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Quantitative Prediction of Paravalvular Leak in Transcatheter Aortic Valve Replacement Based on Tissue-Mimicking 3D Printing

Overview of attention for article published in JACC: Cardiovascular Imaging, July 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#21 of 2,700)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

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24 news outlets
blogs
2 blogs
twitter
43 X users
facebook
2 Facebook pages

Citations

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114 Dimensions

Readers on

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136 Mendeley
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Title
Quantitative Prediction of Paravalvular Leak in Transcatheter Aortic Valve Replacement Based on Tissue-Mimicking 3D Printing
Published in
JACC: Cardiovascular Imaging, July 2017
DOI 10.1016/j.jcmg.2017.04.005
Pubmed ID
Authors

Zhen Qian, Kan Wang, Shizhen Liu, Xiao Zhou, Vivek Rajagopal, Christopher Meduri, James R. Kauten, Yung-Hang Chang, Changsheng Wu, Chuck Zhang, Ben Wang, Mani A. Vannan

Abstract

This study aimed to develop a procedure simulation platform for in vitro transcatheter aortic valve replacement (TAVR) using patient-specific 3-dimensional (3D) printed tissue-mimicking phantoms. We investigated the feasibility of using these 3D printed phantoms to quantitatively predict the occurrence, severity, and location of any degree of post-TAVR paravalvular leaks (PVL). We have previously shown that metamaterial 3D printing technique can be used to create patient-specific phantoms that mimic the mechanical properties of biological tissue. This may have applications in procedural planning for cardiovascular interventions. This retrospective study looked at 18 patients who underwent TAVR. Patient-specific aortic root phantoms were created using the tissue-mimicking 3D printing technique using pre-TAVR computed tomography. The CoreValve (self-expanding valve) prostheses were deployed in the phantoms to simulate the TAVR procedure, from which post-TAVR aortic root strain was quantified in vitro. A novel index, the annular bulge index, was measured to assess the post-TAVR annular strain unevenness in the phantoms. We tested the comparative predictive value of the bulge index and other known predictors of post-TAVR PVL. The maximum annular bulge index was significantly different among patient subgroups that had no PVL, trace-to-mild PVL, and moderate-to-severe PVL (p = 0.001). Compared with other known PVL predictors, bulge index was the only significant predictor of moderate-severe PVL (area under the curve = 95%; p < 0.0001). Also, in 12 patients with post-TAVR PVL, the annular bulge index predicted the major PVL location in 9 patients (accuracy = 75%). In this proof-of-concept study, we have demonstrated the feasibility of using 3D printed tissue-mimicking phantoms to quantitatively assess the post-TAVR aortic root strain in vitro. A novel indicator of the post-TAVR annular strain unevenness, the annular bulge index, outperformed the other established variables and achieved a high level of accuracy in predicting post-TAVR PVL, in terms of its occurrence, severity, and location.

X Demographics

X Demographics

The data shown below were collected from the profiles of 43 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 136 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 13%
Student > Master 16 12%
Student > Bachelor 14 10%
Student > Ph. D. Student 14 10%
Other 11 8%
Other 21 15%
Unknown 42 31%
Readers by discipline Count As %
Medicine and Dentistry 39 29%
Engineering 28 21%
Agricultural and Biological Sciences 3 2%
Biochemistry, Genetics and Molecular Biology 3 2%
Nursing and Health Professions 2 1%
Other 15 11%
Unknown 46 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 208. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 January 2018.
All research outputs
#187,852
of 25,382,440 outputs
Outputs from JACC: Cardiovascular Imaging
#21
of 2,700 outputs
Outputs of similar age
#3,967
of 326,871 outputs
Outputs of similar age from JACC: Cardiovascular Imaging
#1
of 59 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,700 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.1. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,871 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.