J Vasc Access. 2026 Mar 21:11297298261432153. doi: 10.1177/11297298261432153. Online ahead of print.
ABSTRACT
BACKGROUND: Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis, yet primary failure and early dysfunction remain common, thereby prolonging catheter dependence and increasing costs. Traditional prediction models show limited discrimination, and few studies have combined machine-learning (ML) methods with formal assessment of clinical utility. This study aimed to identify predictors of AVF failure and compare ML models with logistic regression in a Turkish hemodialysis cohort, and to derive a clinical risk score.
METHODS: In this retrospective study, 385 adults with native AVFs (171 failures, 214 patent) operated between January 2018 and November 2024 were analyzed. Demographic, clinical, and laboratory variables were extracted from electronic records. Standard logistic regression (GLM), penalized logistic regression (LASSO), Random Forest, and XGBoost models were fitted and evaluated using 10-fold cross-validation and an independent test set (75%/25% split). LASSO coefficients were translated into a hand-calculable clinical risk score.
RESULTS: Patients with AVF failure were older and had lower BMI, albumin, and creatinine than those with patent accesses. In multivariable logistic regression, higher urea and lower BMI and albumin independently predicted failure. On the test set, AUCs were 0.731 (GLM), 0.876 (LASSO), 0.918 (Random Forest), and 0.884 (XGBoost). Cross-validated mean AUCs ranged from 0.906 (GLM) to 0.950 (XGBoost), with Brier scores 0.079-0.116 and generally good calibration. A 30% risk threshold suggested a clinically reasonable balance between sensitivity and specificity, and decision-curve analysis showed net benefit for all ML models versus treat-all/none. The clinical risk score stratified patients into low (⩽3 points), intermediate (4-7), and high (⩾8) risk.
CONCLUSION: ML models, particularly ensemble methods, showed improved discrimination compared with standard logistic regression for predicting AVF failure and supported a simple, clinically usable risk score for stratified surveillance.
PMID:41863397 | DOI:10.1177/11297298261432153