AutoVARP - A framework for automated reproducible inducibility testing in computational models of cardiac electrophysiology.

BACKGROUND AND OBJECTIVE: Simulations of cardiac electrophysiology (CEP) are gaining momentum beyond basic mechanistic studies, as an approach for supporting clinical decision making. The potential for in silico technologies observed from the research community is immense, with studies demonstrating significantly improved therapeutical outcome with little to no additional burden for patients. Studies replicating virtually induction protocols in post myocardial infarction patients are among the most reproduced and promise to identify non invasively ablation targets for therapeutical intervention. Two main factors hinder the translation of these technologies from pure research to applications: virtually no reproducibility of results, and lack of standardized procedures. Inspired by a previously published virtual induction study by Arevalo et al. (2016), We address the issues of reproducibility and efficiency providing auto-VARP, a framework for automated virtual arrhythmia inducibility studies, built upon openCARP and the carputils framework.

METHODS AND RESULTS: Standardization relies on the previously published forCEPSS framework and is ensured by defining the whole induction study with input files that can be easily shared to ensure reproducibility since the whole pipeline relies on open software. Our approach also ensures numerical efficiency by separating the induction study into four stages: (i) pre-pacing with forCEPSS, (ii) S1 pacing for each steady state, (iii) S2 induction with different extrastimuli, (iv) testing of sustenance of induced reentries. We demonstrate the approach in a large virtual subject cohort to investigate numerical artifacts that may arise when improper setups are provided to perform virtual induction, and additionally showcase auto-VARP in a biventricular mesh.

CONCLUSIONS: auto-VARP addresses effectively the current gap in automation and reproducibility of results providing a uniform methodology that can be implemented even by non expert users. auto-VARP is highly scalable and adaptable to markedly different anatomies. Although less flexible than in house implementations it provides automated tools to share setups and does not require re-implementation of any process.