These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


PUBMED FOR HANDHELDS

Search MEDLINE/PubMed


  • Title: Validation of a Decision Tree to Streamline Infrainguinal Vein Graft Surveillance.
    Author: Mofidi R, McBride OMB, Green BR, Gatenby T, Walker P, Milburn S.
    Journal: Ann Vasc Surg; 2017 Apr; 40():216-222. PubMed ID: 27890844.
    Abstract:
    BACKGROUND: Duplex ultrasound (DU)-based graft surveillance remains controversial. The aim of this study was to assess the ability of a recently proposed decision tree in identifying high-risk grafts which would benefit from DU-based surveillance. MATERIALS AND METHODS: Consecutive patients undergoing infrainguinal vein graft bypass from January 2008 to December 2015 were identified from the National Vascular registry and enrolled in a duplex surveillance program. An early postoperative DU was performed at a median of 6 weeks (range: 4-9 weeks). Grafts were classified into high risk or low risk based on the findings of the earliest postoperative scan and 4 established risk factors for graft failure (diabetes, smoking, infragenicular distal anastomosis, and revision bypass surgery) using a classification and regression tree (CRT). The accuracy of the CRT model was evaluated using area under receiver operator characteristic (AROC) curve. RESULTS: About 278 vein graft bypasses were performed; 29 grafts had occluded by the first surveillance visit; 249 vein grafts were entered into surveillance. Sixty-four (23%) developed critical stenosis. Overall 30-month primary patency, primary-assisted patency, and secondary patency rates were 71.2%, 77.2%, and 80.1%, respectively. AROC for prediction of graft stenosis or occlusion was 83% (95% confidence interval [CI]: 78-87%). The sensitivity and specificity of the CRT model for prediction of graft stenosis or occlusion were 95% (95% CI: 88-98%) and 52.2% (95% CI: 45-60%). CONCLUSIONS: A prediction model based on commonly recorded clinical variables and early postoperative DU scan is accurate at identifying grafts which are at high risk of failure. These high-risk grafts may benefit from DU-based surveillance.
    [Abstract] [Full Text] [Related] [New Search]