Sensitivity of Ablation Targets Prediction to Electrophysiological Parameter Variability in Image-Based Computational Models of Ventricular Tachycardia in Post-infarction Patients

Abstract

Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EP avg ) or with ±10% APD or CV (i.e., EP var ); additional simulations were also executed in some models for an extended range of these parameters. The results showed that: (1) a subset of reentries (76.2–100%, depending on EP parameter set) conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EP avg cases; (2) emergent VTs could be induced sometimes after ablation in EP avg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EP var parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.

An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine

“… Summary of cardiovascular IBM’s modules and their respective inputs and outputs with corresponding references: 1 Krishnamurthy et al, 2013 ; Lopez-Perez et al, 2015 ; 2 Krishnamurthy et al, 2013 ; Cardenes et al, 2014 ; Kayvanpour et al, 2015 ; Lopez-Perez et al, 2015 ; 3 Krishnamurthy et al, 2013 ; Grande Gutierrez et al, 2017 ; Finsberg et al, 2018 ; 4 Meoli et al, 2015 ; Bonfanti et al, 2019 ; 5 Arevalo et al, 2016 ; Trayanova et al, 2017 ; Lopez-Perez et al, 2019 ; 6 Krishnamurthy et al, 2013 ; Finsberg et al, 2018 ; Palit et al, 2018 ; 7 Kung et al, 2014 ; Grande Gutierrez et al, 2017 ; Bonfanti et al, 2019 ; 8 Choi et al, 2015 ; Seo et al, 2016 ; Harfi et al, 2017 ; 9 Arevalo et al, 2016 ; Deng et al, 2016 ; Trayanova and Chang, 2016 ; Prakosa et al, 2018 ; 10 Arevalo et al, 2016 ; Trayanova et al, 2017 ; Deng et al, 2019 ; 11 Voorhees and Han, 2015 ; Walmsley et al, 2017 ; Lee et al, 2018 ; 12 Koo et al, 2011 ; Min et al, 2012 , 2015 ; Zhang et al, 2014 ; Min et al, 2015 . …”

“…Furthermore, it was shown that the prediction of the thrombotic risk using the hemodynamic variables was validated with a higher sensitivity and specificity in comparison with the standard clinical metrics. In conclusion, this type of personalized computational modeling can be used to provide a non-invasive thrombotic risk stratification that is FIGURE 14 | Summary of cardiovascular IBM's modules and their respective inputs and outputs with corresponding references: 1 Krishnamurthy et al, 2013;Lopez-Perez et al, 2015;2 Krishnamurthy et al, 2013;Cardenes et al, 2014;Kayvanpour et al, 2015;Lopez-Perez et al, 2015;3 Krishnamurthy et al, 2013;Grande Gutierrez et al, 2017;Finsberg et al, 2018;4 Meoli et al, 2015;Bonfanti et al, 2019;5 Arevalo et al, 2016;Trayanova et al, 2017;Lopez-Perez et al, 2019;6 Krishnamurthy et al, 2013;Finsberg et al, 2018;Palit et al, 2018;7 Kung et al, 2014;Grande Gutierrez et al, 2017;Bonfanti et al, 2019;8 Choi et al, 2015;Seo et al, 2016;Harfi et al, 2017;9 Arevalo et al, 2016;Deng et al, 2016;Trayanova and Chang, 2016;Prakosa et al, 2018;10 Arevalo et al, 2016;Trayanova et al, 2017;Deng et al, 2019;11 Voorhees and Han, 2015;Walmsley et al, 2017;Lee et al, 2018;12 Koo et al, 2011;Min et al, 2012Min et al, , 2015…”

“… Summary of the Electrophysiology module’s inputs, outputs and applications. Superscripts in the figure correspond to the following references: 1 Cardenes et al, 2014 ; Kayvanpour et al, 2015 ; Lopez-Perez et al, 2015 ; 2 Cardenes et al, 2014 ; Palamara et al, 2014 ; 3 Arevalo et al, 2016 ; Trayanova et al, 2017 ; 4 Lopez-Perez et al, 2015 ; 5 Behradfar et al, 2014 ; 6 Arevalo et al, 2016 ; Deng et al, 2016 ; Trayanova and Chang, 2016 ; Prakosa et al, 2018 ; 7 Boyle et al, 2016 ; Roney et al, 2018 ; 8 Smith et al, 2011 ; Boyle et al, 2013 ; Potse et al, 2014 ; 9 Trayanova, 2011 ; Behradfar et al, 2014 ; 10 Arevalo et al, 2016 ; Trayanova et al, 2017 ; Deng et al, 2019 . …”

Electrophysiological Mechanisms Underlying T-Wave Alternans and Their Role in Arrhythmogenesis

“…In theory, simulation of the electrical wave propagation in the heart can be used to clarify the mechanisms of reentrant arrhythmia and develop new treatment strategies. Deng et al (2019) simulated ventricular tachycardia using ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging. They investigated the effects of electrophysiological parameters on ventricular tachycardia and predicted ablation targets in the heart model.…”

In-silico study of the cardiac arrhythmogenic potential of biomaterial injection therapy

“…In-silico studies of histologically-based rabbit heart models with infarction were used to develop indices for measuring vulnerability to VT, which were previously validated in clinical applications and optical mapping 22 . Prediction of electrophysiological behavior of cell-based heart repair was addressed using 3D whole-heart modeling to explore the sustainability of VF of these treatments, demonstrating the promising outcomes of computational modeling for evaluating alternative therapies for HF 23 . More specifically, patient-specific in-silico studies have allowed the quantification of scroll-wave filaments arising during VF 24 – 27 , and their association to the effectiveness of defibrillation therapies 28 – 30 .…”