A digital twin study on thoracic endovascular repair for Stanford type B aortic dissection

Abstract

Thoracic endovascular aortic repair (TEVAR) remains the treatment of choice for Stanford type B aortic dissection (TBAD). In this study, we apply a novel machine learning-based (ML-based) digital twin (DT) method to study the relationship between preoperative indicators, inflammation markers, endoleaks (EL) and long-term outcome of patients who received TEVAR. Our result shows that most postoperative indicators are closely related to their preoperative indicators. We also find that height and onset time of TBAD may be related to the occurrence of EL, while long-term outcome is more related to age, body weight and proximal diameter of endograft applied. Besides, our models successfully predict the occurrence of EL and long-term outcome of patients to some extent based only on pre-operative and operative information. In conclusion, this study represents a novel application of DT technology in clinical settings, which could lead to predictive, preventive and personalized treatments in future.