Assessment of the Severity of Aortic Stenosis using Aortic Valve Coefficient
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| Language: | English |
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University of Cincinnati / OhioLINK
2016
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470672658 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin1470672658 |
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English |
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Biomedical Research Aortic stenosis aortic valve disease inverse algorthm pre-stressing Doppler assessment of aortic stenosis finite element modeling |
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Biomedical Research Aortic stenosis aortic valve disease inverse algorthm pre-stressing Doppler assessment of aortic stenosis finite element modeling Paul, Anup K. Assessment of the Severity of Aortic Stenosis using Aortic Valve Coefficient |
| author |
Paul, Anup K. |
| author_facet |
Paul, Anup K. |
| author_sort |
Paul, Anup K. |
| title |
Assessment of the Severity of Aortic Stenosis using Aortic Valve Coefficient |
| title_short |
Assessment of the Severity of Aortic Stenosis using Aortic Valve Coefficient |
| title_full |
Assessment of the Severity of Aortic Stenosis using Aortic Valve Coefficient |
| title_fullStr |
Assessment of the Severity of Aortic Stenosis using Aortic Valve Coefficient |
| title_full_unstemmed |
Assessment of the Severity of Aortic Stenosis using Aortic Valve Coefficient |
| title_sort |
assessment of the severity of aortic stenosis using aortic valve coefficient |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2016 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470672658 |
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AT paulanupk assessmentoftheseverityofaorticstenosisusingaorticvalvecoefficient |
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1719440701107732480 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin14706726582021-08-03T06:38:16Z Assessment of the Severity of Aortic Stenosis using Aortic Valve Coefficient Paul, Anup K. Biomedical Research Aortic stenosis aortic valve disease inverse algorthm pre-stressing Doppler assessment of aortic stenosis finite element modeling Introduction. Accurate assessment of the severity of stenosis is critical in patients with aortic stenosis (AS). The ambiguities and imprecisions of the current diagnostic parameters can result in sub-optimal clinical decisions. In this research, we investigated the feasibility of using the functional diagnostic parameter AVC (Aortic Valve coefficient: ratio of the total transvalvular pressure drop to the proximal dynamic pressure) in the non-invasive assessment of AS and also for improving the concordance between non-invasive and invasive assessment of AS severity. Methods. AVC was calculated using Doppler (non-invasive) and cardiac catheterization (invasive) measured parameters obtained from retrospective chart reviews. Linear regression analysis was performed to assess any significant correlations between AVC and the measured parameters and also between the Doppler and catheterization derived parameters. To accurately evaluate the hemodynamics for diseased aortic valves using patient-specific computational formulations it is necessary to determine the pre-stressed condition of the in-vivo geometry. The previously developed optimization based inverse algorithm was improved to evaluate the pre-stress due to the change in arterial property of the tapered femoral artery. The compliance of the artery for a range of systemic pressures was also computed. Subsequently, a hybrid inverse algorithm was developed to determine the load-free and pre-stressed condition of patient-specific arterial geometries obtained from clinical MRI. The algorithm included the in-vivo axial stretch, lumen pressure and the patient-specific tissue properties determined from clinical data. <I>Results. A statistically significant and strong combined linear correlation (r = 0.93, p < 0.001) of AVC with the transvalvular pressure drop and the left ventricular outflow tract velocity was observed. The mean values of AVC were shown to better delineate moderate and severe stenosis (54% difference). An improved significant correlation was observed between Doppler and catheter derived AVC (r = 0.92, p < 0.05) when compared to the correlation between Doppler and catheter measurements of mean pressure drop (r = 0.72, p 0.05) and aortic valve area (r = 0.64, p < 0.05). The results obtained from the optimization based inverse algorithm showed that the change in arterial wall property caused significant variation in the dimensions of the load-free artery and insignificant variation in the dimensions and the circumferential stress of the pre-stressed artery. Further, the computed compliance of the artery was significantly influenced by the change in the average arterial pressure. The results obtained from the hybrid inverse algorithm showed that the radial shrinkage and thickening of the load-free patient-specific arterial wall was non-uniform. The load-free inner and outer diameters of the patient-specific artery were 33-38% and 22-25% smaller, respectively, than the corresponding in-vivo</I> diameters. The variation of the pre-stressed diameters from the in-vivo geometry was less than 5.1%. <I>Conclusions. This research has confirmed the feasibility of using both pressure drop and flow in a single combined non-dimensional non-invasive diagnostic index, AVC, for the assessment of AS severity. The long-term goal of this research is to evaluate the specificity and sensitivity of AVC for the diagnosis of AS using patient-specific computational formulations and prospective studies under clinical setting. 2016-09-09 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470672658 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470672658 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |