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Published on: January 15, 2017
Validation of a Decision Tree to Streamline Infrainguinal Vein Graft Surveillance
Reza Mofidi1, Olivia M B McBride2, Barnabas R Green1
1Department of Vascular Surgery, The James Cook University Hospital, Middlesbrough, UK.
Insights
A new decision tree accurately identifies high-risk vein grafts for duplex ultrasound surveillance. This approach helps target surveillance efforts to grafts most likely to fail, improving outcomes.
Area of Science:
- Vascular Surgery
- Medical Imaging
- Health Informatics
Background:
- Duplex ultrasound (DU) surveillance for vein grafts is debated.
- Identifying high-risk grafts is crucial for effective surveillance.
Purpose of the Study:
- To evaluate a new decision tree for identifying high-risk infrainguinal vein grafts.
- To determine if this model can guide duplex ultrasound surveillance.
Main Methods:
- Infrainguinal vein bypasses were analyzed from a national registry (2008-2015).
- A classification and regression tree (CRT) model used early postoperative DU scans and risk factors (diabetes, smoking, anastomosis location, revision surgery).
- Model accuracy was assessed using area under the receiver operator characteristic (AROC) curve.
Main Results:
- The CRT model demonstrated an AROC of 83% for predicting graft stenosis or occlusion.
- Sensitivity was 95% and specificity was 52.2% in identifying high-risk grafts.
- Graft patency rates at 30 months were 71.2% (primary), 77.2% (primary-assisted), and 80.1% (secondary).
Conclusions:
- The prediction model accurately identifies infrainguinal vein grafts at high risk of failure.
- This model can potentially optimize the use of duplex ultrasound surveillance for these grafts.
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.
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