ArticleThe Journal of physiology2016
Uncertainty and variability in computational and mathematical models of cardiac physiology.
Article in The Journal of physiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 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.
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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.
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Who cites it
83 citing papers in PubMed, 1 synthesis or guideline pooled it, 163 citations in OpenAlex.
- Variability in reported midpoints of (in)activation of cardiac INa.The Journal of general physiology · 2025Pooled it
- Determining properties of human-induced pluripotent stem cell-derived cardiomyocytes using spatially resolved electromechanical metrics.The Journal of physiology · 2026Article
- Post-MI Remodeling Mechanics of Left Ventricle: Microstructure-Informed Models, Identifiability, and Uncertainty for Patient-Specific Prediction.Bioengineering (Basel, Switzerland) · 2026Review
- Weaving the Digital Tapestry: Methods for Emulating Cohorts of Cardiac Digital Twins Using Gaussian Processes.Annals of biomedical engineering · 2026Article
- Modelling maternal cardiovascular adaptation to pregnancy: a scoping review.BMC pregnancy and childbirth · 2025Article
- 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
- Parameter inference for stochastic reaction models of ion channel gating from whole-cell voltage-clamp data.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Decoding uncertainty for clinical decision-making.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Observer- and sequence variability in personalized 4D flow MRI-based cardiovascular models.Scientific reports · 2025Article
- Computational physiological modeling for lung-specific ventilation and perfusion management inFrontiers in physiology · 2025Article
- Reconstructing ventricular cardiomyocyte dynamics and parameter estimation using data assimilation.Biophysical journal · 2024Article
- Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.Journal of the American Heart Association · 2024Review
- Article
- Computational Modeling of Cardiac Electrophysiology.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A range of voltage-clamp protocol designs for rapid capture of hERG kinetics.Wellcome open research · 2024Article
- Neural network emulation of the human ventricular cardiomyocyte action potential: a tool for more efficient computation in pharmacological studies.bioRxiv : the preprint server for biology · 2023Article
- Impact of noise on the instability of spiral waves in stochastic 2D mathematical models of human atrial fibrillation.Journal of biological physics · 2023Article
- Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics.Bulletin of mathematical biology · 2023Article
- Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.Archives of toxicology · 2023Article
- Leak current, even with gigaohm seals, can cause misinterpretation of stem cell-derived cardiomyocyte action potential recordings.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
key pointsMathematical and computational models of cardiac physiology have been an integral component of cardiac electrophysiology since its inception, and are collectively known as the Cardiac Physiome. We identify and classify the numerous sources of variability and uncertainty in model formulation, parameters and other inputs that arise from both natural variation in experimental data and lack of knowledge. The impact of uncertainty on the outputs of Cardiac Physiome models is not well understood, and this limits their utility as clinical tools. We argue that incorporating variability and uncertainty should be a high priority for the future of the Cardiac Physiome. We suggest investigating the adoption of approaches developed in other areas of science and engineering while recognising unique challenges for the Cardiac Physiome; it is likely that novel methods will be necessary that require engagement with the mathematics and statistics community. ABSTRACT: The Cardiac Physiome effort is one of the most mature and successful applications of mathematical and computational modelling for describing and advancing the understanding of physiology. After five decades of development, physiological cardiac models are poised to realise the promise of translational research via clinical applications such as drug development and patient-specific approaches as well as ablation, cardiac resynchronisation and contractility modulation therapies. For models to be included as a vital component of the decision process in safety-critical applications, rigorous assessment of model credibility will be required. This White Paper describes one aspect of this process by identifying and classifying sources of variability and uncertainty in models as well as their implications for the application and development of cardiac models. We stress the need to understand and quantify the sources of variability and uncertainty in model inputs, and the impact of model structure and complexity and their consequences for predictive model outputs. We propose that the future of the Cardiac Physiome should include a probabilistic approach to quantify the relationship of variability and uncertainty of model inputs and outputs.
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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.