Aaa Risk Assessment-integrating Morphologic, Biomechanic, Molecular and Clinical Risk Factors to Improve Decision Making

In an effort to improve abdominal aortic aneurysm (AAA) rupture risk assessment several studies have identified demographic, morphometric, thrombus-related, biomechanic, and biological characteristics that associate with rupture risk. However, it is unlikely that parameters from either the biomechanics or the biology aspect of AAA rupture alone can capture the multifactorial pathology of AAA rupture, accurately predict the rupture risk and thus be trusted by surgeons for rupture risk assessment. Therefore, a paradigm shift to our approach is needed that will better capture the multifactorial pathology of AAA. The structure of a decision tree is one that is appealing intuitively and congruent with methods of decision-making that a physician already uses on many occasions [1]. Towards such an approach, the purpose of this study was to build a decision tree that classifies AAAs into high versus low growth rate groups, based on demographic, morphometric, thrombus-based, biomechanic, and biologic characteristics that can all be measured from 3D imaging data and routine blood sample analysis.