Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis

Pranav Bhagirath*, Fernando O. Campos, Hassan A. Zaidi, Zhong Chen, Mark Elliott, Justin Gould, Michiel J.B. Kemme, Arthur A.M. Wilde, Marco J.W. Götte, Pieter G. Postema, Anton J. Prassl, Aurel Neic, Gernot Plank, Christopher A. Rinaldi, Martin J. Bishop

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background: Implantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death after myocardial infarction. However, improved risk stratification for device requirement is still needed. Objective: The purpose of this study was to improve assessment of postinfarct ventricular electropathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modeling. Methods: ADAS 3D LV (ADAS LV Medical, Barcelona, Spain) and custom-made software were used to generate 3-dimensional patient-specific ventricular models in a prospective cohort of patients with a myocardial infarction (N = 40) having undergone LGE imaging before ICD implantation. Corridor metrics and 3-dimensional surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced ventricular tachycardias and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up. Results: Patients with an event (n = 17) had a larger interface between healthy myocardium and scar and higher VITA metrics. Cox regression analysis demonstrated a significant independent association with an event: interface (hazard ratio [HR] 2.79; 95% confidence interval [CI] 1.44–5.44; P < .01), unique ventricular tachycardias (HR 1.67; 95% CI 1.04–2.68; P = .03), mean RTT (HR 2.14; 95% CI 1.11–4.12; P = .02), and maximum RTT (HR 2.13; 95% CI 1.19–3.81; P = .01). Conclusion: A detailed quantitative analysis of LGE-based scar maps, combined with advanced computational modeling, can accurately predict ICD therapy and could facilitate the early identification of high-risk patients in addition to left ventricular ejection fraction.

Original languageEnglish
Pages (from-to)1962-1969
Number of pages8
JournalHeart Rhythm
Volume21
Issue number10
DOIs
Publication statusPublished - Oct 2024

Keywords

  • Computational Modelling
  • ICD therapy
  • Ischemic cardiomyopathy
  • Late gadolinium enhancement
  • Ventricular tachycardia

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