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A Highly Predictive Risk Model for Pacemaker Implantation After TAVR

Overview of attention for article published in JACC: Cardiovascular Imaging, April 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

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1 news outlet
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55 X users
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2 Facebook pages

Citations

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207 Dimensions

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150 Mendeley
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Title
A Highly Predictive Risk Model for Pacemaker Implantation After TAVR
Published in
JACC: Cardiovascular Imaging, April 2017
DOI 10.1016/j.jcmg.2016.11.020
Pubmed ID
Authors

Yoshio Maeno, Yigal Abramowitz, Hiroyuki Kawamori, Yoshio Kazuno, Shunsuke Kubo, Nobuyuki Takahashi, Geeteshwar Mangat, Kazuaki Okuyama, Mohammad Kashif, Tarun Chakravarty, Mamoo Nakamura, Wen Cheng, John Friedman, Daniel Berman, Raj R. Makkar, Hasan Jilaihawi

Abstract

This study sought to develop a robust and definitive risk model for new permanent pacemaker implantation (PPMI) after SAPIEN 3 (third generation balloon expandable valve) (Edwards Lifesciences, Irvine, California) transcatheter aortic valve replacement (third generation balloon expandable valve TAVR), including calcification in the aortic-valvular complex (AVC). The association between calcium in the AVC and need for PPMI is poorly delineated after third generation balloon expandable valve TAVR. At Cedars-Sinai Heart Institute in Los Angeles, California, a total of 240 patients with severe aortic stenosis underwent third generation balloon expandable valve TAVR and had contrast computed tomography. AVC was characterized precisely by leaflet sector and region. The total new PPMI rate was 14.6%. On multivariate analysis for predictors of PPMI, pre-procedure third generation balloon expandable valve TAVR, right bundle branch block (RBBB), shorter membranous septum (MS) length, and noncoronary cusp device-landing zone calcium volume (NCC-DLZ CA) were included. Predictive probabilities were generated using this logistic regression model. If 3 pre-procedural risk factors were present, the c-statistic of the model for PPMI was area under the curve of 0.88, sensitivity of 77.1%, and specificity of 87.1%; this risk model had high negative predictive value (95.7%). The addition of the procedural factor of device depth to the model, with the parameter of difference between implantation depth and MS length, combined with RBBB and NCC-DLZ CA increased the c-statistic to 0.92, sensitivity to 94.3%, specificity to 83.8%, and negative predictive value to 98.8% CONCLUSIONS: By using a precise characterization of distribution of calcification in the AVC in a single-center, retrospective study, NCC-DLZ CA was found to be an independent predictor of new PPMI post-third generation balloon expandable valve TAVR. The findings also reinforce the importance of short MS length, pre-existing RBBB, and ventricular implantation depth as important synergistic PPMI risk factors. This risk model will need validation by future prospective multicenter studies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 55 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 150 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
Unknown 149 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 17%
Other 25 17%
Student > Ph. D. Student 16 11%
Student > Postgraduate 11 7%
Student > Bachelor 7 5%
Other 20 13%
Unknown 45 30%
Readers by discipline Count As %
Medicine and Dentistry 76 51%
Agricultural and Biological Sciences 4 3%
Biochemistry, Genetics and Molecular Biology 2 1%
Unspecified 1 <1%
Social Sciences 1 <1%
Other 3 2%
Unknown 63 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 41. 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 12 December 2019.
All research outputs
#1,000,592
of 25,392,582 outputs
Outputs from JACC: Cardiovascular Imaging
#290
of 2,700 outputs
Outputs of similar age
#20,413
of 324,695 outputs
Outputs of similar age from JACC: Cardiovascular Imaging
#8
of 67 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th 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 well, scoring higher than 89% 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 324,695 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 93% of its contemporaries.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.