Advances and challenges in personalized cardiac modeling and simulation for predicting the risk of sudden death from malignant arrhythmias in post-myocardial infarction.

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.