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Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis
BACKGROUND: 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 indic...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348915/ https://www.ncbi.nlm.nih.gov/pubmed/28288599 http://dx.doi.org/10.1186/s12911-017-0420-x |
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author | Bozzetto, Michela Rota, Stefano Vigo, Valentina Casucci, Francesco Lomonte, Carlo Morale, Walter Senatore, Massimo Tazza, Luigi Lodi, Massimo Remuzzi, Giuseppe Remuzzi, Andrea |
author_facet | Bozzetto, Michela Rota, Stefano Vigo, Valentina Casucci, Francesco Lomonte, Carlo Morale, Walter Senatore, Massimo Tazza, Luigi Lodi, Massimo Remuzzi, Giuseppe Remuzzi, Andrea |
author_sort | Bozzetto, Michela |
collection | PubMed |
description | BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-017-0420-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5348915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53489152017-03-14 Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis Bozzetto, Michela Rota, Stefano Vigo, Valentina Casucci, Francesco Lomonte, Carlo Morale, Walter Senatore, Massimo Tazza, Luigi Lodi, Massimo Remuzzi, Giuseppe Remuzzi, Andrea BMC Med Inform Decis Mak Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-017-0420-x) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-14 /pmc/articles/PMC5348915/ /pubmed/28288599 http://dx.doi.org/10.1186/s12911-017-0420-x Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Bozzetto, Michela Rota, Stefano Vigo, Valentina Casucci, Francesco Lomonte, Carlo Morale, Walter Senatore, Massimo Tazza, Luigi Lodi, Massimo Remuzzi, Giuseppe Remuzzi, Andrea Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis |
title | Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis |
title_full | Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis |
title_fullStr | Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis |
title_full_unstemmed | Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis |
title_short | Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis |
title_sort | clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348915/ https://www.ncbi.nlm.nih.gov/pubmed/28288599 http://dx.doi.org/10.1186/s12911-017-0420-x |
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