Predicting 1-year successful clinical use of an arteriovenous access for hemodialysis using machine learning

TL;DR

This study uses machine learning to predict whether an arteriovenous access for hemodialysis will be successfully used for one year, helping guide clinical decisions.

Contribution

A novel machine learning model (XGBoost) is developed to predict 1-year AV access success with higher accuracy than traditional logistic regression.

Findings

XGBoost achieved an AUROC of 0.90 in predicting 1-year successful AV access use.

Logistic regression had a lower performance with an AUROC of 0.70.

The model was trained on 111 pre-operative features from 59,674 patients.

Abstract

Arteriovenous (AV) access is important to support long-term hemodialysis; however, a significant proportion fail due to inadequate maturation or other complications. Tools that can predict long-term AV access success may guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year successful clinical use of an AV access using pre-operative data. The Vascular Quality Initiative (VQI) was used to identify patients who underwent surgical AV fistula/graft creation between 2011–2024. We identified 111 pre-operative demographic, clinical, and anatomic features. Six ML models were trained with 10-fold cross-validation. Overall, 59,674 patients underwent AV access creation and 28,304 (47.4%) had 1-year successful clinical use of their index AV access. The best prediction model was XGBoost, achieving an AUROC of 0.90. In comparison, logistic regression had an AUROC of 0.70. The XGBoost model accurately predicted 1-year successful clinical use of an AV access for hemodialysis, performing better than logistic regression.

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