Prediction of Distal Aortic Enlargement after Proximal Repair of Aortic Dissection Using Machine Learning

Objectives: This study aimed to construct a risk prediction model for distal aortic enlargement in patients with type B aortic dissection (TBAD) treated with proximal thoracic endovascular aortic repair (TEVAR). Methods: From June 2010 to June 2016, patients with TBAD who underwent proximal TEVAR were retrospectively analyzed. A total of 38 clinical and imaging variables were collected. Univariable logistic regression was conducted to explore potential risk factors associated with distal aortic enlargement. Elastic net regression was employed to select significantly influential var iables. Then, machine learning algor ithms (logistic regression (LR), ar tificial neutral net work (ANN), random forest and support vector machine) were applied to build risk prediction models. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were used to evaluate the performance of these models. Results: A total of 503 patients were enrolled in this study. During the follow-up, 105 (20.9%) patients were identified as having distal aortic enlargement, and 69 (13.7%) patients were found to have distal aortic aneurysm formation. Five patients were identified with aortic rupture. True lumen collapse and multi-false lumens were two potential risk factors for distal aortic enlargement after proximal repair of TBAD. The LR model performed the best in predicting distal aortic enlargement, with the highest sensitivity (96.7%) and an AUC of 0.773. The best model for predicting distal aneurysm formation was the ANN model, which yielded the highest AUC (0.876) and a specificity of 79.1%. Conclusions: Machine learning approaches can produce accurate predictions of distal aortic enlargement after proximal repair of TBAD, which potentially benefits subsequent management.