Why the study?
It was unclear whether lesion-specific pericoronary adipose tissue CT attenuation (PCATa) is superior to PCATa around the proximal RCA and LAD for predicting MACE in coronary artery disease.
Does lesion-specific pericoronary adipose tissue CT attenuation (PCATa) improve risk prediction of major adverse cardiovascular events compared to proximal PCATa in patients with coronary artery disease?
Does lesion-specific pericoronary adipose tissue CT attenuation (PCATa) improve risk prediction of major adverse cardiovascular events compared to proximal PCATa in patients with coronary artery disease?
Lesion-specific pericoronary adipose tissue CT attenuation provides superior prognostic value for predicting MACE in CAD patients compared to proximal RCA or LAD measurements.
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Lesion-specific PCATa may enhance MACE risk stratification in CAD; supports prospective validation before clinical adoption.
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.
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Chen et al. (2023) studied this question.
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