Abdominal aortic aneurysms are common and responsible for many deaths. They are treated increasingly by EndoVascular Aneurysm Repair (EVAR) rather than conventional surgery. Approximately 25% of EVAR patients require re-intervention to prevent aneurysm enlargement which can rupture despite previous repair. All EVAR patients undergo life-long surveillance for complications such as stent-graft migration or endoleak. Computed Tomography (CT) has been the ‘gold-standard’ for surveillance accounting for 65% of EVAR costs, and exposes patients to cumulative radiation and nephrotoxic contrast. Duplex Ultrasound Scanning (DUS) has been proposed as an alternative for surveillance with lesser cost and patient risk. However, clinical studies have reported varying results. The addition of microbubble contrast significantly improves endoleak detection rates, making it comparable with CT. The physical properties that affect endoleak detection with DUS have not been determined. It is also unknown specifically which endoleaks’ detection are improved by Contrast Enhanced Aortic Duplex UltraSound Scanning (CEADUSS). To investigate the physical properties of endoleaks, I constructed an EVAR phantom model with a simulated endoleak of variable velocity (fast/slow), position (near/far) and plane (anterior/lateral/posterior). Preliminary studies investigated the behavior of microbubble contrast in the phantom system, and then laboratory experiments tested subjects over 36 variable endoleaks using DUS and CEADUSS. These laboratory experiments were translated clinically with a pilot study of CEADUSS in 10 patients with endoleaks on CT not detected by DUS, undefined endoleak type or origin, or a sac enlargement with no endoleak present. My experiments reveal an insight into factors influencing ultrasound endoleak detection. With this knowledge, the use of these modalities for surveillance protocols can be increased, reducing current CT burden, radiation and nephrotoxic contrast exposure, and overall EVAR cost. Clinical assessment of an endoleak, specifically noting physical characteristics (plane, position and velocity) will improve detection and surveillance.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:667229 |
| Date | January 2013 |
| Creators | Dindyal, Shiva |
| Publisher | Queen Mary, University of London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://qmro.qmul.ac.uk/xmlui/handle/123456789/8782 |
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