Preoperative Coronary CT Angiography-derived Pericoronary Fat Attenuation Index Predicts In-Stent Restenosis After Stent Placement.

Authors

Shiwei Shi, Yiqiu Shi, Jingzhou Jiang, Shan Shan, Tiantian Xu, Xiaoxiao Liu, Lulu Liu, Song Wang, Tao Zhong, Yali Han, Rui Wang, Xu Ge, Feng Xu

Published in

Academic radiology. Aug 07, 2026. Epub Aug 07, 2026.

Abstract

To evaluate the predictive value of the lesion-specific preoperative pericoronary fat attenuation index (FAI) for in-stent restenosis (ISR) after stent placement.
This retrospective multicenter study, conducted between January 2016 and December 2023, included patients with coronary artery disease who underwent coronary CT angiography (CCTA) within 30 days before percutaneous coronary intervention (PCI). Patients who subsequently underwent invasive coronary angiography (ICA) were identified and stratified into ISR-positive and ISR-negative groups based on the angiographic findings. Lesion-specific pericoronary FAI was measured from preoperative CCTA. The optimal FAI threshold was determined using the Youden index. Kaplan-Meier analysis and Cox regression were used to assess ISR risk. Predictive models were evaluated using area under receiver operating characteristic curves (AUC).
A total of 223 patients (263 lesions) were included, with 68.2% male and a median age of 65.0 years. ISR occurred in 14.7% patients during a median follow-up of 17.0 months. The optimal FAI threshold for predicting ISR was -83.5 HU. Kaplan-Meier survival analysis demonstrated that lesions with FAI ≥ -83.5 HU had a significantly higher cumulative incidence of ISR than those with FAI < -83.5 HU (P < 0.001). Lesion-specific pericoronary FAI was identified as independent risk factor of ISR (HR: 8.09, 95% CI: 1.93-33.96, P = 0.004). A model including FAI showed improved predictive performance (AUC: 0.78, 95% CI: 0.72-0.83) compared to models without FAI.
Lesion-specific pericoronary FAI measured before stent placement is an independent risk factor for ISR. Incorporating FAI into clinical risk models may improve risk stratification and support individualized management after stent placement.

PMID:
42567795
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.

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