Surgical planning of arteriovenous fistulae in routine clinical practice: a machine learning predictive tool

Background: Arteriovenous fistula (AVF) is the preferred vascular access (VA) for hemodialysis, but it is associated with high non-maturation and failure rates. Predicting patient-specific AVF maturation and postoperative changes in blood flow volumes (BFVs) and vessel diameters is of fundamental importance to support the choice of optimal AVF location and improve VA survival. The goal of this study was to employ machine learning (ML) in order to give physicians a fast and easy-to-use tool that provides accurate patient-specific predictions, useful to make AVF surgical planning decisions. Methods: We applied a set of ML approaches on a dataset of 156 patients. Both parametric and non-parametric ML approaches, taking preoperative data as input, were exploited to predict maturation, postoperative BFVs, and diameters. The best approach associated with lowest cross-validation errors between predictions and real measurements was then chosen to provide estimates and quantify prediction errors. Results: The k-NN was the best approach to predict brachial BFV, AVF maturation, and other VA variables, and it was also associated with the least computational effort. With this approach, the confusion matrices proved the high accuracy of the prediction for AVF maturation (96.8%) and the low absolute error distribution for the continuous BFV and diameter variables. Conclusions: Our data-based approach provided accurate patient-specific predictions for different AVF configurations, requiring short computational time as compared to a physical model we previously developed. By supporting VA surgical planning, this fast computing approach could allow AVF surgical planning and help reducing the rate of non-maturation, which might ultimately have a broad impact on the management of hemodialysis patients.