Bozzetto Michela, Rota Stefano, Vigo Valentina, Casucci Francesco, Lomonte Carlo, Morale Walter, Senatore Massimo, Tazza Luigi, Lodi Massimo, Remuzzi Giuseppe, Remuzzi Andrea
Department of Biomedical Engineering, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Bergamo, Italy.
Unit of Nephrology and Dialysis, ASST Papa Giovanni XXIII, Bergamo, Italy.
BMC Med Inform Decis Mak. 2017 Mar 14;17(1):26. doi: 10.1186/s12911-017-0420-x.
Autogenous 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.
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. 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.
Out 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.
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.
自体动静脉内瘘（AVF）是血液透析的最佳血管通路（VA），但其创建仍是一个关键程序。建议在规划AVF时进行体格检查、血管造影和多普勒超声（DUS）评估，但它们无法直接指示AVF的结果。我们最近在一项临床试验中开发并验证了一种患者特异性计算模型，该模型可根据人口统计学、临床和DUS数据术前预测不同手术配置下AVF的血流量（BFV）。在本研究中，我们测试了该计算模型在常规临床环境中的预测能力和可用性。
我们开发了一个基于网络的系统（AVF.SIM），该系统将计算模型集成在一个单一程序中，包括数据收集和传输、模拟管理和数据存储。设计了一项关于观察数据的可用性测试，以比较预测的BFV与测量的BFV，并评估该系统在临床环境中的可接受性。六个意大利肾脏病单位参与了为期6个月的评估，该评估包括所有有AVF手术指征的新发透析患者。
在74例患者中，最终数据集中纳入了60例患者的完整数据。术后40天预测的肱动脉BFV与测量值显示出良好的相关性（平均分别为787±306和751±267mL/min，R = 0.81，p < 0.001）。对于远端AVF，预测的BFV与测量的BFV之间的平均差异（±SD）为-2.0±20.9%，50%的预测值在测量BFV的86-121%范围内。临床医生提供的反馈表明，AVF.SIM易于使用，在临床常规中得到了很好的接受，额外工作量有限。
将计算模型用于AVF手术规划的临床应用可以帮助外科医生选择最佳手术策略，减少AVF早期失败和并发症。这种方法允许血管通路护理的个体化，旨在降低与血管通路功能障碍相关的成本，并改善AVF的临床结果。
