Development and Validation of Medical Decision Tools in Detection and Treatment of Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is a permanent dilatation of the infrarenal segment of the abdominal aorta which can be fatal if the aneurysm ruptures. Ruptured AAA is the second leading cause of global surgical mortality, and prophylactic AAA repair can decrease mortality by a tenfold if surgery is performed as an elective procedure. While screening and repair of AAA could potentially reduce AAA-related mortality, selecting patients that are likely to benefit from repair remains a complex medical decision process which has been compounded by an improved life expectancy of the general population, minimal invasive treatment methods and the increased prevalence of AAA in the elderly. The overall aim of this thesis was to improve detection and management of AAA and to develop a predictive decision tool that can assist in clinical management. This thesis has been conducted, to shed some light into issues highlighted above using New Zealand and international data. The format of this thesis was categorized into three main domains: First, the prevalence of AAA and the influence of aortic size on late survival was documented in a large cohort of individuals undergoing CT colonography for gastrointestinal symptoms in Canterbury, New Zealand; Second, a systematic review and meta-analysis of prognostic factors that might influence late survival following AAA repair were performed, and the national clinical and administrative AAA repair databases were interrogated to provide epidemiological and outcome data; Third, the factors identified from this review were applied into developing a discrete event-simulation model to predict survival following AAA repair. The model developed has been externally validated against existing national databases of patients undergoing AAA repair and it appears sufficiently accurate to predict five- year survival. The results and conclusions presented throughout this thesis fill some of the gap in AAA knowledge, and such predictive decision-making tools might help improve AAA management