Unsupervised Stratification of Patients With Myocardial Infarction Based on Imaging and In-Silico Biomarkers.

Area of Science:

• Cardiovascular Imaging and Simulation
• Computational Electrophysiology
• Precision Medicine

Background:

• Post-myocardial infarction (MI) patients face significant risk of ventricular arrhythmias (VA).
• Current risk stratification methods often lack precision and fail to identify all high-risk individuals.
• Personalized risk assessment is crucial for effective management of cardiac conditions.

Purpose of the Study:

• To develop and validate a novel methodology for stratifying VA risk in post-MI patients.
• To integrate patient-specific 3D cardiac models with computational electrophysiology for rapid risk assessment.
• To enhance precision medicine approaches in cardiac care.

Main Methods:

• Utilized patient-specific 3D cardiac models derived from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) imaging.
• Employed the fast electrophysiology solver, Arritmic3D, for personalized computational simulations.
• Generated thousands of simulations to assess arrhythmia inducibility and predict VA risk.

Main Results:

• Identified slow conduction channels (SCCs) within scar tissue as critical determinants of reentrant arrhythmias.
• Localized high-risk zones within the cardiac models for targeted interventions.
• Developed the Arrhythmic Risk Score (ARRISK), showing strong concordance with clinical outcomes and outperforming traditional methods.

Conclusions:

• The novel methodology offers automated, rapid, and accurate VA risk stratification for post-MI patients.
• Patient-specific 3D cardiac models and computational simulations enhance precision in cardiac care.
• This approach promises to improve treatment strategies and patient outcomes by guiding interventions to high-risk areas.