E-Abstract

JACC

Lots of interesting abstracts and cases were submitted for TCTAP 2023. Below are the accepted ones after a thorough review by our official reviewers. Don’t miss the opportunity to expand your knowledge and interact with authors as well as virtual participants by sharing your opinion in the comment section!

TCTAP A-001

Prediction Model for In-Stent Restenosis After Carotid Artery Angioplasty and Stenting

By Sheng-Fu Liu, Ying-Hsien Chen, Mao-Shin Lin, Ching-Chang Huang, Chih-Fan Yeh, Cheng-Hsuan Tsai, Hsien-Li Kao

Presenter

Sheng-Fu Liu

Authors

Sheng-Fu Liu1, Ying-Hsien Chen2, Mao-Shin Lin2, Ching-Chang Huang2, Chih-Fan Yeh2, Cheng-Hsuan Tsai2, Hsien-Li Kao2

Affiliation

National Taiwan University Hospital, Hsin-Chu branch, Taiwan1, National Taiwan University Hospital, Taiwan2
View Study Report
TCTAP A-001
Carotid & Neurovascular Intervention

Prediction Model for In-Stent Restenosis After Carotid Artery Angioplasty and Stenting

Sheng-Fu Liu1, Ying-Hsien Chen2, Mao-Shin Lin2, Ching-Chang Huang2, Chih-Fan Yeh2, Cheng-Hsuan Tsai2, Hsien-Li Kao2

National Taiwan University Hospital, Hsin-Chu branch, Taiwan1, National Taiwan University Hospital, Taiwan2

Background

Carotid angioplasty and stenting (CAS) is one of the mainstream treatments for extracranial carotid artery stenoses. Carotid angioplasty and stenting in-stent restenosis (CAS ISR) is associated with an increased rate of recurrent cerebrovascular events. Multiple factors contribute to CAS ISR. The study sought to identify potential factors and construct a prediction model for CAS ISR.

Methods

We conducted a retrospective cohort study of consecutive CAS from 1997 to 2019. The enrolled subjects were divided into two groups according to CAS ISR and non-ISR. We analyzed potential variables including baseline demographic data, and angiographic and procedural characteristics. Prediction model was derived by multivariable Cox proportional hazard model. Both internal and external validation methods were conducted to examine the external generalizability of the derived model. The model was further transformed into a simplified scoring system for the ease of use.

Results

A total of 361 patients who receivedCAS were enrolled, where CAS ISR developed in 106 (29.4%) patients.Multivariate analysis showed head and neck radiation exposure history (AdjustedHR: 2.50; 95% CI: 1.42-4.38), bilateral ICA stenosis (Adjusted HR: 1.97; 95%CI: 1.28-3.03), subclavian artery (or right innominate artery) stenosis(Adjusted HR: 2.10; 95% CI: 1.15-3.85), ICA diameter < 5mm (Adjusted HR:1.98; 95% CI: 1.08-3.64), ICA total occlusion (Adjusted HR: 3.12; 95% CI:1.71-5.71) and multiple stents implant (Adjusted HR: 3.07; 95% CI: 1.71-5.52)as independent predictors for CAS ISR. Based on the finding, a CAS ISRprediction scoring system was created with the AUC of 0.768 by Bootstrapsampling. The AUC of external cohort was 0.742 (95% CI: 0.629 to 0.854) byrestricting estimates to be equal to those of the training cohort. For patientswith score higher than 39, the probability of CAS ISR was >80% and if thescore was lower than 18, the projected chance of restenosis would be less than40%.

Conclusion

Development of carotid stentrestenosis was correlated with multiple clinical factors, and a proposedprediction model for CAS ISR help identifying high-risk patients. 



Leave Comments