Lesion-specific pericoronary adipose tissue CT attenuation improves risk prediction of major adverse cardiovascular events in coronary artery disease

Author:

Chen Meng1,Hao Guangyu1,Hu Su1,Chen Can1,Tao Qing1,Xu Jialiang2,Geng Yayuan3,Wang Ximing1,Hu Chunhong1ORCID

Affiliation:

1. Department of Radiology, The First Affiliated Hospital of Soochow University , Suzhou, Jiangsu 215006, China

2. Department of Cardiology, The First Affiliated Hospital of Soochow University , Suzhou, Jiangsu 215006, China

3. Department of Research and Development, ShuKun Technology Co., Ltd , Beijing 100102, China

Abstract

Abstract Objectives To determine whether lesion-specific pericoronary adipose tissue CT attenuation (PCATa) is superior to PCATa around the proximal right coronary artery (PCATa-RCA) and left anterior descending artery (PCATa-LAD) for major adverse cardiovascular events (MACE) prediction in coronary artery disease (CAD). Methods Six hundred and eight CAD patients who underwent coronary CTA from January 2014 to December 2018 were retrospectively included, with clinical risk factors, plaque features, lesion-specific PCATa, PCATa-RCA, and PCATa-LAD collected. MACE was defined as cardiovascular death, non-fatal myocardial infarction, unplanned revascularization, and hospitalization for unstable angina. Four models were established, encapsulating traditional factors (Model A), traditional factors and PCATa-RCA (Model B), traditional factors and PCATa-LAD (Model C), and traditional factors and lesion-specific PCATa (Model D). Prognostic performance was evaluated with C-statistic, area under receiver operator characteristic curve (AUC), and net reclassification index (NRI). Results Lesion-specific PCATa was an independent predictor for MACE (adjusted hazard ratio = 1.108, P < .001). The C-statistic increased from 0.750 for model A to 0.762 for model B (P = .078), 0.773 for model C (P = .046), and 0.791 for model D (P = .005). The AUC increased from 0.770 for model A to 0.793 for model B (P = .027), 0.793 for model C (P = .387), and 0.820 for model D (P = .019). Compared with model A, the NRIs for models B, C, and D were 0.243 (−0.323 to 0.792, P = .392), 0.428 (−0.012 to 0.835, P = .048), and 0.708 (0.152-1.016, P = .001), respectively. Conclusions Lesion-specific PCATa improves risk prediction of MACE in CAD, which is better than PCATa-RCA and PCATa-LAD. Advances in knowledge Lesion-specific PCATa was superior to PCATa-RCA and PCATa-LAD for MACE prediction.

Funder

Jiangsu Medical Association

Suzhou Municipal Health Commission

Publisher

Oxford University Press (OUP)

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

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