Imaging-based simulations for predicting sudden death and guiding ablation

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Natalia Trayanova envisions the future of cardiology and electrophysiology advancing through personalised medicine and computational simulations. Trayanova is the director of the Computational Cardiology Lab at Johns Hopkins University (Baltimore, USA), as well as the Murray B Sachs professor in the Department of Biomedical Engineering and the university’s Institute for Computational Medicine. In this commentary, she discusses her research and the potentials of combining clinical imaging with computational modelling to create a unique virtual heart–one for every patient.Natalia Trayanova envisions the future of cardiology and electrophysiology advancing through personalised medicine and computational simulations. Trayanova is the director of the Computational Cardiology Lab at Johns Hopkins University (Baltimore, USA), as well as the Murray B Sachs professor in the Department of Biomedical Engineering and the university’s Institute for Computational Medicine. In this commentary, she discusses her research and the potentials of combining clinical imaging with computational modelling to create a unique virtual heart–one for every patient.

Simulation-driven engineering has put rockets in space, aeroplanes in the sky, and self-driving cars on the road. Computational approaches, however, have rarely been applied to human health. In the arena of cardiac care, this reality is slowly changing. The recent emphasis on personalised medicine has provided a significant impetus for the development of predictive approaches combining clinical imaging and computational modelling that can be applied to the diagnosis and treatment of heart rhythm disorders.

A major avenue in this direction is the creation and translation into clinical practice of novel imaging- and simulation-based strategies for predicting an individual’s risk of sudden cardiac death (SCD) and for the non-invasive planning of optimal personalized anti-arrhythmia therapies. Clinical decisions regarding the stratification of patients for SCD risk resulting from arrhythmia and for determining the optimal targets for anti-arrhythmia ablation therapies could greatly benefit from such targeted developments, since current approaches, although life-saving, remain often sub-optimal.

The first application of such non-invasive imaging- and simulation-based strategies has been in patients with structural heart disease. The strategy incorporates personalised information regarding the distribution of structural remodelling in the patient ventricles or atria as obtained from clinical scans. This information is combined with knowledge about the biology and physics of heart cells and the electrical current flow through the cardiac syncytium, resulting in the construction of a virtual replica of the patient’s heart—i.e. the patient’s virtual heart. Using a virtual heart, the patient’s unique lethal heart rhythm disorder can be studied, and personalised treatment devised. Researchers can poke and prod the virtual organ in ways that are simply not possible with a flesh-and-blood heart. The hope is that with such models at the patient bedside, therapies can be improved, invasiveness of diagnostic procedures minimised, and health-care costs reduced.

 

Computational prediction of sudden cardiac death risk in patients with myocardial infarction

How do you construct a virtual heart?

For both SCD risk stratification and ablation planning, a three-dimensional (3D) computer model of the patient’s individual ventricles or atria is constructed from the contrast-enhanced clinical magnetic resonance imaging (MRI) data. The heart model incorporates the patient’s ventricular or atrial geometry and structural remodelling (scarring resulting from ischaemic cardiomyopathy, atrial fibrosis, etc.) as well as electrical functions from the sub-cellular to the organ. The model is thus capable of representing the interplay between abnormal myocardial structure and electrical instability in the heart that results in the generation and maintenance of ventricular or atrial arrhythmias. A virtual multi-site delivery of electrical stimuli from a large number of ventricular or atrial locations at different distances to remodelled tissue ensures that the ventricular/atrial substrate’s propensity to develop structural-remodelling-related arrhythmias can be comprehensively evaluated.

For example, using patient-derived virtual hearts to predict optimal ablation targets in patients with infarct-related ventricular tachycardia (VT) could substitute the invasive mapping to determine the arrhythmia critical pathways with the evaluation of model VT circuits. From the simulated VTs, ablation targets can be determined, and then implemented in the virtual heart as non-conductive lesions to simulate ablation and determine whether the lesions result in VT non-inducibility from any pacing site.

It is possible that following simulated ablation, new VT circuits could be formed in the virtual heart. They can be evaluated and the appropriate ablation targets are determined. The process can be repeated until complete VT non-inducibility is achieved. The resulting set of ablation targets are then loaded into the 3D electro-anatomical mapping system, so that the ablation catheter is navigated during the procedure to the model-predicted targets. The hope is that VT ablation would then be swift and precise, eliminating VT circuits with minimum lesions and maximum chance of VT non-inducibility.

Virtual-heart arrhythmia risk prediction for two patients. Top panel shows geometric replicas of hearts reconstructed from patients’ MRIs. Bottom panels show propagation of electrical wave in virtual hearts, with lines representing the activation time. Despite the injury in the low risk heart, the electrical wave swept through uniformly, triggering strong uniform contraction. In high risk heart, arrhythmia developed.

Using virtual hearts to stratify arrhythmia risk in post-myocardial infarct (MI) patients

In this application, the multi-site delivery of stimuli was used to determine the patient’s heart propensity to develop infarct-related ventricular arrhythmias. We termed this non-invasive SCD risk assessment approach the ‘virtual-heart arrhythmia risk predictor’ (VARP). A retrospective proof-of-concept study by Arevalo et al1 used data from 41 patients with prior MI and LVEF<35% to test the predictive capability of VARP. Patients were followed for the primary end-point of appropriate implantable cardiac device (ICD) firing due to ventricular arrhythmia or cardiac death. Follow-up time averaged 4.8±2.9 years. VARP predictive capabilities were compared to LVEF as well as to other clinical metrics that have been used to predict arrhythmic risk. Furthermore, at the time of ICD implantation, 32 of the 41 patients in the cohort underwent clinical electrophysiological testing; for these 32 patients, VARP assessment was also compared with the outcome of the clinical testing. Statistical analysis demonstrated that a positive VARP test was significantly associated with the primary end-point, with a four-fold higher arrhythmia risk than patients with negative VARP test. The comparison of VARP with LVEF revealed that only the VARP outcome was significantly associated with arrhythmic risk in this cohort. Among the 32 patients who had both VARP and invasive testing, the hazard ratio for VARP was 10.4, vs. 1.7 for clinical electrophysiological testing. Should the predictive capability of the approach be demonstrated in larger studies, VARP has the potential to radically change the process of SCD risk assessment and patient selection for prophylactic ICD implantation, eliminating many unnecessary ICD implantations and their associated complications. Importantly, VARP could be applied to patients with prior MI but preserved LVEF>30–35%, who could also be at significant risk for arrhythmia because of their remodelled myocardium, but are generally not targeted for therapy under current clinical recommendations. Because current guidelines for ICD placement target low LVEF patients who constitute only one-third of SCD victims, VARP has the potential to identify increased SCD risk in a much larger number of at-risk patients.

Initial successes of the virtual heart approach provide a glimpse into the potential of this technology. Further development of the approach for different cardiac diseases and rhythm disorders could help usher in a number of new personalised medicine approaches in cardiac patient care.

Natalia Trayanova is a professor and laboratory director at Johns Hopkins University, Baltimore, USA. She is a fellow of the Heart Rhythm Society, American Heart Association, Biomedical Engineering Society, and the American Institute for Medical and Biological Engineering.

Reference

  1. Arevalo HJ, Vadakkumpadan F, Guallar E, Jebb A, Malamas P, Wu KC, Trayanova NA. Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nature Communications. 2016;7:11437.
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