3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging

Hans Thijs Van Den Broek,Rutger Van De Leur,Steven Wenker,Steven A.J Chamuleau,Pieter A Doevendans,René Van Es,Frebus J Van Slochteren

doi:10.1007/s12265-019-09899-w

Abstract

Many cardiac catheter interventions require accurate discrimination between healthy and infarcted myocardia. The gold standard for infarct imaging is late gadolinium–enhanced MRI (LGE-MRI), but during cardiac procedures electroanatomical or electromechanical mapping (EAM or EMM, respectively) is usually employed. We aimed to improve the ability of EMM to identify myocardial infarction by combining multiple EMM parameters in a statistical model. From a porcine infarction model, 3D electromechanical maps were 3D registered to LGE-MRI. A multivariable mixed-effects logistic regression model was fitted to predict the presence of infarct based on EMM parameters. Furthermore, we correlated feature-tracking strain parameters to EMM measures of local mechanical deformation. We registered 787 EMM points from 13 animals to the corresponding MRI locations. The mean registration error was 2.5 ± 1.16 mm. Our model showed a strong ability to predict the presence of infarction (C-statistic = 0.85). Strain parameters were only weakly correlated to EMM measures. The model is accurate in discriminating infarcted from healthy myocardium. Unipolar and bipolar voltages were the strongest predictors.

Highlights

For many cardiac catheter interventions, accurate discrimination between healthy and infarcted myocardia is crucial [1]
Because myocardial infarction (MI) is heterogeneous of nature and individual Electromechanical mapping (EMM) parameters enable the differentiation of distinct regions, we propose that a prediction model that incorporates multiple EMM parameters could improve the detection and differentiation of MI
We investigated the use of a logistic prediction model based on multiple EMM parameters to distinguish infarcted from healthy myocardium with the most accuracy, and we evaluated the predictive accuracy of this model in a porcine model of chronic MI

Summary

Introduction

For many cardiac catheter interventions, accurate discrimination between healthy and infarcted myocardia is crucial [1]. (2019) 12:517–527 arrhythmia and to distinguish healthy from scarred myocardium [8, 9]. This technique is performed using a mapping catheter that is positioned inside the left ventricle (LV) and that is able to measure local electrical characteristics of the myocardium [10]. Electromechanical mapping (EMM) is an extension of this technique that allows for measurements of local mechanical properties as well [11]. Using these measurements, a 3D electromechanical map of the LV can be constructed

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