Evidence map›Paper›PMID 41034383›Full record

ArticleScientific reports2025

Computational simulations of coronary artery bifurcation stenting using realistic plaque distribution and materials.

Wei Wu, Shijia Zhao, Rakshita Ramesh Bhat, Yash Vardhan Trivedi, Parth Munjal, Rahul Chikatimalla, Ruben K A Tapia-Orihuela, Hammad Zafar, Haritha Darapaneni, Komal Arora and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

12 authors.

Wei WuCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Shijia ZhaoCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Rakshita Ramesh BhatCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Yash Vardhan TrivediCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Parth MunjalCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Rahul ChikatimallaCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Ruben K A Tapia-OrihuelaCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Hammad ZafarCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Haritha DarapaneniCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Komal AroraCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Changkye LeeCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA.
Yiannis S ChatzizisisCenter for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Leonard M. Miller School of Medicine, University of Miami Health System, University of Miami, 1120 NW 14th Street, Suite 1124, Miami, FL, 33136, USA. ychatzizisis@icloud.com.

Funding

Patient-Specific Simulations to Guide Coronary Bifurcation StentingR01HL144690 · NHLBI · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI CHATZIZISIS, IOANNIS S · 2019 to 2023
$3.9M
NHLBI NIH HHS R01 HL144690NIH HHS R01 HL144690
6 · The paper itself

Abstract

Coronary bifurcation stenting is a technically demanding procedure that benefits from precise preoperative planning. Finite element analysis (FEA) offers a powerful framework for simulating stent deployment, with full-plaque (FP) models representing the most detailed and physiologically realistic approach. In this study, we developed and validated an FP finite element model for coronary bifurcation stenting, aiming to establish a reference standard for future model assessments. High-resolution pre-stenting optical coherence tomography (OCT) data from nine patient cases were used to reconstruct anatomically accurate 3D models, incorporating detailed plaque compositions. Stent deployment was simulated following real clinical procedural steps. Model accuracy was assessed by comparing minimal lumen diameter (MLD) between simulations and post-stenting OCT measurements using Bland-Altman analysis. The FP model reliably reproduced multi-step stenting procedures, capturing vessel remodeling and stent deformation with high fidelity. The analysis showed a mean bias of 0.07 mm (2.1% error) with 95% limits of agreement from - 0.38 mm to 0.51 mm. These results demonstrate the model's high accuracy in replicating real-world stenting outcomes. Beyond validation, the FP model offers broad applications. It can serve as a benchmark for evaluating simplified, faster simulation tools, support patient-specific procedural planning, and enable inverse modeling to extract biomechanical properties of plaques. This study establishes the FP model as a robust and versatile platform with potential for future integration into real-time clinical workflows.

Indexed as

Computer SimulationCoronary Artery DiseaseCoronary VesselsModels, CardiovascularPlaque, AtheroscleroticStentsFemaleFinite Element AnalysisHumansMaleMiddle AgedTomography, Optical Coherence3D reconstructionComputational simulationsCoronary bifurcation stentingFinite element analysisPlaque materials

Identifiers

PMID41034383
PMCPMC12489092

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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.