ReviewExperimental physiology2009
Towards predictive modelling of the electrophysiology of the heart.
Review in Experimental physiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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.
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
44 citing papers in PubMed, 105 citations in OpenAlex.
- In silico predictions of action potential propagation in doxorubicin cardiotoxicity: A parametric study using preclinical 3D magnetic resonance imaging-based fibrotic left ventricle models.The Journal of physiology · 2026Article
- An organic artificial cardiomyocyte.Nature communications · 2026Article
- Comparative analysis of the ten Tusscher and Tomek human ventricular cell models at cellular, tissue, and organ levels: Implications for post-infarct ventricular tachycardia simulation.Physiological reports · 2025Article
- Population-based computational simulations elucidate mechanisms of focal arrhythmia following stem cell injection.Journal of molecular and cellular cardiology · 2025Article
- Graft-host coupling changes can lead to engraftment arrhythmia: a computational study.The Journal of physiology · 2023Article
- Cardiac biophysical detailed synergetic modality rendering and visible correlation.Frontiers in physiology · 2023Article
- Cardiovascular Diseases in the Digital Health Era: A Translational Approach from the Lab to the Clinic.Biotech (Basel (Switzerland)) · 2022Review
- Resource-Efficient Use of Modern Processor Architectures For Numerically Solving Cardiac Ionic Cell Models.Frontiers in physiology · 2022Article
- Area Available for Atrial Fibrillation to Propagate Is an Important Determinant of Recurrence After Ablation.JACC. Clinical electrophysiology · 2021Article
- Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging.Current cardiology reports · 2020Review
- Combining an in silico proarrhythmic risk assay with a tPKPD model to predict QTc interval prolongation in the anesthetized guinea pig assay.Toxicology and applied pharmacology · 2020Article
- Cardiovascular disease models: A game changing paradigm in drug discovery and screening.Biomaterials · 2019Review
- Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia.Frontiers in physiology · 2019Article
- On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.PLoS computational biology · 2018Article
- Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models.Computers in biology and medicine · 2018Article
- Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A Sensitivity Study.Cardiovascular engineering and technology · 2018Article
- Muscle Thickness and Curvature Influence Atrial Conduction Velocities.Frontiers in physiology · 2018Article
- Computational assessment of the functional role of sinoatrial node exit pathways in the human heart.PloS one · 2017Article
- Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology.IEEE transactions on medical imaging · 2016Article
- How computer simulations of the human heart can improve anti-arrhythmia therapy.The Journal of physiology · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
The simulation of cardiac electrical function is an example of a successful integrative multiscale modelling approach that is directly relevant to human disease. Today we stand at the threshold of a new era, in which anatomically detailed, tomographically reconstructed models are being developed that integrate from the ion channel to the electromechanical interactions in the intact heart. Such models hold high promise for interpretation of clinical and physiological measurements, for improving the basic understanding of the mechanisms of dysfunction in disease, such as arrhythmias, myocardial ischaemia and heart failure, and for the development and performance optimization of medical devices. The goal of this article is to present an overview of current state-of-art advances towards predictive computational modelling of the heart as developed recently by the authors of this article. We first outline the methodology for constructing electrophysiological models of the heart. We then provide three examples that demonstrate the use of these models, focusing specifically on the mechanisms for arrhythmogenesis and defibrillation in the heart. These include: (1) uncovering the role of ventricular structure in defibrillation; (2) examining the contribution of Purkinje fibres to the failure of the shock; and (3) using magnetic resonance imaging reconstructed heart models to investigate the re-entrant circuits formed in the presence of an infarct scar.
Indexed as
Identifiers
What OpenQuestion holds
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.