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Pericoronary adipose tissue attenuation predicts outcome of percutaneous intervention for chronic total occlusion
1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Aim:
To investigate the association between pericoronary adipose tissue (PCAT) attenuation (PCATA) and outcomes of chronic total occlusion (CTO) after percutaneous coronary intervention (PCI), and to establish a clinical model that can be easily generalised to predict the outcomes of PCI-CTO.
Materials And Methods:
Between September 2015 and September 2019, patients from two centres were enrolled retrospectively. The primary endpoint was a procedural success (defined as achieving residual stenosis of <30% and a grade 3 thrombolysis in myocardial infarction [TIMI] flow). The new predictive model was generated by factors that were determined by multivariate analysis. The PCATA of CTO (PCATA-CTO) score was developed by assigning 1 point for each independent predictor, and then summing all points accrued. In addition, the predictive efficacy and interobserver and intraobserver agreement of PCATA-CTO and other scoring systems based on coronary computed tomography angiography (CCTA) were compared.
Results:
A total of 201 patients (mean age 58.9 ± 10.8 years, 85% male) were enrolled. The PCI success was achieved in 76% of the lesions. PCAT was higher in the PCI success group (-72.44 ± 10.45HU versus -76.76 ± 10.54 HU, p<0.05). Multivariable analysis yielded severe calcification, lesion length ≥15 mm, and perivascular fat attenuation index (FAI) ≤-69.5HU as independent negative predictors for procedural success. The area under the receiver operating characteristic curves for the PCATA-CTO score was 0.72. Comparing the PCATA-CTO score with other predictive scores, the PCATA-CTO score showed the highest interobserver (kappa = 0.74) and intraobserver agreement (kappa = 0.90, all p<0.01).
Conclusion:
FAI ≤-69.5HU is an independent negative predictor of procedural success. The PCATA-CTO score improved the reliability of the prediction model. Its potential for clinical implementation requires evaluation.
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