How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post‐infarction patients
Author(s) -
Trayanova Natalia A.,
Doshi Ashish N.,
Prakosa Adityo
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: systems biology and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.087
H-Index - 51
eISSN - 1939-005X
pISSN - 1939-5094
DOI - 10.1002/wsbm.1477
Subject(s) - ventricular tachycardia , context (archaeology) , computational model , cardiology , cardiac electrophysiology , medicine , personalized medicine , ablation , myocardial infarction , cardiomyopathy , ischemic cardiomyopathy , precision medicine , computer science , heart failure , bioinformatics , artificial intelligence , pathology , electrophysiology , paleontology , ejection fraction , biology
Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient‐specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient‐specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof‐of‐concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine
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