Multimodal Artificial Intelligence Model for Prediction of Abdominal Aortic Aneurysm Shrinkage After Endovascular...

The goal of the study described in this protocol is to build a multimodal artificial intelligence (AI) model to predict abdominal aortic aneurysm (AAA) shrinkage 1 year after endovascular aneurysm repair (EVAR).

In this retrospective observational multicenter study, approximately 1000 patients will be enrolled from hospital records of 5 experienced vascular centers. Patients will be included if they underwent elective EVAR for infrarenal AAA with initial assisted technical success and had imaging available of the same modality preoperatively and at 1-year follow-up (CTA-CTA or US-US). Data collection will include baseline and vascular characteristics, medication use, procedural data, preoperative and postoperative imaging data, follow-up data, and complications.

The cohort will be stratified into 3 groups of AAA remodeling based on the maximum AAA diameter difference between the preoperative and 1-year postoperative moment. Patients with a diameter reduction of ≥5 mm will be assigned to the AAA shrinkage group, cases with an increase of ≥5 mm will be assigned to the AAA growth group, and patients with a diameter increase or reduction of <5 mm will be assigned to the stable AAA group. Furthermore, an additional fourth group will include all patients who underwent an AAA-related reintervention within the first year after EVAR, because both the complication and the reintervention might have influenced the state of AAA remodeling at 1 year. The preoperative and postoperative CTA scans will be used for anatomical AAA analysis and biomechanical assessment through semi-automatic segmentation and finite element analysis. All collected clinical, biomechanical, and imaging data will be used to create an AI prediction model for AAA shrinkage. Explainable AI techniques will be used to identify the most descriptive input features in the model. Predicting factors resulting from the AI model will be compared with conventional univariate and multivariate logistic regression analyses to find the best model for the prediction of AAA shrinkage. The study is registered at www.clinicaltrials.gov under the registration number NCT06250998.

This study aims to develop a robust and high-performance AI model for predicting AAA shrinkage one-year after EVAR, with great potential for optimizing both EVAR treatment and follow-up. The model can identify cases with an initially lower chance of early AAA shrinkage, in whom EVAR-treatment could be tailored by including additional preoperative coil embolization, active sac management and/or postoperative tranexamic acid therapy, which have shown to promote AAA shrinkage rate but are too complex and costly to perform in all patients. The model could aid in stratification of post-EVAR surveillance based on the patient's individual risk and possibly decrease follow-up for the 40-50% of patients who will experience AAA sac shrinkage. Overall, the AI prediction model is expected to improve patient survival and decrease the number of reinterventions after EVAR and associated healthcare costs.