Digital Twin-Based Risk Stratification Integrating Aortic Shape and Wall Mechanics in Acute Uncomplicated Type B Aortic Dissection

Objective This study evaluated whether patient-specific digital twins integrating 3D aortic anatomy and structural wall stress could identify patients with acute uncomplicated type B aortic dissection (AUTBAD) at high risk for adverse aortic events. Methods We analyzed CTAs from 50 patients extracted from a 400 AUTBAD patient database treated with optimal medical therapy from 1999-2025. Digital twin models were constructed, and statistical shape modeling and reduced-order fluid-structure interaction analysis were performed. Anatomic shape and biomechanical stress data were integrated into models predicting freedom from aortic events, defined as intervention or aortic-related death, using leave-one-out cross-validation. A cross-validated risk score was generated for each patient, with higher scores indicating higher event risk. Results At diagnosis, the median descending aortic diameter was 40.61 mm. During follow-up, 35 patients experienced an aortic event: 28 interventions (56%) and 7 aortic-related deaths (14%). Stratification by descending aortic diameter above or below 40.61 mm yielded higher- versus lower-risk aortic event-free survival of 72.0% versus 84.0% at 1 year and 27.8% versus 56.7% at 6 years (p=0.035; C-index=0.590). In the cross-validated multidimensional model combining morphology and structural stress, higher- versus lower-risk event-free survival was 60.0% versus 96.0% at 1 year and 12.5% versus 68.9% at 6 years (p<0.001; C-index=0.792). Conclusions Digital twin modeling integrating morphology and wall stress provided greater risk separation than univariable predictors in AUTBAD patients. With independent validation, patient-specific AUTBAD risk stratification using geometric and biomechanical data may ultimately contribute to identification of high-risk patients who may benefit from early endovascular repair.