Machine Learning-Based Random Forest to Predict 3-Year Survival after Endovascular Aneurysm Repair.

purposeEndovascular aneurysm repair (EVAR) is widely used to treat abdominal aortic aneurysms (AAAs), but mid-term survival remains a concern. This study aims to develop a machine learning-based random forest model to predict 3-year survival after EVAR.

methodsA random forest model was trained using data from 176 EVAR patients, of whom 169 patients were retained for analysis, incorporating 23 preoperative and perioperative variables. Model performance was evaluated using 5-fold cross-validation.

resultsThe model achieved an area under the receiver-operating characteristic curve (AUC) of 0.91, with an accuracy of 81.1%, a sensitivity of 81.6%, a specificity of 80.9%, and an F1 score of 0.66. Feature importance analysis identified poor nutritional status (geriatric nutritional risk index <101.4), compromised immunity (neutrophil-to-lymphocyte ratio >3.19), chronic kidney disease (CKD), octogenarian status, chronic obstructive pulmonary disease (COPD), small aneurysm size, and statin use as the top predictors of 3-year mortality. The average values of the AUC, accuracy, sensitivity, specificity, and F1 score across the 5-folds were 0.76 ± 0.10, 73.9 ± 5.8%, 60.4 ± 1.9%, 77.8 ± 0.7%, and 0.59 ± 0.17, indicating consistent performance across different data subsets.

conclusionsThe random forest model effectively predicts 3-year survival after EVAR, indicating key factors such as poor nutritional status, compromised immunity, CKD, octogenarian status, COPD, small aneurysm size, and statin use.