Project Summary Type B Aortic Dissection (TBAD) is a lethal disease which occurs when a tear develops in the inner lining (intimal layer) of the aorta, causing the layers of the aortic wall to separate (dissect) creating “true” and “false” lumens. Complicated TBADs with presence of either organ malperfusion or aortic rupture have a high in-hospital mortality rate and require emergent surgical or endovascular therapy. Uncomplicated TBADs have been traditionally managed with optimal medical therapy (OMT) consisting of aggressive anti-hypertensive therapy and surveillance imaging. OMT results in low in-hospital mortality rates, but dismal long-term survival rates of 48- 66%, and overall intervention-free survival rates of less than 50% secondary to aortic aneurysm formation and rupture. These poor long-term outcomes support a paradigm change in the treatment of the uncomplicated TBADs. Thus, there is an urgent and unmet clinical need for promptly identifying those uncomplicated TBAD patients that will likely fail OMT in the acute phase, and thus benefit from early intervention such as Thoracic Endovascular Aortic Repair (TEVAR). Therefore, the objective of this project is to develop a risk stratification model for predicting both failure of OMT and the optimal timing of intervention in uncomplicated TBAD patients. To achieve this goal, a retrospective analysis will be conducted for about 500 uncomplicated TBAD patients from the Emory Aortic Databank. Clinical and anatomic data will be harvested from the electronic medical record and image studies to identify predictors of OMT failure. Next, using the same patient database, a series of mechanical experiments will be performed to obtain hyperelastic and failure properties of the TBAD tissues, from which rupture/tear risk metrics will be developed. Fluid-structure interaction (FSI) analyses will be validated and applied to obtain “heat maps” of hemodynamic and wall stress fields. The risk indices will be consequently extracted. For patients with longitudinal imaging data, TBAD progression will be predicted using an integrated growth and remodeling (G&R) and dissection propagation model. Critical biomechanical parameters will be identified as potential predictors of OMT failure. Finally, machine learning (ML) techniques will be used to combine clinical and biomechanical predictors to develop a multi-factorial, personalized TBAD risk stratification model. To evaluate the performance of the proposed approach, we will recruit and perform a longitudinal follow-up study of 35 acute uncomplicated TBAD patients to validate our approach by comparing the ML-model- prediction results with actual clinical outcomes.