ArticleMedical & biological engineering & computing2018
Sensitivity analysis of ventricular activation and electrocardiogram in tailored models of heart-failure patients.
Article in Medical & biological engineering & computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Digital twinning of cardiac electrophysiology for congenital heart disease.Journal of the Royal Society, Interface · 2024Article
- Digital twinning of cardiac electrophysiology for congenital heart disease.bioRxiv : the preprint server for biology · 2023Article
- Basic Research Approaches to Evaluate Cardiac Arrhythmia in Heart Failure and Beyond.Frontiers in physiology · 2022Review
- Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications.Biophysics reviews · 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
- The importance of modeling the human cerebral vasculature in blunt trauma.Biomedical engineering online · 2021Article
- Parameter variations in personalized electrophysiological models of human heart ventricles.PloS one · 2021Article
- Approaches for determining cardiac bidomain conductivity values: progress and challenges.Medical & biological engineering & computing · 2020Review
- An audit of uncertainty in multi-scale cardiac electrophysiology models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Review
- Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning.BMC medical informatics and decision making · 2020Article
- Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification.Computer methods in applied mechanics and engineering · 2019Article
- Computational models in cardiology.Nature reviews. Cardiology · 2019Review
- MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex.Frontiers in physiology · 2019Article
- Computational Modeling for Cardiac Resynchronization Therapy.Journal of cardiovascular translational research · 2018Review
- Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances.Journal of the Royal Society, Interface · 2018Review
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac resynchronization therapy is not effective in a variable proportion of heart failure patients. An accurate knowledge of each patient's electroanatomical features could be helpful to determine the most appropriate treatment. The goal of this study was to analyze and quantify the sensitivity of left ventricular (LV) activation and the electrocardiogram (ECG) to changes in 39 parameters used to tune realistic anatomical-electrophysiological models of the heart. Electrical activity in the ventricles was simulated using a reaction-diffusion equation. To simulate cellular electrophysiology, the Ten Tusscher-Panfilov 2006 model was used. Intracardiac electrograms and 12-lead ECGs were computed by solving the bidomain equation. Parameters showing the highest sensitivity values were similar in the six patients studied. QRS complex and LV activation times were modulated by the sodium current, the cell surface-to-volume ratio in the LV, and tissue conductivities. The T-wave was modulated by the calcium and rectifier-potassium currents, and the cell surface-to-volume ratio in both ventricles. We conclude that homogeneous changes in ionic currents entail similar effects in all ECG leads, whereas the effects of changes in tissue properties show larger inter-lead variability. The effects of parameter variations are highly consistent between patients and most of the model tuning could be performed with only ~10 parameters.
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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.