Prediction of Major Adverse Events After Endovascular Aneurysm Repair Using a Machine Learning Model

Abstract

Predicting major adverse events (MAE), including type I and III endoleaks (ELs), after endovascular aneurysm repair (EVAR) is essential for clinical decision-making. No clinical decision instruments can reliably predict for MAE after EVAR. We developed a machine learning model (MLM) for predicting MAE within 3 years after elective EVAR using preoperative demographic information and aneurysm geometry. MLMs infer complex interaction without prespecification, possibly increasing the prediction accuracy compared with logistic regression models (LRMs). Patients with intact infrarenal abdominal aortic aneurysms who had undergone EVAR with preoperative M2S reconstruction from 2010 to 2017 at a single institution were analyzed. The MAEs included aneurysm- or intervention-related death within 30 days, aneurysm rupture, the development of type I or III ELs, greater than three reinterventions, or EVAR explantation. Patients with <3 years of follow-up were excluded. Seventy-four preoperative predictors were evaluated. Of these, 30 were geometric factors derived from M2S models. The remaining factors were demographic. The performance of LRMs, random forest models (RFMs), and gradient-boosted models (GBMs) were evaluated using a 75% and 25% training and testing split, fivefold cross-validation, and receiver operating characteristic performance metric. A total of 250 patients were included (mean age, 71.9 ± 8.6 years; 86% male). The abdominal aortic aneurysm diameter was 56.0 ± 10.6 mm, and the infrarenal neck length was 24.6 ± 4.5 mm. Of the 250 patients, 40 (16%) had experienced MAE: 11 had died within 30 days, 3 had experienced aneurysm rupture, 10 had developed a type I EL, 10 had developed a type III EL, 10 had required graft explantation, and 4 had undergone four or more reinterventions. The most significant 45 predictive variables are shown in the Fig. All discrimination and calibration metrics favored the MLM over the LRM. The area under the curve for the LRM was 0.70 compared with 0.83 for the RFM and 0.77 for the GBM. The LRM classification accuracy was 68% compared with 84% for the RFM and GBM. The GBM showed superior calibration performance (Hosmer-Lemeshow P = .201). The removal of the geometric parameters significantly degraded the performance for all the models (likelihood ratio P < .001). The MLM displayed superior discrimination and calibration compared with the LRM in predicting MAE for the first 3 years after elective EVAR, possibly owing to the complex interaction terms in the predictive factors. The anatomic characteristics were more influential than the demographic data in predicting for MAE. Our MLM might predict for MAE after EVAR and thereby aid in selecting patients for EVAR, nonoperative observation, or open repair.