ReviewHeart (British Cardiac Society)2017
Computational modelling for congenital heart disease: how far are we from clinical translation?
Review in Heart (British Cardiac Society), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- Renaissance of Cardiac Imaging to Assist Percutaneous Interventions in Congenital Heart Diseases:The Role of Three-Dimensional Echocardiography and Multimodality Imaging.Frontiers in pediatrics · 2022Pooled it
- Postnatal pulmonary artery development from transcript to tissue.Journal of the Royal Society, Interface · 2026Article
- Single ventricle care: challenges, successes and the future in a single center program.Translational pediatrics · 2025Review
- Computational Fluid Dynamic Assessment of Patients with Congenital Heart Disease from 3D Rotational Angiography.Pediatric cardiology · 2025Article
- OpenHands: An Open-Source Statistical Shape Model of the Finger Bones.Annals of biomedical engineering · 2024Article
- Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.PLoS computational biology · 2024Article
- Understanding the influence of surgical parameters on craniofacial surgery outcomes: a computational study.Royal Society open science · 2024Article
- Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases.Anatolian journal of cardiology · 2024Article
- Influence of aortic valve morphology on vortical structures and wall shear stress.Medical & biological engineering & computing · 2023Article
- Artificial Intelligence in Congenital Heart Disease: Current State and Prospects.JACC. Advances · 2022Review
- Automatic segmentation of the great arteries for computational hemodynamic assessment.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Computational Modeling of Right Ventricular Motion and Intracardiac Flow in Repaired Tetralogy of Fallot.Cardiovascular engineering and technology · 2022Article
- High fidelity blood flow in a patient-specific arteriovenous fistula.Scientific reports · 2021Article
- The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs.Journal of the Royal Society, Interface · 2021Article
- Application of physics-based flow models in cardiovascular medicine: Current practices and challenges.Biophysics reviews · 2021Review
- Computational Modeling of Blood Flow Hemodynamics for Biomechanical Investigation of Cardiac Development and Disease.Journal of cardiovascular development and disease · 2021Review
- Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients.Frontiers in cardiovascular medicine · 2021Article
- Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time.International journal for numerical methods in biomedical engineering · 2020Article
- Quantitative Assessment of Parenchymal Involvement Using 3D Lung Model in Adolescent With Covid-19 Interstitial Pneumonia.Frontiers in pediatrics · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Computational models of congenital heart disease (CHD) have become increasingly sophisticated over the last 20 years. They can provide an insight into complex flow phenomena, allow for testing devices into patient-specific anatomies (pre-CHD or post-CHD repair) and generate predictive data. This has been applied to different CHD scenarios, including patients with single ventricle, tetralogy of Fallot, aortic coarctation and transposition of the great arteries. Patient-specific simulations have been shown to be informative for preprocedural planning in complex cases, allowing for virtual stent deployment. Novel techniques such as statistical shape modelling can further aid in the morphological assessment of CHD, risk stratification of patients and possible identification of new 'shape biomarkers'. Cardiovascular statistical shape models can provide valuable insights into phenomena such as ventricular growth in tetralogy of Fallot, or morphological aortic arch differences in repaired coarctation. In a constant move towards more realistic simulations, models can also account for multiscale phenomena (eg, thrombus formation) and importantly include measures of uncertainty (ie, CIs around simulation results). While their potential to aid understanding of CHD, surgical/procedural decision-making and personalisation of treatments is undeniable, important elements are still lacking prior to clinical translation of computational models in the field of CHD, that is, large validation studies, cost-effectiveness evaluation and establishing possible improvements in patient outcomes.
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Registered trials
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.