Predicting Short-Term Mortality after Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis.

Background: Endovascular aneurysm repair (EVAR) has revolutionized the treatment of abdominal aortic aneurysms by offering a less invasive alternative to open surgery. Understanding the factors that influence patient outcomes, particularly for high-risk patients, is crucial. The aim of this study was to determine whether machine learning (ML)-based decision tree analysis (DTA), a subset of artificial intelligence, could predict patient outcomes by identifying complex patterns in data.
Methods: This study analyzed 169 patients who underwent EVAR to identify predictors of short-term mortality (within 3 years) using DTA. Data included 23 variables such as age, gender, nutritional status, comorbidities, and surgical details. The Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the decision tree classifier.
Results: DTA identified poor nutritional status as the most significant predictor, followed by chronic kidney disease, chronic obstructive pulmonary disease, and advanced age (octogenarian). The decision tree identified 6 terminal nodes with a risk of short-term mortality ranging from 0% to 79.9%. This model had 68.7% accuracy, 65.7% specificity, and 79.0% sensitivity.
Conclusions: ML-based DTA is promising in predicting short-term mortality after EVAR, highlighting the need for comprehensive preoperative assessment and individualized management strategies.
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