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Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis.
Bhagirath, Pranav; Campos, Fernando O; Zaidi, Hassan A; Chen, Zhong; Elliott, Mark; Gould, Justin; Kemme, Michiel J B; Wilde, Arthur A M; Götte, Marco J W; Postema, Pieter G; Prassl, Anton J; Neic, Aurel; Plank, Gernot; Rinaldi, Christopher A; Bishop, Martin J.
Afiliação
  • Bhagirath P; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands. Electronic address: p.bhagirath@amsterdamumc.nl.
  • Campos FO; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Zaidi HA; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Chen Z; Department of Cardiology, Royal Brompton & Harefield NHS Foundation Trust, London, United Kingdom.
  • Elliott M; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, St. Thomas' Hospital, London, United Kingdom.
  • Gould J; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, St. Thomas' Hospital, London, United Kingdom.
  • Kemme MJB; Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands.
  • Wilde AAM; Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands.
  • Götte MJW; Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands.
  • Postema PG; Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands.
  • Prassl AJ; Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria.
  • Neic A; NumeriCor GmbH, Graz, Austria.
  • Plank G; Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria; NumeriCor GmbH, Graz, Austria.
  • Rinaldi CA; Department of Cardiology, St. Thomas' Hospital, London, United Kingdom.
  • Bishop MJ; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Heart Rhythm ; 2024 Apr 24.
Article em En | MEDLINE | ID: mdl-38670247
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Heart Rhythm Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Heart Rhythm Ano de publicação: 2024 Tipo de documento: Article