Predicting long-term ablation targets for ventricular arrhythmia; the evolution with computational cardiology – Correspondence

Dear Editor, Cardiovascular diseases (CVD) form the lion's share of global disease burden—a paradigm shift from infectious-to non-infectious diseases. The prevalence of CVD has nearly doubled in the last three decades–271 million in 1990 to 523 million in 2019 [1]. In the US, one person dies every 37 seconds from CVD and it carries a fiscal burden of ˜$219 billion a year. Ventricular arrhythmias (VA), a subset of CVD is associated with approximately 300,000 sudden cardiac death (SCD) each year; an incidence rate of 53 per 100,000 population and accounting for 5.6% of all mortality [2].VA can be broadly classified into: 1. Ventricular Tachycardia (VT) and 2. Ventricular Fibrillation (VF). The presentation for VA can vary from asymptomatic to sudden cardiac death (SCD). Sustained VT/VF can commonly lead to SCD in the setting of myocardial infarction (MI) and heart failure [3]. The postulated mechanisms in the pathogenesis of VT include altered automaticity, triggered activity, and re-entry [4]. A novel ‘metabolic sink theory’ plausibly explain the re-entry mechanism by which arrhythmias arise in the heart [5]. As postulated by O'Rourke and colleagues, metabolic stress reduces ATP/ADP ratio in the diseased cardiac myocytes, which activates sarcolemmal K+ATP currents, resulting in the elimination of action potential in these damaged cardiac cells [5]. A drop in the cellular ATP/ADP ratio activates K+ATP channels causing the high extra-cellular fluid (ECF) K+ efflux. Consequentially, the healthy intact cells act as cellular regions of inexcitability termed as ‘metabolic sinks’ of current, which initiate heterogeneous conduction triggering ‘re-entry’ arrhythmias. Thus, cardiac injury and fibrosis delineate the altered metabolic and electrophysiological functions of cardiomyocytes. These alterations, occur in response to varied mechanical signals (stress and strain) and adjust hemodynamic pressure-volume changes to pump blood throughout the body effectively [6]. The VA induced SCD pathophysiology can be replicated by computer-generated simulation models which mimic the process of mechano-adaptation to self-generated haemodynamics by studying the ‘growth’ laws—a system of equations which experimentally induce pressure and volume load in-vivo by enforcing artificial stretches and strains [7]. Thus, employing various independent mechanical inputs, feedbacks related to the adapted in-vivo load, and late gadolinium enhancement magnetic resonance imaging (LGE-MRI) of individualized patients, simulation equations can be devised which envisage information about cardiac musculature remodelling (Fig. 1) [8].Fig. 1.: Integration of diagnostic data (LGR-MRI) to design a personalized heart simulation model based on physical principles (growth laws) and physiological phenomena (metabolic sink theory).Computational Cardiology Images used in the illustration have been derived from Trayanova NA et al. [10].To avoid dire consequences associated with VA induced SCD, cardiologists perform an interventional procedure called cardiac ablation in VA patients to normalize their cardiac rhythm reducing their chances of SCD and/or stroke [3]. However, to perform an ablation procedure on a specific patient, cardiologists rely on computational methods, which analytically solve mathematical models of diseased heart physiology via integration of a variety of medical data of that particular patient (radiographs, cardiovascular parameters, etc.), thus providing critical diagnostic framework and predicting the accurate outcome of interventional cardiac ablation procedures. However, current simulations are focused on predicting short-term responses to treatment. Mechanistically, both ablation and ischemia cause cardiac tissue death; the remaining healthy cardiac tissue grows in response to resultant changes in hemodynamic loading. ‘Growth’ laws experimentally foresee this pressure and volume load in vivo by enforcing artificial stretches, thus potentially predict growth in myocardial fiber in both longitudinal- and radial direction, and the ratio of longitudinal-to-radial growth. These laws seem to have enormous potential to uncover the endogenous cardiac regeneration archetype and repair by providing predictive models of cardiac growth which in-turn evaluate current treatment and future clinical decision-making [9]. Computational cardiology introduces computational methods in cardiology research and analytically solves mathematical models of the physiology of the diseased heart, utilizing patient-specific clinical data and providing recommendations to the clinicians for the most optimal interventional procedure for the patient. We hereby present a pipeline for predicting the long-term heart response to ablation interventions utilizing partial differential equations (PDE) and patient-specific LGE-MRI scans that model cardiac remuscularization (Fig. 2). The primary step in constructing a 3-dimensional (3D) computer model of the patient's diseased heart is the acquisition of LGE-MRI scans to reconstruct the heart geometry, fiber, and sheet orientation, explicitly meant to estimate myocardial regional scar formation and/or fibrosis. This computational heart model is expected to predict heart's post-ablation growth response utilizing individual PDE and LGE-MRI, thus providing improved prognosis and treatment of VA variant of CVD. Indeed, these models accurately resemble diseased hearts and can be used to study mechanistic relationships of cardiac pathologies [10].Fig. 2.: ‘Pipeline’ for the image-based reconstruction of heart geometry and structure. Computational Cardiology Images used in the illustration have been derived from Trayanova NA et al. [10].As such, there is a pressing need to devise simulations of long-term heart response to interventions in order to avoid multiple ablation procedures on patients. This challenge is even more prominent as heart remodels itself constantly due to disease progression or regression and/or treatment. And as we shift from umbrella medicine to precision medicine, the application of computational cardiology to predict adverse cardiovascular events can help design treatment interventions specific to the patient's distinctive heart condition. Ethical approval Not applicable. The current article is a correspondence article and no primary data involving human or animal subjects was shared, so an ethical approval was not required. Sources of funding No funding relationships to disclose. Author contribution Arsalan Moinuddin: Conceptualization, Data curation, Writing-Original draft preparation, Writing-Original draft preparation, Writing- Reviewing and Editing, Visualization, Supervision. Yashendra Sethi: Conceptualization, Data curation, Writing-Original draft preparation, Writing- Reviewing and Editing. Ashish Goel: Data curation, Writing Original draft preparation, Writing- Reviewing and Editing. Nidhi Uniyal: Writing- Reviewing and Editing. Research registration Unique Identifying number (UIN) Name of the registry: Not applicable. Unique Identifying number or registration ID: Not applicable. Hyperlink to your specific registration (must be publicly accessible and will be checked): Not applicable. Guarantor Arsalan Moinuddin. Yashendra Sethi. Provenance and peer review Not commissioned, internally peer-reviewed. Data statement All the required data are available in the manuscript. Any further data/details can be requested from the corresponding author. Declaration of competing interest Authors declare that they have no conflicts of interest. Arsalan Moinuddin Yashendra Sethi Ashish Goel Nidhi Uniyal 1School of Sports and Exercise, University of Gloucestershire, UK 2Government Doon Medical College, H.N.B. Uttarakhand Medical Education University, Dehradun, Uttarakhand, India 3Gautam Buddha Chikitsa Mahavidyalaya, Ras Bihari Bose Subharti University, Dehradun, Uttarakhand, India E-mail addresses:[email protected]