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

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...

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Published in:British journal of radiology 2024-01, Vol.97 (1153), p.258-266
Main Authors: Chen, Meng, Hao, Guangyu, Hu, Su, Chen, Can, Tao, Qing, Xu, Jialiang, Geng, Yayuan, Wang, Ximing, Hu, Chunhong
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Language:English
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Coronary Artery Disease
Epicardial Adipose Tissue
Humans
Retrospective Studies
Tomography, X-Ray Computed
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container_title British journal of radiology
container_volume 97
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Hao, Guangyu
Hu, Su
Chen, Can
Tao, Qing
Xu, Jialiang
Geng, Yayuan
Wang, Ximing
Hu, Chunhong
description 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). 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). Lesion-specific PCATa was an independent predictor for MACE (adjusted hazard ratio = 1.108, P 
doi_str_mv 10.1093/bjr/tqad017
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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). Lesion-specific PCATa was an independent predictor for MACE (adjusted hazard ratio = 1.108, P &lt; .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. Lesion-specific PCATa improves risk prediction of MACE in CAD, which is better than PCATa-RCA and PCATa-LAD. 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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). Lesion-specific PCATa was an independent predictor for MACE (adjusted hazard ratio = 1.108, P &lt; .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. Lesion-specific PCATa improves risk prediction of MACE in CAD, which is better than PCATa-RCA and PCATa-LAD. 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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). Lesion-specific PCATa was an independent predictor for MACE (adjusted hazard ratio = 1.108, P &lt; .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. Lesion-specific PCATa improves risk prediction of MACE in CAD, which is better than PCATa-RCA and PCATa-LAD. Lesion-specific PCATa was superior to PCATa-RCA and PCATa-LAD for MACE prediction.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>38263819</pmid><doi>10.1093/bjr/tqad017</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-6343-758X</orcidid><oa>free_for_read</oa></addata></record>
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source Oxford Journals Online
subjects Coronary Artery Disease
Epicardial Adipose Tissue
Humans
Retrospective Studies
Tomography, X-Ray Computed
title Lesion-specific pericoronary adipose tissue CT attenuation improves risk prediction of major adverse cardiovascular events in coronary artery disease
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