EARLY ARTERIOVENOUS FISTULA FAILURE PREDICTION WITH ARTIFICIAL INTELLIGENCE: A NEW APPROACH WITH CHALLENGING RESULTS

The native arteriovenous fistula is considered the vascular access of preference, since it is directly related to major survival, reduced complications, mortality, and costs. Still, its proper maintenance remains a challenge for nephrologists. A previous study from our group, which used the data of 117 arteriovenous fistulas, led to identify in the multivariable analysis age and vein diameters as predictive factors for early failure. On the other hand, Artificial Intelligence has been established as a tool to identify relationships between variables at deep levels, which might be unseen with more conservative methods like classic statistics. Therefore, through a Machine Learning technique known as Random Forest, the aim is to evaluate the same comorbidity, biological and Doppler ultrasound variables data to identify those with a major relation with the early failure of the native arteriovenous fistula.