Prediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis.

INTRODUCTION

A simple risk stratification model to predict aneurysm sac shrinkagein patients undergoing endovascular aortic repair (EVAR) for abdominal aortic aneurysms (AAA) was developed using machine learning-based decision tree analysis.

METHODS

One hundred nineteen patients with AAA who underwent elective EVAR at Tokyo Medical University Hospital between November 2013 and July 2019 were included in the study. Predictors of aneurysm sac shrinkage identified in univariable analysis (P < 0.05) were entered into the decision tree analysis.

RESULTS

Univariable analysis revealed significant differences between patients with and without aneurysm sac shrinkage in the variables of age (<75 y or ≥75 y), current smoking, operative type II endoleak, and preoperative pulse wave velocity (PWV) (<1800 cm/s or ≥1800 cm/s). The decision tree showed that preoperative PWV was the most relevant predictor, followed by operative type II endoleak and current smoking, and identified 6 terminal nodes with likelihoods of aneurysm sac shrinkage ranging from 5.6% to 63.6%.

CONCLUSIONS

We established a decision tree model with 3 variables (preoperative PWV, operative type II endoleak, and current smoking) to predict aneurysm sac shrinkage in patients undergoing EVAR for AAA. This classification model may help identify patients with a high or low likelihood of aneurysm sac shrinkage.