Bradycardias are very common heart condition characterized by an abnormally slow heart rate. Pacemaker therapy is the most effective treatment. It consists in a pulse generator and leads that are implanted in the heart to deliver an electrical pulse so as to elicit cardiac contraction when the natural conduction system fails. Among other parameters, the capture threshold (the minimum energy required to stimulate the heart) is critical to assess and predict pacemaker performance at implantation and on the long-term. In the weeks following implantation, this threshold may change due to fibrosis associated with the inflammatory response of the tissue. This may result in loss-of-capture, requiring device reprogramming or lead extraction.
We developed a three-dimensional computational model and a cloud-based platform to perform an in-silico trial for new prospective pacemaker bradycardia leads, which can be used to assist device companies in the early development phase of new lead designs.
Our computational pipeline, implemented in a web-based platform, allows to compute the proportion of a population for which the initial device setting no longer captures, based on user-defined lead geometric and electric properties and population statistics. Our model combines the bidomain equations with the electrical equations of the pulse generator through contact impedance boundary conditions. It accounts for the energy delivery of a lead design to the tissue, and output threshold curves. Its credibility has been assessed by rigorous verification and validation in the context of capture threshold measurements during preclinical animal experiments. As a proof of concept, the pipeline was used to compare the performance of MicroPort's VEGA™ lead and a custom lead design. The results show that the new design decreases the threshold to capture in one over three tested pulse durations, which is an improvement, but achieves poorer performance after the onset of fibrosis.