Development of a Decision Tree to Streamline Infrainguinal Vein Graft Surveillance

Background Duplex ultrasound (DU) remains the gold standard for identification and grading of infrainguinal vein graft stenosis. However, DU-based graft surveillance remains controversial. The aim of this study was to develop a decision tree to identify high-risk grafts which would benefit from DU-based surveillance. Methods Consecutive patients undergoing infrainguinal vein graft bypass were enrolled in a DU surveillance program. An early postoperative DU was performed at a median of 6 weeks (range 4–9). Based on the findings of this scan and 4 established risk factors for graft failure (diabetes, smoking, infragenicular distal anastomosis, revision bypass surgery), a classification and regression tree (CART) was created to stratify grafts into grafts which are at high and low risk of developing severe stenosis or occlusion. The accuracy of the CART model was evaluated using area under receiver operator characteristic curve (ROC). Results Of 796 vein graft bypasses performed (760 patients), 64 grafts were occluded by the first surveillance visit and 732 vein grafts were entered into surveillance program. The CART model stratified 299 grafts (40.8%) as low-risk and 433 (59.2%) as high-risk grafts. One hundred twenty-six (17.2%) developed critical vein graft stenosis. Overall, 30-month primary patency, primary-assisted and secondary patency rates were 76.2%, 83.6%, and 85.3%, respectively. The area under ROC curve for the CART model was 0.88 (95% confidence interval 0.81–0.94). Primary graft patency rates were higher in low-risk versus high-risk grafts (log rank 186, P