Advances and challenges in personalized cardiac modeling and simulation for predicting the risk of sudden death from malignant arrhythmias in post-myocardial infarction
- 1Department of Cardiology, Capital Medical University Affiliated Anzhen Hospital
Abstract
Sudden cardiac death (SCD) represents a major global public health challenge, with malignant ventricular arrhythmias being its primary cause. This review focuses specifically on post-myocardial infarction (post-MI) ischemic cardiomyopathy—the most prevalent substrate for malignant ventricular arrhythmias. Current clinical practice predominantly relies on single functional indices, such as left ventricular ejection fraction, for risk stratification. This approach suffers from significant limitations in predictive accuracy, leading to dilemmas in the prophylactic application of implantable cardioverter-defibrillators, including both under-implantation and over-implantation. Personalized cardiac modeling and simulation technology, an advanced embodiment of the digital twin concept in cardiovascular medicine, offers a highly promising non-invasive solution to overcome these limitations. The core of this technology lies in the deep integration of personalized, multi-modal clinical imaging data, particularly late gadolinium enhancement sequences from cardiac magnetic resonance imaging. This enables the precise construction of individualized cardiac anatomical substrates that reflect the spatial distribution of myocardial fibrosis and scar. Building upon this anatomical foundation, multi-scale computational models are established, spanning from microscopic ion channel kinetics and cellular electrophysiological activity to macroscopic tissue conduction properties. These models systematically simulate core pro-arrhythmic factors under pathological conditions, including key ion channel remodeling, the coupling effects of non-myocytes, and alterations in anisotropic conduction properties of myocardial tissue. Ultimately, by implementing programmed electrical stimulation protocols on the computational heart model, the susceptibility to arrhythmias is dynamically assessed, achieving quantitative and visual stratification of an individual's SCD risk. This technological framework not only non-invasively reveals the dynamic electrical instability underlying static anatomical substrates but also demonstrates broad application prospects in understanding complex arrhythmia mechanisms, assisting in clinical therapeutic decision-making, and advancing the shift towards precision medicine models.
Related
- Myocardial Work Index Dispersion as a Potential Predictor of Ventricular Arrhythmias in Patients with Heart Failure with Reduced Ejection Fraction
- Cardiac unloading via a transvalvular pump prevents arrhythmias following myocardial ischemia / reperfusion by preserving coronary microcirculation and cardiomyocyte nanostructure
- Prevalence, causes, and predictors of 30-day readmissions after acute myocardial infarction with ventricular arrhythmias: a cohort study using the 2016–2017 national readmissions database
- Evaluation of Clinical SYNTAX Score in Predicting New-Onset Atrial Fibrillation and Long-Term Prognosis in Patients Hospitalized with Non-ST Segment Elevation Myocardial Infarction
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review
BibTeX
@article{zhao2026advances,
title = {{Advances and challenges in personalized cardiac modeling and simulation for predicting the risk of sudden death from malignant arrhythmias in post-myocardial infarction}},
author = {Erwei Zhao and Liting Cheng and Huanfu Liu and Zefeng Wang and Yongquan Wu},
journal = {Frontiers in Cardiovascular Medicine},
year = {2026},
month = oct,
doi = {10.3389/fcvm.2026.1879657},
url = {https://www.frontiersin.org/articles/10.3389/fcvm.2026.1879657/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Advances and challenges in personalized cardiac modeling and simulation for predicting the risk of sudden death from malignant arrhythmias in post-myocardial infarction
AU - Erwei Zhao
AU - Liting Cheng
AU - Huanfu Liu
AU - Zefeng Wang
AU - Yongquan Wu
PY - 2026
DA - 2026/10/15
JO - Frontiers in Cardiovascular Medicine
DO - 10.3389/fcvm.2026.1879657
UR - https://www.frontiersin.org/articles/10.3389/fcvm.2026.1879657/full
AB - Sudden cardiac death (SCD) represents a major global public health challenge, with malignant ventricular arrhythmias being its primary cause. This review focuses specifically on post-myocardial infarction (post-MI) ischemic cardiomyopathy—the most prevalent substrate for malignant ventricular arrhythmias. Current clinical practice predominantly relies on single functional indices, such as left ventricular ejection fraction, for risk stratification. This approach suffers from significant limitations in predictive accuracy, leading to dilemmas in the prophylactic application of implantable cardioverter-defibrillators, including both under-implantation and over-implantation. Personalized cardiac modeling and simulation technology, an advanced embodiment of the digital twin concept in cardiovascular medicine, offers a highly promising non-invasive solution to overcome these limitations. The core of this technology lies in the deep integration of personalized, multi-modal clinical imaging data, particularly late gadolinium enhancement sequences from cardiac magnetic resonance imaging. This enables the precise construction of individualized cardiac anatomical substrates that reflect the spatial distribution of myocardial fibrosis and scar. Building upon this anatomical foundation, multi-scale computational models are established, spanning from microscopic ion channel kinetics and cellular electrophysiological activity to macroscopic tissue conduction properties. These models systematically simulate core pro-arrhythmic factors under pathological conditions, including key ion channel remodeling, the coupling effects of non-myocytes, and alterations in anisotropic conduction properties of myocardial tissue. Ultimately, by implementing programmed electrical stimulation protocols on the computational heart model, the susceptibility to arrhythmias is dynamically assessed, achieving quantitative and visual stratification of an individual's SCD risk. This technological framework not only non-invasively reveals the dynamic electrical instability underlying static anatomical substrates but also demonstrates broad application prospects in understanding complex arrhythmia mechanisms, assisting in clinical therapeutic decision-making, and advancing the shift towards precision medicine models.
SN - 2297-055X
ER -