Native arteriovenous fistula (AVF) is the best vascular access (VA) for hemodialysis, but its creation remains challenging. We previously developed and validated in a controlled clinical study a patient-specific computational model to predict blood flow volume (BFV) in AVF for different surgical configurations on the basis of demographic and clinical data, as well as pre-operative ultrasound measurements. In the present research we aimed at investigating power of prediction and usability of the computational model in routine clinical setting.
We developed a web-based system (AVF.SIM) that integrates the computational model in a single procedure, including data collection and transfer, simulation management and data storage. An observational usability test was conducted to compare predicted vs. measured BFV and evaluate acceptance of the system in clinical routine. Complete data of 40 patients were collected at Ospedale Papa Giovanni XXIII, Bergamo and Ente Ecclesiastico “F. Miulli”, Acquaviva delle Fonti, Bari. All AVF was created in the lower arm (radiocephalic AVF), and the configuration was side-to-end or end-to-end.
Predicted brachial artery BFV at 40 days after surgery showed a good correlation with measured values (in average 739 ± 225 vs. 727 ± 214 mL/min, R = 0.75, p < 0.001). The mean difference (±SD) between predicted vs. measured BFV was -4.0 ± 22%, with 50% of predicted values in the range of 83 - 122% of measured BFV. Feedbacks provided by clinicians suggested that AVF.SIM is easy to use and well accepted in clinical routine, with limited additional workload.
Clinical use of computational modeling for AVF surgical planning can help the surgeon to select the best surgical strategy, reducing AVF early failures and complications. This approach allows individualization of VA care, with the aim to reduce the costs associated with VA dysfunction, and to improve AVF clinical outcome.
