Evidence map›Paper›PMID 35464089›Full record

ArticleFrontiers in physiology2022

Left Ventricle Biomechanics of Low-Flow, Low-Gradient Aortic Stenosis: A Patient-Specific Computational Model.

Andrew D Wisneski, Yunjie Wang, Salvatore Cutugno, Salvatore Pasta, Ashley Stroh, Jiang Yao, Tom C Nguyen, Vaikom S Mahadevan, Julius M Guccione

Open access · goldAbstract read
In one paragraph

Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 16% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Frontiers in bioengineering and biotechnology · 2026
    Article
  3. Article
  4. Article
  5. 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

9 authors at 4 institutions in 2 countries.

Andrew D WisneskiDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Yunjie WangThornton Tomassetti Life Sciences, Santa Clara, CA, United States.
Salvatore CutugnoDepartment of Engineering, Viale Dell Scienze, Universita degli Studi di Palermo, Palermo, Italy.
Salvatore PastaDepartment of Engineering, Viale Dell Scienze, Universita degli Studi di Palermo, Palermo, Italy.
Ashley StrohCATIA, Dassault Systèmes, Wichita, KS, United States.
Jiang YaoSimulia, Dassault Systèmes Simulia, Johnston, RI, United States.
Tom C NguyenDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Vaikom S MahadevanDivision of Cardiology, University of California, San Francisco, San Francisco, CA, United States.
Julius M GuccioneDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
University of California, San Francisco · USUniversity of Palermo · ITDassault Systèmes (United States) · USThornton Tomasetti (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to create an imaging-derived patient-specific computational model of low-flow, low-gradient (LFLG) aortic stenosis (AS) to obtain biomechanics data about the left ventricle. LFLG AS is now a commonly recognized sub-type of aortic stenosis. There remains much controversy over its management, and investigation into ventricular biomechanics may elucidate pathophysiology and better identify patients for valve replacement. ECG-gated cardiac computed tomography images from a patient with LFLG AS were obtained to provide patient-specific geometry for the computational model. Surfaces of the left atrium, left ventricle (LV), and outflow track were segmented. A previously validated multi-scale, multi-physics computational human heart model was adapted to the patient-specific geometry, yielding a model consisting of 91,000 solid elements. This model was coupled to a virtual circulatory system and calibrated to clinically measured parameters from echocardiography and cardiac catheterization data. The simulation replicated key physiologic parameters within 10% of their clinically measured values. Global LV systolic myocardial stress was 7.1 ± 1.8 kPa. Mean stress of the basal, middle, and apical segments were 7.7 ± 1.8 kPa, 9.1 ± 3.8 kPa, and 6.4 ± 0.4 kPa, respectively. This is the first patient-specific computational model of LFLG AS based on clinical imaging. Low myocardial stress correlated with low ejection fraction and eccentric LV remodeling. Further studies are needed to understand how alterations in LV biomechanics correlates with clinical outcomes of AS.

Indexed as

aortic stenosisfinite elememt methodmyofiber stressrealistic simulationventricle-aortic couplingventricular function

Identifiers

PMID35464089
PMCPMC9019780
OpenAlexW4226244831

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.