Determining whether to proceed with a clinical intervention can be a challenging endeavor due to the potentially numerous variables at play. One of the most crucial piece of information for making this decision is a precise assessment of the intervention's effectiveness, but it tends to be a complex calculation for healthcare professionals. In hemodialysis patients, the presence of a functional arteriovenous fistula (AVF) is essential to achieve a sufficient dialysis dosage and prevent various complications. Percutaneous transluminal angioplasty (PTA) is a commonly employed procedure to restore the patency of AVFs. However, it carries the disadvantage of causing long-term vessel damage, thereby reducing the lifespan of the AVF. In this preliminary study we explore Dynamic Bayesian Network (DBN) to estimate the effectiveness of the next PTA from the elaboration of routinely collected clinical data. We build a DBN to predict the risk of problems of AVF and simulate how the next PTA could impact this prediction. The outcomes of this research could potentially contribute to the development of a decision support system for vascular surgeons, aiding in the optimization of the decision-making process regarding whether to proceed with a PTA and/or consider alternative solutions.