ReviewPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2020
An audit of uncertainty in multi-scale cardiac electrophysiology models.
Review in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Multimodal Characterization of Atrial Fibrillation: From Patient-Specific Anatomy and Electrophysiology to Standardized Atrial Mapping.Tomography (Ann Arbor, Mich.) · 2026Review
- Uncertainty quantification via polynomial chaos expansion of myocardial fibre orientation and cardiac activation patterns.The Journal of physiology · 2026Article
- Atrial fibrosis in atrial fibrillation: Mechanisms, mapping techniques and clinical applications.The Journal of physiology · 2026Review
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.Journal of the American Heart Association · 2024Review
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- [Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data.Arrhythmia & electrophysiology review · 2024Review
- Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning.Interface focus · 2023Article
- lifex-ep: a robust and efficient software for cardiac electrophysiology simulations.BMC bioinformatics · 2023Article
- Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.Archives of toxicology · 2023Article
- Editorial: Diagnosis, monitoring, and treatment of heart rhythm: new insights and novel computational methods.Frontiers in physiology · 2023Article
- Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment.Frontiers in pharmacology · 2023Article
- Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics.Biomechanics and modeling in mechanobiology · 2022Article
- An ensemble of parameters from a robust Markov-based model reproduces L-type calcium currents from different human cardiac myocytes.PloS one · 2022Article
- Space-time shape uncertainties in the forward and inverse problem of electrocardiography.International journal for numerical methods in biomedical engineering · 2021Article
- Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning.International journal for numerical methods in biomedical engineering · 2021Article
- Data integration for the numerical simulation of cardiac electrophysiology.Pacing and clinical electrophysiology : PACE · 2021Review
- Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium.Royal Society open science · 2021Article
- EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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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.