Evidence map›Paper›PMID 42306643›Full record

ArticleJTCVS structural and endovascular2025

A novel computational method to predict hypoattenuated leaflet thickening post-transcatheter aortic valve replacement using preprocedural computed tomography scans.

Aniket Venkatesh, Fateme Esmailie, Noah Tregobov, Hoda Hatoum, Breandan Yeats, Huang Chen, Beom Jun Lee, Philipp Ruile, Franz-Josef Neumann, Philipp Blanke and 6 more

Abstract read
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In one paragraph

Article in JTCVS structural and endovascular, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Aniket VenkateshParker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Ga.
Fateme EsmailieDepartment of Biomedical Engineering, University of North Texas, Denton, Tex.
Noah TregobovCardiovascular Translational Laboratory, St. Paul's Hospital and University of British Columbia, Vancouver, Canada.
Hoda HatoumDepartment of Biomedical Engineering, Michigan Technological University, Houghton, Mich.
Breandan YeatsParker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Ga.
Huang ChenDepartment of Biomedical Engineering, University of Nevada, Las Vegas, Las Vegas, Nev.
Beom Jun LeeWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Ga.
Philipp RuileDepartment of Cardiology and Angiology, Medical Center, University of Freiburg, Faculty of Medicine, Bad Krozingen, Germany.
Franz-Josef NeumannDepartment of Cardiology and Angiology, Medical Center, University of Freiburg, Faculty of Medicine, Bad Krozingen, Germany.
Philipp BlankeDepartment of Radiology, St. Paul's Hospital and University of British Columbia, Vancouver, Canada.
Jonathon LeipsicDepartment of Radiology, St. Paul's Hospital and University of British Columbia, Vancouver, Canada.
Gaurav GulsinDepartment of Cardiovascular Sciences, University of Leicester and the NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom.
Vinod ThouraniDepartment of Cardiovascular Surgery, Marcus Heart Valve Center, Piedmont Heart Institute, Atlanta, Ga.
David MeierCardiovascular Translational Laboratory, St. Paul's Hospital and University of British Columbia, Vancouver, Canada.
Lakshmi Prasad DasiParker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Ga.
Stephanie L SellersCardiovascular Translational Laboratory, St. Paul's Hospital and University of British Columbia, Vancouver, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Hypoattenuated leaflet thickening (HALT) is a computed tomography (CT) finding after transcatheter aortic valve replacement (TAVR) that is indicative of bioprosthetic valvular thrombosis. There are currently no standardized or validated methods for predicting HALT, which can cause bioprosthetic valve dysfunction and has been associated with adverse patient outcomes. The objective was to develop a novel fast-response, artificial intelligence, and machine learning (ML)-driven computational pipeline to predict HALT using preprocedural CT scans. Methods: The pipeline consisted of (1) pre-TAVR CT reconstruction and reduced order modeling simulations to automatically predict postprocedural geometric parameters, (2) a landmark-guided automated left ventricle segmentation method to predict hemodynamic parameters, and (3) statistical and ML analyses to develop HALT predictive metrics. Results: Pre- and postprocedural scans from 45 patients (21 with HALT, 24 without) were used as inputs for the pipeline. We identified statistically significant relationships between HALT and peak systolic blood velocity ( Conclusions: A computational pipeline using pre-procedural CT scans as inputs that outputs post-TAVR geometric and hemodynamic measurements was developed to assess metrics with the potential to predict the risk of HALT. Such a tool may help guide decision-making and understanding of prevention of postprocedural thrombosis.

Indexed as

computed tomographyhypoattenuated leaflet thickeningmachine learningreduced order modelingthrombosistranscatheter aortic valve implantation/replacement

Identifiers

PMID42306643
PMCPMC13244794

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.