P039 In Silico Safety and Performance Assessment of Vagus Nerve Stimulation with Metamodeling-Based Uncertainty/Variability Quantification
Antonino M. Cassara'*1, Javier Garcia Ordonez1, Werner Van Geit1, Esra Neufeld1
1Foundation for Research on Information Technologies in Society (IT’IS), Zurich, Switzerland
*Email: cassara@itis.swiss
Introduction
Safety and efficacy assessments of medical devices are key to regulatory submissions. We established anin silicopipeline for neural interface assessment and demonstrated it for a vagus nerve stimulation (VNS) cuff electrode. It combines histology-based electromagnetic, electrophysiology, and thermal simulations, as well as tissue damage predictors, with high throughput screening of data from the NIH SPARC program [1], and systematic uncertainty quantification to assess safety and shed light on primary concerns, dominant factors, variability, and model limitations. This study serves to guide the development and application of regulatory-gradein silicomethodologies for safer, more effective medical technologies.
Methods Evaluated quantities-of-interest (QoIs) included iso-percentiles of dosimetric exposure quantities, current intensities and densities, charge injection, off-target stimulation predictors, tissue heating, as well as tissue damage predictors – all as a function of varying degrees of fiber recruitment. The pipeline is implemented on the o2S2PARC platform for open and FAIR computational modeling [2] using modeling functionalities from Sim4Life [3]. Variability was quantified through iteration over histological samples from different subjects, multiple sources of numerical uncertainty were quantified, and model parameter uncertainties (e.g., tissue properties, fiber statistics) were propagated using advanced surrogate modeling methodologies.
Results
E-field thresholds were compared to safety guidelines [4], temperature increases to FDA limits [5], and the commonly applied (though questionably relevant) Shannon’s Criteria [6] was evaluated. The surrogate model-based uncertainty propagation permitted to shed light on complex correlations between model parameters and QoIs, fully accounting for non-linear dependencies and multi-factor interactions, and revealing novel mechanistic insights. Discussion The fully automatized pipeline enables quantitative safety assessment for a wide variety neural interfaces for bioelectronic medicine. It supports electrode design optimization towards improved safety and effectivity, and the identification of safe therapeutic windows. The systematic uncertainty analysis using advanced surrogate-model-based techniques illustrates the value of o2S2PARC intelligent metamodeling framework and scalable cloud resources for exploring large parameter spaces. In conclusion, carefully executed, regulatory-gradein silicosafety assessment is a powerful tool for accelerating medical device innovation.
Figure 1. Figure 1. (a) Safety assessment pipeline on o2S2PARC; (b) histology-based nerve model-generation and population with electrophysiological fiber models; (c) visualization of selected dosimetric and thermal distributions; (d) cross sections through the surrogate models with associated interpolation uncertainty; uncertainty propagation of EM and thermal tissue properties through QoI surrogate models. Acknowledgements “This research is supported by the NIH Common Fund’s SPARC program under award3OT3OD025348-01S8” References [1] NIH SPARC program, USA.https://commonfund.nih.gov/sparc [2] Neufeld E. et al. 2020. SPARC’s Open Online Simulation Platform: o2S2PARC. FASEB J 34(S1). [3] Sim4Life, ZMT Zurich MedTech AG, Zurich, Switzerland. [4] ICNIRP. 2010. Guidelines for exposure to time-varying EM fields (1 Hz–100 kHz). Health Phys 99(6):818-36. [5] FDA guidance on thermal effects:https://www.fda.gov. [6] Shannon RV. 1992. Safe levels for electrical stimulation. IEEE Trans Biomed Eng 39(4):424-6.
