Predicting outcomes following endovascular aortoiliac revascularization using machine learning.

ABSTRACT: Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease that carries non-negligible risks. Outcome prediction tools may support clinical decision-making but remain limited. We developed machine learning algorithms that predict 30-day post-procedural outcomes. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent endovascular aortoiliac revascularization between 2011-2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day post-procedural major adverse limb event (MALE) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using pre-operative fea

SUBMITTER: Li B 

PROVIDER: S-EPMC12289885 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature