) and XGBoost (AUC = 0.879). Calibration analysis demonstrated strong agreement between predicted and observed outcomes. SHAP analysis identified ferritin, hemoglobin, neutrophil percentage, and C-reactive protein as the most influential predictors, highlighting the role of inflammation and hematologic status in AVF failure. Conclusions: Gradient boosting-based ML models, particularly LightGBM and CatBoost, accurately predict AVF occlusion using routine clinical data. Explainable AI methods enhance interpretability, enabling early identification of high-risk patients and supporting precision vascular access management in hemodialysis care.

Source: Medicina

DOI: 10.3390/medicina61122150

Authors: Jae Hoon Lee, Sang Gyu Kwak.

Institutions: Daegu Catholic University, Daegu Catholic University Medical Center

Language: English

Topic: Central Venous Catheters and Hemodialysis

Subfield: Emergency Medical Services

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,654