Impact of lesion preparation-induced calcium fractures in vascular intervention for atherosclerotic disease: in silico assessment

Abstract

Atherosclerosis is the most common form of obstructive vascular disease and is the predominant cause of mortality world-wide. Endovascular interventions like balloon angioplasty and stent implantation have dominated as therapies with tremendous impact and yet are least effective in most severe disease – especially those with heavily calcified lesions.

Intravascular lithotripsy (IVL) has been proposed to “prepare” lesions and optimize endovascular intervention with the idea of removing and/or modifying lesions resistive stiffness so as to make balloon or stent placement more effective. Despite clinical enthusiasm, there remains a lack of understanding as to how this occurs, and which lesions would be most amenable to and most affected by IVL.

The range and extent of lesions are substantial presenting a formidable challenge in managing their modification. This complexity hampers the extrapolation of findings from both clinical and preclinical models. In silico models offer a means by which to examine diverse lesion morphologies and a range of lesion modifications to address these deficiencies, and in particular to understand if there is a correlation between calcium morphology alteration and improvement of stenting outcomes. We build a computational platform to connect stenting outcomes to IVL induced calcium modification. Three models were inspired by clinical optical coherence tomography image analyses and a stenting procedure was simulated for a number of variations within each model. Results show that expansion of stents and treated arteries rose with the volume of tissue affected and excised. For one particular model, stent expansion reached a local maximum. 3 In silico models provide a valuable perspective for considering complex vascular interventions – not only in simulating effects that are challenging to recapitulate in preclinical models but in helping develop a tool that can predict susceptible candidate lesions and help determine the ideal extent of lesion modification to optimize overall effect.