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AAA Risk Assessment

“Einstein’s silent killer”

The first written evidence of abdominal aortic aneurysm (AAA) dates back over 3,000 years to ancient Egypt. Since then, many advances have come about, but the disease is still prevalent. AAA is a permanent dilation of the lower part of the aorta – typically defined as being bigger than 3cm in diameter. Aneurysms are deadly in that they do not usually show any symptoms, often not until it’s too late! Many are diagnosed accidentally when checking for other conditions, or some are found through AAA screening studies. AAAs are quite common in the elderly population, particularly amongst men, however, AAAs in women appear to rupture more often. The striking thing about AAAs is that not all aneurysms will rupture and the majority will actually remain stable for the rest of the person’s life.

Albert Einstein died when his AAA ruptured. He knew he had it and it had even grown to about 12cm before it burst! However, he did not want the surgeons to repair it again (they had previously reinforced the aortic wall with cellophane). He is quoted as saying:

“I want to go when I want. It is tasteless to prolong life artificially. I have done my share, it is time to go. I will do it elegantly.”

This is not entirely uncommon in sufferers of AAA – they have lived a good life and don’t want to risk surgery to repair something that may never need it. 

So this begs the question – how do we determine which ones will burst and which ones won’t? And what patients will benefit from surgery?

The current method used to determine if the AAA is at risk of rupture is to measure the maximum diameter (using ultrasound or CT images if they have already been acquired). AAAs bigger than 5-5.5cm in diameter are considered risky, and the patient will usually be offered surgery. Now, of course, the bigger the AAA, the bigger the problem, and not many people will decide to take their chances with a 12cm time-bomb in their abdomen. Additionally, there is evidence to show that rupture risk is related to maximum diameter – however, there is also evidence showing that AAAs below 5cm can burst, as well as data indicating that not all big ones (>5cm) ever rupture. Technology has changed nearly every aspect of our lives – why not harness these advancements to improve AAA rupture-risk?

Patient-Specific Modelling (PSM)

Nowadays personalised medicine is a reality and technology has revolutionised every facet of healthcare. It is possible to use medical imaging and computational modelling to obtain a patient-specific risk assessment of the AAA.

Together with Peter Hoskins, Pankaj Pankaj and Perumal Nithiarasu, we recently edited a special issue in the International Journal of Numerical Methods in Biomedical Engineering on the topic of 'Patient-Specific Modelling'. This is the leading numerical methods journal of the field. The issue can be found here.

By taking routinely acquired CT scans (as every person who accepts surgery will have CT to plan their operation) and reconstructing the AAA into 3D, we can then use the finite element method to estimate the stress acting on the diseased aortic wall. As with any material, failure occurs when the stress exceeds the strength. Therefore, by calculating the wall stress and by estimating the wall strength (based on empirical evidence) we can predict the risk of rupturing. Importantly, this can be done in a short time frame and could be performed while the patient sits in the waiting room after their CT scan. We can also use computational fluid dynamics (CFD) to quantify and visualise the haemodynamics within the aneurysm.

Rupture Prediction

Over the past few years, we have been trying to demonstrate that modelling CAN predict the location of future AAA rupture – however, getting the clinical evidence of this is challenging. It is obviously not possible to monitor a patient in the hope their AAA bursts! Therefore, we need to rely on surgeons taking note of the location of rupture in the cases where the aneurysm actually bursts (and the location is identifiable). This is easier said than done, as most of the time rupture is a catastrophic event involving a large portion of the diseased wall. It is also an emergency operation and research activities obviously rank second to saving the person’s life.

In 2010, our vascular surgeon colleagues in Limerick (Eamon Kavanagh and Pierce Grace) were performing an open-repair of a ruptured AAA. They noticed that the ruptured was quite localised. They had the foresight to make an intra-operative sketch (Fig-a) of the rupture location and then marked it on the CT images after surgery (Fig-b). This was done with the intention of a test to see if we could predict the burst site – the location was withheld from us.

The results were quite obvious (Fig-c) – computational modelling did predict highest stresses in the proximal neck region where the rupture occurred. Since then we have searched for more conclusive data and also for more cases to convince the clinical community.

In 2013, Paul Norman had a case with localised AAA rupture on the lateral wall captured on CT. A similar blind study as before was performed and again, predictions were accurate. This time we used the CT scans taken 4 months before the rupture to see if the region of highest wall stress was the same area that burst 4 months later. It was. This was published in Cardiovascular & Interventional Radiology.

    Together with Prof. Janet Powell, we are now     retrospectively validating predicted rupture        locations with known rupture sites in cases of     the IMPROVE Trial.

Risk Assessment

There are several known obstacles that current limit the clinical applicability of computational AAA risk assessment - the unknown patient-specific material properties and wall thickness. We have recently published a new method to compute AAA wall stress without any knowledge of material properties that can also incorporate wall thickness measurements. 3D geometries are reconstructed from CT and wall thickness is measured using MRI - the data is merged together to crate a truly patient-specific geometry. We then use our new method to compute the wall stress. For more information please see the paper published in the Journal of the Mechanical Behavior of Biomedical Materials or visit the Publications page.