Unsupervised stratification of patients with myocardial infarction based on imaging and in-silico biomarkers

TL;DR

This paper introduces a fully automated, patient-specific 3D cardiac modeling approach combining imaging and simulation to improve risk stratification for ventricular arrhythmias post-myocardial infarction.

Contribution

It presents a novel automated methodology integrating imaging and in-silico modeling for personalized arrhythmia risk assessment in MI patients.

Findings

ARRISK score correlates strongly with clinical outcomes.

Slow conduction channels are key to reentrant arrhythmias.

Model outperforms traditional risk stratification methods.

Abstract

This study presents a novel methodology for stratifying post-myocardial infarction patients at risk of ventricular arrhythmias using patient-specific 3D cardiac models derived from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images. The method integrates imaging and computational simulation with a simplified cellular automaton model, Arrhythmic3D, enabling rapid and accurate VA risk assessment in clinical timeframes. Applied to 51 patients, the model generated thousands of personalized simulations to evaluate arrhythmia inducibility and predict VA risk. Key findings include the identification of slow conduction channels (SCCs) within scar tissue as critical to reentrant arrhythmias and the localization of high-risk zones for potential intervention. The Arrhythmic Risk Score (ARRISK), developed from simulation results, demonstrated strong concordance with clinical outcomes and outperformed traditional imaging-based risk stratification. The methodology is fully automated, requiring minimal user intervention, and offers a promising tool for improving precision medicine in cardiac care by enhancing patient-specific arrhythmia risk assessment and guiding treatment strategies.