Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm.

Abstract

Objective: The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, peri-procedural death, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of this study was to construct and validate a random forest machine learning tool to predict two year survival following elective AAA repair.

Methods: All patients who underwent elective open or endovascular repair of AAA from 1 January 2008 to 31 March 2021 were reviewed. They were assessed using the Vascular Services Quality Improvement Program pathway involving cardiopulmonary exercise testing, contrast enhanced computerised tomography scan, and multidisciplinary assessment. Patients were followed up for at least two years. A RFC was developed using 70% of the dataset and validated using 30% to predict survival for at least two years following AAA repair.

Results: Nine hundred and twenty five patients (n = 836 men; n = 89 women) underwent elective AAA repair; 126 (13.6%) died during the first two years; 11 (1.2%) died peri-procedurally. Variable importance analysis suggested that anaerobic threshold, pre-operative haemoglobin, maximal O₂ consumption, body mass index, risk category, and forced expiratory volume in 1 second - forced vital capacity ratio were the most important contributors to the model. Sensitivity and specificity of the RFC for prediction of two year survival following surgery was 96.7% (95% CI 94.4 - 99%) and 67.1% (95% CI 61 - 72%); overall accuracy: 92.6% (95% CI 88 - 95%) (positive predictive value: 0.93, negative predictive value: 0.80); 10 fold cross validation revealed area under the receiver operator characteristic curve of 0.88.

Conclusion: RFCs based on readily available clinical data can successfully predict survival in the first two years following elective AAA repair. Such information can contribute to the risk benefit assessment when deciding to electively repair AAAs.