Using CARDIac simulations to run in-silicO clinical TRIALS

Introduction

Clinical trials are a key tool for advancing medical knowledge, but they consist of a long and costly process entailing the recruitment of a representative cohort of participants to properly account for the population statistical variability.

Computational Engineering in Cardiology

Computational engineering is a promising approach to gain more insight into patients' cardiac pathologies and to test innovative medical devices before running conclusive in-vivo experiments on animals or medical trials on humans.

Challenges

This technological breakthrough, however, is limited by some technical and epistemic challenges:

1. The reliability of cardiovascular computational models depends on the accurate solution of the hemodynamics coupled with the deforming biologic tissues.
2. The resulting multi-physics solver requires immense computational power and long time-to-results.
3. A great variability among individuals exists, thus calling for a statistical approach.

Proposed Solution

For the first time, I will accomplish and employ a computational platform for determining the outcome of pathologies or device implantation by combining my GPU-accelerated multi-physics solver for the accurate solution of cardiac dynamics with an uncertainty quantification analysis to account for individual variability.

Methodology

The input parameters of the computational model will be treated as aleatory variables, whose probability distribution function will be obtained using three-dimensional datasets of cardiac configurations available to the PI's group and acquired in-vivo by the clinical members involved in the project.

Simulation Campaigns

Simulation campaigns (rather than a single simulation) will then be run in order to sweep the uncertain input distributions and obtain the synthetic population response in the case of selected pathologies like myocardial infarction and the optimal stimulation pattern for cardiac resynchronization therapy.

Conclusion

My approach removes the main barrier that keeps us from a systematic use of computational engineering to run in-silico clinical trials.