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A new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment

Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life‐threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and...

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Autores principales: Yu, Huidan, Khan, Monsurul, Wu, Hao, Du, Xiaoping, Chen, Rou, Rollins, Dave M., Fang, Xin, Long, Jianyun, Xu, Chenke, Sawchuk, Alan P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539998/
https://www.ncbi.nlm.nih.gov/pubmed/35509229
http://dx.doi.org/10.1002/cnm.3611
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author Yu, Huidan
Khan, Monsurul
Wu, Hao
Du, Xiaoping
Chen, Rou
Rollins, Dave M.
Fang, Xin
Long, Jianyun
Xu, Chenke
Sawchuk, Alan P.
author_facet Yu, Huidan
Khan, Monsurul
Wu, Hao
Du, Xiaoping
Chen, Rou
Rollins, Dave M.
Fang, Xin
Long, Jianyun
Xu, Chenke
Sawchuk, Alan P.
author_sort Yu, Huidan
collection PubMed
description Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life‐threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient‐specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in‐house computational platform, InVascular, for image‐based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with graphic processing unit parallel computing technology. The TPI–S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S = 0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in vivo measured ones and the systolic pressure is statistical equivalence to the in‐vivo measurements with p < .001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical insights of the new index. This computational concept can be applied to other arterial stenoses such as iliac stenosis. Such a noninvasive and patient‐specific hemodynamic index has the potential to aid in the clinical decision‐making of interventional treatment with reduced medical cost and patient risks.
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spelling pubmed-95399982022-10-14 A new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment Yu, Huidan Khan, Monsurul Wu, Hao Du, Xiaoping Chen, Rou Rollins, Dave M. Fang, Xin Long, Jianyun Xu, Chenke Sawchuk, Alan P. Int J Numer Method Biomed Eng Applied Research Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life‐threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient‐specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in‐house computational platform, InVascular, for image‐based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with graphic processing unit parallel computing technology. The TPI–S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S = 0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in vivo measured ones and the systolic pressure is statistical equivalence to the in‐vivo measurements with p < .001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical insights of the new index. This computational concept can be applied to other arterial stenoses such as iliac stenosis. Such a noninvasive and patient‐specific hemodynamic index has the potential to aid in the clinical decision‐making of interventional treatment with reduced medical cost and patient risks. John Wiley & Sons, Inc. 2022-05-18 2022-07 /pmc/articles/PMC9539998/ /pubmed/35509229 http://dx.doi.org/10.1002/cnm.3611 Text en © 2022 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Applied Research
Yu, Huidan
Khan, Monsurul
Wu, Hao
Du, Xiaoping
Chen, Rou
Rollins, Dave M.
Fang, Xin
Long, Jianyun
Xu, Chenke
Sawchuk, Alan P.
A new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
title A new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
title_full A new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
title_fullStr A new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
title_full_unstemmed A new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
title_short A new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
title_sort new noninvasive and patient‐specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
topic Applied Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539998/
https://www.ncbi.nlm.nih.gov/pubmed/35509229
http://dx.doi.org/10.1002/cnm.3611
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