posted on 2023-01-23, 11:32authored byTimothy M. McGloughlin, Barry J. Doyle
Abdominal aortic aneurysm (AAA) rupture remains a significant cause of death in the developed world. Current treatment approaches rely heavily on the size of the aneurysm to decide on the most appropriate time for clinical intervention and treatment. However, over recent years several alternative rupture-risk indicators have been proposed. This brief review examines some of these new approaches to AAA rupture-risk assessment, from both numerical and experimental aspects and also what the future may hold for AAA rupture-risk. While numerically-predicted wall stress, finite element analysis rupture index (FEARI), rupture potential index (RPI), severity parameter (SP), and geometrical factors such as asymmetry have all been developed and show promise in possibly helping to predict AAA rupture-risk, validation of these tools remains a significant challenge. Validation of biomechanics-based rupture indicators may be feasible by combining in vitro modeling of realistic AAA analogues together with both retrospective and prospective monitoring and modeling of AAA cases. Peak wall stress is arguably the primary result obtained from numerical analyses however, as the majority of ruptures occur in the posterior and posterior-lateral regions, the role of posterior wall stress has also recently been highlighted as potentially significant. It is also known that wall stress alone is not enough to cause rupture as wall strength plays an equal role. Therefore, should a biomechanics-based rupture-risk be implemented? There have been some significant steps, both numerically and experimentally, towards answering this and other questions relating to AAA rupture-risk prediction yet regardless of the efforts underway in several laboratories, the introduction of a numerically-predicted rupture-risk parameter into the clinicians’ decision-making process may still be quite some time away.
Funding
A new method for transforming data to normality with application to density estimation