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

TL;DR

AutoVARP is a standardized, automated framework that enhances reproducibility and efficiency in virtual arrhythmia inducibility testing within cardiac electrophysiology models, facilitating broader clinical and research applications.

Contribution

It introduces autoVARP, a scalable, open-source framework that standardizes and automates virtual induction studies, addressing reproducibility and sharing challenges in cardiac electrophysiology simulations.

Findings

Demonstrated in a large virtual cohort, highlighting reproducibility improvements.

Showcased in a biventricular mesh, confirming adaptability.

Identified numerical artifacts with improper setups.

Abstract

Simulations of Cardiac Electrophysiology 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. 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 standardization providing autoVARP, a framework for standardization of virtual arrhythmia inducibility studies, built upon openCARP and the carputils framework. 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. Our approach also ensures numerical efficiency by separating the induction study into four stages: (i) pre-pacing with forCEPSS, (ii) S1 pacing tor 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 autoVARP in a biventricular mesh. AutoVARP addresses effectively the current gap in standardization and reproducibility of results providing a uniform methodology that can be implemented even by non expert users. AutoVARP 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.