Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis

Abstract

BackgroundAutogenous arteriovenous fistula (AVF) is the best vascular access (VA) for hemodialysis, but its creation is still a critical procedure. Physical examination, vascular mapping and doppler ultrasound (DUS) evaluation are recommended for AVF planning, but they can not provide direct indication on AVF outcome. We recently developed and validated in a clinical trial a patient-specific computational model to predict pre-operatively the blood flow volume (BFV) in AVF for different surgical configuration on the basis of demographic, clinical and DUS data. In the present investigation we tested power of prediction and usability of the computational model in routine clinical setting.MethodsWe 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. A usability test on observational data was designed to compare predicted vs. measured BFV and evaluate the acceptance of the system in the clinical setting. Six Italian nephrology units were involved in the evaluation for a 6-month period that included all incident dialysis patients with indication for AVF surgery.ResultsOut of the 74 patients, complete data from 60 patients were included in the final dataset. Predicted brachial BFV at 40 days after surgery showed a good correlation with measured values (in average 787 ± 306 vs. 751 ± 267 mL/min, R = 0.81, p < 0.001). For distal AVFs the mean difference (±SD) between predicted vs. measured BFV was −2.0 ± 20.9%, with 50% of predicted values in the range of 86–121% of measured BFV. Feedbacks provided by clinicians indicate that AVF.SIM is easy to use and well accepted in clinical routine, with limited additional workload.ConclusionsClinical 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0420-x) contains supplementary material, which is available to authorized users.

The complex relationship among arteriovenous access, heart, and circulation

“…A patient‐specific hemodynamic computational model, using preoperative ultrasound measurements together with other input variables, has been recently validated to predict the increase in Qa after AVF placement . The clinical use of this computational model may facilitate individualization of the vascular access and a more patient‐centered approach . A collaborative decision‐making process with involvement of cardiologists in the vascular access choice in patients at risk should be strongly considered …”

Pre-operative Patient Specific Flow Predictions to Improve Haemodialysis Arteriovenous Fistula Maturation (Shunt Simulation Study): A Randomised Controlled Trial

“…From recent studies the computational model has been found to reliably predict immediate post-operative blood flows and could therefore be useful for clinical decision support. 12,13,21 Unfortunately, in this randomised clinical trial maturation rates were not improved by providing flow predictions during surgical planning. Despite an overall accurate flow prediction, the FTM rates were around 30% in both groups.…”

“…R 2 ¼ .66) in 60 patients for flow prediction at 40 days. 21 This difference can be explained by the controlled study setting in which Caroli et al attained their input, whereas in Bozzetto et al input data was more representative for daily practice.…”

Machine-readable Yin–Yang imbalance: traditional Chinese medicine syndrome computer modeling based on three-dimensional noninvasive cardiac electrophysiology imaging

“…Diagnosis and treatment in TCM are mainly based on syndrome differentiation. Multidisciplinary computational modeling approaches 43–48 have become valuable tools for diagnosis, therapy, and prevention of diseases in the clinical setting, but these tools have not been used in TCM syndrome differentiation to date. Because correct syndrome differentiation is fundamental for effective treatment of diseases, this project is being performed to develop an application-oriented, machine-readable TCM Yin–Yang syndrome computer modeling system for cardiovascular disease with the aid of a noninvasive cardiac electrophysiology imaging technique.…”