Calibration and validation strategy for electromechanical cardiac digital twins
Curation statements for this article:-
Curated by eLife
eLife Assessment
This is a potentially important study that explores the relevant range of parameter values for calibration and validation of cardiac electromechanics in ventricular models. Although much of the work presented is solid, the evidence provided to support the authors' key scientific claims is incomplete, especially as it relates to the emphasis on standardized validation and verification approaches. Notably, the level of model personalization presented in this work falls short of the threshold for what could reasonably be called a "digital twin", even by the relatively relaxed standards that have emerged in computational physiology and related fields in recent years.
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
Abstract
Abstract
State-of-the-art cardiac electromechanical modelling and simulation form the basis for recent developments in cardiac Digital Twin technologies. However, a comprehensive evaluation of electromechanical models at cellular, tissue, and organ level has yet to be performed that addresses both ECG and pressure-volume biomarkers. Such an evaluation would build credibility for applications of cardiac Digital Twins in clinical research and therapy development.
We aimed to follow ASME V&V40 standards to develop a strategy for calibration, validation, and uncertainty quantification of ventricular electromechanical Digital Twins under healthy conditions. We performed a multi-scaled review of ventricular electromechanics to compile a dataset for calibration and validation incorporating ECG, pressure-volume, displacement, and strain biomarkers.
When applied to a biventricular multiscale model, we achieved healthy calibrated values for the QRS duration (89 ms), QT interval (360 ms), left ventricular ejection fraction (LVEF) (51 %), peak systolic pressure (14 kPa), end diastolic (110 mL) and end systolic volumes (50 mL), peak ejection flow rate (180 mL/ms). Model validation was performed by comparison to displacement and strain biomarkers including systolic atrioventricular plane displacement (1.5 cm), systolic fibre strain (−0.18) and longitudinal strain (−0.15). Sensitivity analysis of model parameters at cellular and ventricular scales was also performed. We quantified the effects of variability in ionic conductance, mechanical stiffness, cross-bridge cycling dynamics, and systemic circulation on action potential and active tension dynamics at the cellular scale, and on ECG, pressure-volume, displacement, and strain biomarkers at the ventricular scale. Simulations showed that the relationship between healthy LVEF and T wave biomarkers was primarily underpinned by variability in L-type calcium channel conductance and SERCA activity through multi-scale effects. In this study, we pave the way towards credible cardiac electromechanical Digital Twins by setting the basis for a strategy for calibration and validation based on both ECG and mechanical biomarkers.
Article activity feed
-
-
-
eLife Assessment
This is a potentially important study that explores the relevant range of parameter values for calibration and validation of cardiac electromechanics in ventricular models. Although much of the work presented is solid, the evidence provided to support the authors' key scientific claims is incomplete, especially as it relates to the emphasis on standardized validation and verification approaches. Notably, the level of model personalization presented in this work falls short of the threshold for what could reasonably be called a "digital twin", even by the relatively relaxed standards that have emerged in computational physiology and related fields in recent years.
-
Reviewer #1 (Public review):
Summary:
The study by Wang et al. investigates cardiac electromechanical modeling and simulation techniques, focusing on the calibration and validation of ventricular models according to ASME V&V40 standards. The researchers aim to calibrate model parameters to align with key biomarkers such as QRS duration and left ventricular ejection fraction, and validate the model against independent measurements such as displacement and strain metrics. The authors also examine the impact of parameter variations on deformation, ejection fraction, strains, and other biomarkers. The overarching aim of the study is to give "credibility to the underlying computational electromechanics framework" and to "pave the way towards credible cardiacelectromechanical Digital Twins."
Strengths:
(1) The study presents a solid …
Reviewer #1 (Public review):
Summary:
The study by Wang et al. investigates cardiac electromechanical modeling and simulation techniques, focusing on the calibration and validation of ventricular models according to ASME V&V40 standards. The researchers aim to calibrate model parameters to align with key biomarkers such as QRS duration and left ventricular ejection fraction, and validate the model against independent measurements such as displacement and strain metrics. The authors also examine the impact of parameter variations on deformation, ejection fraction, strains, and other biomarkers. The overarching aim of the study is to give "credibility to the underlying computational electromechanics framework" and to "pave the way towards credible cardiacelectromechanical Digital Twins."
Strengths:
(1) The study presents a solid validation strategy for cardiac models based on independent data.
(2) It integrates electrophysiological, mechanical, and hemodynamic biomarkers for sensitivity analysis and calibration.
Weaknesses and Limitations:
(1) Model Assumptions: The study employs simplified modeling assumptions that are not state-of-the-art, e.g.,
a) Isotropic scaling of the mesh to generate an unloaded reference geometry.
b) Simple afterload and preload models that fail to produce physiological results.
c) Simplified epicardial boundary conditions.(2) Numerical Framework:
a) The mesh resolution and/or the numerical framework used for the mechanical part appears to suffer from known numerical artifacts (locking effects), leading to overly stiff or inaccurate behavior in finite element analysis. This results in an artificially stiff response to deformation, which is compensated by setting active contraction to ten times the value reported in the literature. The authors attribute this to limitations in using ex vivo tissue measurements to represent in vivo function, although similar issues were not observed in previous works.
b) Further, the authors employ the monodomain model for the simulation of the electrical excitation and relaxation on a relatively coarse grid with an approximate edge length of 1mm. This resolution is known to be insufficient for reliable results in organ-scale electrophysiology modeling.(3) Geometrical model and digital twin: The geometrical model, taken from a public cohort and calibrated to an ECG of another individual along with population-averaged values from a databank (UK Biobank), and unrelated measurements from surgical procedures, can hardly be considered a digital twin. Further, validation of the model was then performed against data from yet another cohort.
(4) Calibration procedure: There are apparent flaws in the calibration procedure, or it is not described in sufficient detail. The authors dedicate significant effort to motivating parameter ranges, but in the end they use mostly other parameters for the calibration process, aiming to maximize left ventricular ejection fraction. It is not clear whether the chosen parameters result in, e.g., physiological calcium traces or calibrated parameters that are within physiological ranges.
(5) Goodness of fits, e.g., a direct comparison of the measured and the simulated ECG, are not provided to assess calibration quality.
(6) Due to these limitations and weaknesses, the authors fall short of achieving some of their goals, particularly establishing credibility for the underlying computational framework and in reproducing healthy pressure-volume loops, and in achieving physiological simulations while using physiological or reported ranges for the calibrated parameters.
For example, a key physiological requirement is that the right and left ventricular stroke volumes are approximately equal in a heart beating at a limit cycle, as the blood pumped by the right ventricle into the pulmonary circulation must match the amount pumped by the left ventricle into the systemic circulation. This balance is not achieved in this study.
(7) The conclusive claim that "the study paves the way towards credible electromechanical cardiac Digital Twins" is not supported. The model exhibits non-physiological behavior, requires unsupported parameter alterations (such as a 10-fold active stress scaling), and does not represent a digital twin, as model data are drawn from various unrelated, non-patient-specific sources.
Conclusion:
Overall, this reviewer considers that the study requires a major revision, including improvements in numerical methods, modeling choices, and checks for physiological behavior. Nevertheless, the provided tables with averaged values from the UK Biobank and the presented validation strategy could be valuable to the research community.
-
Reviewer #2 (Public review):
The authors present an interesting study on calibrating and validating a biventricular cardiac electromechanical model. This is an important contribution, but some questions remain about the quantitative validation and verification aspects of the study.
Major comments:
(1) The title and paper stress the importance of validation on several occasions. However, the actual validation performed is limited to the section in lines 427-439. Furthermore, it is entirely qualitative, making assessing the model's quality difficult. Most of the paper is focused on sensitivity analysis, which is also interesting but unrelated to validation. Can you include a quantitative comparison with deformation biomarkers? E.g., spatially quantify strain differences between simulation and in vivo data, or overlay the current …
Reviewer #2 (Public review):
The authors present an interesting study on calibrating and validating a biventricular cardiac electromechanical model. This is an important contribution, but some questions remain about the quantitative validation and verification aspects of the study.
Major comments:
(1) The title and paper stress the importance of validation on several occasions. However, the actual validation performed is limited to the section in lines 427-439. Furthermore, it is entirely qualitative, making assessing the model's quality difficult. Most of the paper is focused on sensitivity analysis, which is also interesting but unrelated to validation. Can you include a quantitative comparison with deformation biomarkers? E.g., spatially quantify strain differences between simulation and in vivo data, or overlay the current configuration of the geometry with MRI in various views, and calculate a displacement error norm.
(2) You mention the ASME V&V40 standards throughout your paper. Yet, you only address the "second V" validation, ignoring the "first V" verification. How did you ensure that your computational models are implemented correctly?
(3) All parameters discussed in this publication are physical parameters. What is the sensitivity of your model outputs concerning computational parameters?
-
