Abdominal aortic aneurysm (AAA) is when the diameter of the abdominal aorta is larger than 30 mm. The primary risk associated with AAA is aortic rupture which is fatal in 68-90% of cases.  Once a patient is diagnosed with AAA, the AAA is monitored via abdominal ultrasound. The rationale for the regular surveillance is that the risk of rupture is low for AAA less than 55 mm in size but increases dramatically in diameter larger than 55 mm. Early surgery on patients with smaller AAA diameters (lower risk of rupture) has a higher mortality rate than taking no action. Despite numerous researches done about prediction of AAA size, there is a lack of a design which predicts the risk of surgery and rates of rupture and mortality at surveillances. This research tried to address the necessity of surveillance or surgery, rupture rate, and mortality rate in different time periods.
We applied Monte-Carlo simulation technique to a growth model based on Bayesian Analysis to simulate 10,000 hypothetical patients.  Using Cholesky decomposition matrix on the patient cohort data  kept the generated data correlated to the original data. Probability of each possible growth trajectory and cumulative risk of rupture is computed by Bayesian Analysis for each patient. Mortality and rupture rates are calculated individually applying Monte-Carlo simulation on meta-analysis papers. The risk of rupture increases in the patients with increase in the size and the mortality rate increases with the time. Generating all these predictions for numerous numbers of hypothetical patients and comparing the risk of mortality due to rupture versus the risk of mortality from surgery gives us a unique opportunity to analyze the effect of the surveillance and surgery decisions on patients’ mortality. In this presentation we will utilize this mathematical technic to identify surgery and surveillance protocols to maximize the patients’ survival.
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