Arteriovenous (AV) fistulas, the preferred vascular access for hemodialysis, fail to mature in up to 60% of patients with kidney failure. This high failure rate is often attributed to adverse hemodynamic conditions, yet the exact mechanisms remain poorly understood. This review explores the application of computational fluid dynamics (CFD) and machine learning (ML) to elucidate these mechanisms and predict clinical outcomes. CFD models have been instrumental in characterizing the complex interplay between AV fistula geometry, such as anastomotic angle and curvature, and hemodynamic parameters, such as wall shear stress (WSS) and oscillatory shear index (OSI). These studies consistently link disturbed flow patterns, including low WSS and high OSI, to regions prone to neointimal hyperplasia and stenosis. Concurrently, ML models have demonstrated significant promise in predicting AV fistula maturation, stenosis, and failure by leveraging diverse data sources, including clinical characteristics, ultrasound imaging, and acoustic bruit analysis. While powerful, the clinical utility of these computational models is often limited by small, single-center datasets, a lack of external validation, and simplifying assumptions that may not capture true physiological complexity. Future progress depends on integrating these complementary approaches, utilizing larger and more diverse datasets, and validating models prospectively to create generalizable tools that can guide surgical planning and improve AV fistula maturation rates.
Nowacki et al. (Fri,) studied this question.