ArticleInternational journal for numerical methods in biomedical engineering2017
Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty.
Article in International journal for numerical methods in biomedical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Mathematical simulations of paediatric haemodynamics in isolated ventricular septal defect.Journal of the Royal Society, Interface · 2026Article
- Accelerating cardiovascular digital twin creation using precomputed virtual populations as Bayesian priors.Frontiers in physiology · 2026Article
- A Patient-Specific Computational Model for Neonates and Infants with Borderline Left Ventricles.Annals of biomedical engineering · 2026Article
- Inverse analysis of patient-specific parameters of a 3D-0D closed-loop cardiovascular model with an exemplary application to an adult tetralogy of Fallot case.Biomechanics and modeling in mechanobiology · 2025Article
- InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients.Journal of the Royal Society, Interface · 2025Article
- Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.Journal of the American Heart Association · 2024Review
- Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.ArXiv · 2024Article
- Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.NPJ digital medicine · 2024Article
- Computational modelling of valvular heart disease: haemodynamic insights and clinical implications.Frontiers in bioengineering and biotechnology · 2024Review
- Tissue-growth-based synthetic tree generation and perfusion simulation.Biomechanics and modeling in mechanobiology · 2023Article
- Parameter inference in a computational model of haemodynamics in pulmonary hypertension.Journal of the Royal Society, Interface · 2023Article
- Automated generation of 0D and 1D reduced-order models of patient-specific blood flow.International journal for numerical methods in biomedical engineering · 2022Article
- Patient-Specific Modelling and Parameter Optimisation to Simulate Dilated Cardiomyopathy in Children.Cardiovascular engineering and technology · 2022Article
- Inverse problems in blood flow modeling: A review.International journal for numerical methods in biomedical engineering · 2022Review
- On the Periodicity of Cardiovascular Fluid Dynamics Simulations.Annals of biomedical engineering · 2021Article
- An optimal control approach to determine resistance-type boundary conditions from in-vivo data for cardiovascular simulations.International journal for numerical methods in biomedical engineering · 2021Article
- Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid-dynamics model of the pulmonary circulation.International journal for numerical methods in biomedical engineering · 2021Article
- Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling.Frontiers in physiology · 2021Article
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5 authors.
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Abstract
Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. Copyright © 2016 John Wiley & Sons, Ltd.
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