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已发表论文
冠状动脉 CT 血管造影中 CT 血流储备分数和脂肪衰减指数对经皮冠状动脉介入治疗后支架内再狭窄的预测价值
Authors Li B, Zhang Z, Wei X, Song T, Zhao Y, Wang W
Received 16 July 2025
Accepted for publication 26 October 2025
Published 3 November 2025 Volume 2025:21 Pages 1535—1548
DOI https://doi.org/10.2147/TCRM.S554207
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 5
Editor who approved publication: Professor Garry Walsh
Beibei Li,1,2 Zhiguo Zhang,2 Xianglei Wei,2 Tianzeng Song,3 Yi Zhao,4 Wei Wang4
1Graduate School of Dalian Medical University, Dalian, Liaoning, 116000, People’s Republic of China; 2Department of Radiology, Linyi Central Hospital, Yishui County, Linyi, Shandong, 276400, People’s Republic of China; 3Department of Orthopeadic Surgery, Linyi Central Hospital, Yishui County, Linyi, Shandong, 276400, People’s Republic of China; 4Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu, 225000, People’s Republic of China
Correspondence: Wei Wang, Email waywang@126.com
Aim: To investigate the predictive value of CT fractional flow reserve (CT-FFR) and fat attenuation index (FAI) based on Coronary CT Angiography (CCTA) for in-stent restenosis (ISR) in patients with CAD after PCI.
Methods: Patients with coronary heart disease who were followed up after coronary stent implantation were retrospectively collected, and clinical data, stent features and imaging characteristics were recorded. The Spearman test was used to analyze the correlation between CT-FFR, FAI and ISR. Univariate and multivariate logistic regression were used to determine the independent influencing factors of ISR, and a nomogram model was constructed.
Results: A total of 378 patients were ultimately included. Among them, there were 120 cases in the ISR group and 258 cases in the non-ISR group. Multivariate analysis revealed that CT-FFR2cm, ΔCT-FFR, FAIlesion, stent length, ΔCT-FFR/length, hyperlipidemia, and lipoprotein(a) are independent predictors of ISR. The ROC analysis demonstrated that ΔCT-FFR had the highest predictive accuracy for ISR, with an AUC of 0.923 (95% CI: 0.889– 0.957). Several clinical prediction models were developed, among which Model 3 displayed the highest predictive performance (AUC: 0.958, 95% CI, 0.932– 0.984). A statistically significant difference was observed between Model 1 and Model 2 (AUC: 0.925 vs 0.950, P < 0.05). However, no significant difference was found between Model 1 and Model 3 (AUC: 0.925 vs 0.928, P > 0.05).
Conclusion: ΔCT-FFR and peri-stent FAI, as independent predictors of ISR after PCI in patients with coronary heart disease, have a high predictive value for ISR. In addition, the FAI around the stent has incremental value for CT-FFR. It is worth noting that, compared with clinical data, imaging features show higher predictive value.
Keywords: coronary CT angiography, CT fractional flow reserve, perivascular fat attenuation index, coronary heart disease, in-stent restenosis
1Graduate School of Dalian Medical University, Dalian, Liaoning, 116000, People’s Republic of China; 2Department of Radiology, Linyi Central Hospital, Yishui County, Linyi, Shandong, 276400, People’s Republic of China; 3Department of Orthopeadic Surgery, Linyi Central Hospital, Yishui County, Linyi, Shandong, 276400, People’s Republic of China; 4Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu, 225000, People’s Republic of China
Correspondence: Wei Wang, Email waywang@126.com
Aim: To investigate the predictive value of CT fractional flow reserve (CT-FFR) and fat attenuation index (FAI) based on Coronary CT Angiography (CCTA) for in-stent restenosis (ISR) in patients with CAD after PCI.
Methods: Patients with coronary heart disease who were followed up after coronary stent implantation were retrospectively collected, and clinical data, stent features and imaging characteristics were recorded. The Spearman test was used to analyze the correlation between CT-FFR, FAI and ISR. Univariate and multivariate logistic regression were used to determine the independent influencing factors of ISR, and a nomogram model was constructed.
Results: A total of 378 patients were ultimately included. Among them, there were 120 cases in the ISR group and 258 cases in the non-ISR group. Multivariate analysis revealed that CT-FFR2cm, ΔCT-FFR, FAIlesion, stent length, ΔCT-FFR/length, hyperlipidemia, and lipoprotein(a) are independent predictors of ISR. The ROC analysis demonstrated that ΔCT-FFR had the highest predictive accuracy for ISR, with an AUC of 0.923 (95% CI: 0.889– 0.957). Several clinical prediction models were developed, among which Model 3 displayed the highest predictive performance (AUC: 0.958, 95% CI, 0.932– 0.984). A statistically significant difference was observed between Model 1 and Model 2 (AUC: 0.925 vs 0.950, P < 0.05). However, no significant difference was found between Model 1 and Model 3 (AUC: 0.925 vs 0.928, P > 0.05).
Conclusion: ΔCT-FFR and peri-stent FAI, as independent predictors of ISR after PCI in patients with coronary heart disease, have a high predictive value for ISR. In addition, the FAI around the stent has incremental value for CT-FFR. It is worth noting that, compared with clinical data, imaging features show higher predictive value.
Keywords: coronary CT angiography, CT fractional flow reserve, perivascular fat attenuation index, coronary heart disease, in-stent restenosis