In silico modelling of changes in spinal cord blood flow after endovascular aortic aneurysm repair

TL;DR

This study develops a computational model to simulate blood flow changes in the spinal cord after endovascular aortic aneurysm repair, aiming to predict spinal cord ischemia risk using patient-specific data.

Contribution

It introduces an in-silico modeling approach using open-source tools and routine imaging to analyze post-surgical haemodynamic changes in the spinal cord.

Findings

Segmental artery flow to the spinal cord decreased by 51.86% post-surgery.

Postoperative increases in flow observed in non-spinal vascular beds.

Spinal cord-supplying arteries showed increased TAWSS and decreased RRT and ECAP.

Abstract

Aims: To develop an in-silico model of the aorta and its spinal cord-supplying branches, and to characterise haemodynamic changes following aortic aneurysm (AA) repair with such a model. The work is motivated by the risk of spinal cord ischaemia (SCI) and paraplegia, serious complications that can arise from disruption of spinal cord perfusion during AA surgery. Methods: SimVascular was used to retrospectively create models of a 76 year old female patient's aorta pre- and post- uncomplicated endovascular AA repair. The full extent of the aorta and its branches, including vessels supplying the spinal cord, was segmented. Pulsatile flow simulations were conducted under the assumption of rigid vessel walls, with patient-specific inlet and three-element Windkessel models for the outlet boundary conditions on the SimVascular Gateway Cluster. Results: Postoperatively, segmental artery flow to the spinal cord decreased by 51.86% due to exclusion of lumbar and posterior intercostal arteries by the stent graft. Spinal cord-supplying arteries showed increased TAWSS (+5.2%) and reduced RRT and ECAP, with minimal change in OSI. Consistent with redistribution away from the spinal territory, modest postoperative flow increases were observed in non-spinal vascular beds, including the legs (+6.09%), reno-visceral vessels (+5.89%), and supra-aortic branches (+5.97%). Across vascular territories, visceral arteries had the highest TAWSS and lowest RRT and ECAP, while leg arteries had the lowest TAWSS and highest RRT and ECAP; supra-aortic vessels exhibited the highest OSI. Conclusion: This study lays a foundation for computational prediction of SCI risk. It leverages in-silico modelling, using an open-source software pipeline and routine medical imaging, to assess spinal cord blood flow alterations after aortic surgery. This may forge a path towards a clinical decision-making tool.