Computational Identification of Ventricular Arrhythmia Risk in Pediatric Myocarditis

Area of Science:

• Cardiology
• Biomedical Engineering
• Medical Imaging

Background:

• Children with myocarditis face a higher risk of ventricular tachycardia (VT) due to heart muscle inflammation and remodeling.
• Current methods for stratifying VT risk in pediatric myocarditis patients are lacking.

Purpose of the Study:

• To investigate the potential of personalized computational cardiac models, derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI), for determining VT risk in children with myocarditis.

Main Methods:

• Reconstructed personalized 3D computational cardiac models from LGE-MRI scans of 12 pediatric myocarditis patients (4 with VT, 8 without).
• Incorporated patient-specific fibrosis distribution and myocardial fiber orientations into models.
• Assessed VT inducibility via rapid pacing simulations from 26 ventricular sites.

Main Results:

• Sustained reentrant VT was successfully induced in all patients with a history of clinical VT.
• No sustained reentry was induced in patients without clinical VT during simulations.

Conclusions:

• Personalized computational cardiac models utilizing LGE-MRI data can effectively differentiate VT risk in pediatric myocarditis.
• This non-invasive simulation approach shows promise for identifying children at risk of developing VT, aiding clinical decision-making.