Identification of patients with acute coronary syndrome and representation of their degree of inflammation: application of pericoronary adipose tissue within different radial distances of the proximal coronary arteries

Figures

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  Figure 1. Flowchart showing inclusion and exclusion criteria for the study population. ICA, invasive coronary angiography; CTA, computed tomography angiography; CAD, coronary artery disease; ACS, acute coronary syndrome; UA, unstable angina; STEMI, ST-segment elevation myocardial infarction; NSTEMI, non-ST-segment elevation myocardial infarction.

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  Figure 2. A case example of PCAT around the proximal RCA in a 66-year-old female patient with hypertension and diabetes mellitus. The PCAT is visualized with an adipose tissue HU color table shown with color bars. The following quantified PCATs around the proximal RCA from CTA are shown: pericoronary adipose tissue attenuation between −190 and −30 HU in the corresponding cross-sectional views (A,C,E) and straightened views (B,D,F), (A,B) the PCAT quantification around the proximal RCA (10–50 mm from the ostium) within a 2-mm diameter from the outer wall of the vessel, (C,D) the PCAT quantification around the proximal RCA (10–50 mm from the ostium) within the reference diameter from the outer wall of the vessel, and (E,F) the PCAT quantification around the proximal RCA (10–50 mm from the ostium) within a 6-mm diameter from the outer wall of the vessel. PCAT, pericoronary adipose tissue; RCA, right coronary artery; HU, Hounsfield unit.

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  Figure 3. Semiautomatic measurement of the PCAT around the lesion on CCTA. PCAT is visualized with adipose tissue in the HU color table shown with color bars. The width of all lesions was measured as the reference diameter from the outer wall with a total length of 10 mm. (A) Corresponding cross-sectional views of the PCAT around the lesion are shown; (B) the segment of the lesion in a straightened view; (C) the curved multiplanar view showing the PCAT quantification of the lesion. PCAT, pericoronary adipose tissue; CCTA, computed tomography angiography.

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  Figure 4. Comparison of the FAI of the 3 main coronary arteries within different radial distances in patients. (A) Comparison of the FAI in patients with ACS; (B) comparison of the FAI in patients with stable CAD. FAI, fat attenuation index; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery; ACS, acute coronary syndrome; CAD, coronary artery disease.

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  Figure 5. Pairwise correlations between FAIs based on different sites around coronary vessels. (A) Correlation between FAIs within different radial distances around the coronary artery. The diagonal line represents the normal distribution histogram of the data of different coronary proximal FAIs. The top of the diagonal line is the correlation r value and the significance level (indicated by asterisks) for the pairwise comparison of different FAIs, where *** represents P<0.001, and the bottom of the diagonal is shown as a binary scatterplot with fitted lines. (B) Correlation between the FAI around culprit lesions and LADref. (C) Correlation between the FAI around culprit lesions and LCXref. FAI, fat attenuation index; LAD, left anterior descending artery; LCX, left circumflex artery; LADref, FAI around the proximal LAD within the reference diameter from the outer vessel wall; LCXref, FAI around the proximal LCX within the reference diameter from the outer vessel wall.

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  Figure 6. Example of a nomogram model prediction for ACS. Density plot of total score, age, Gensini score, LADref, and LAD6mm shows their distribution. For categorical variables, the distributions are reflected by the size of the box. Each point that corresponds to each variable is on the uppermost β terms. The sum of all points is the total score. The patient was 89 years old, did not have dyslipidemia or hypertension, and the LADref and LAD6mm were −102 and −105 HU, respectively, in addition with a Gensini score of 30. The point total projected at the bottom scale indicates the probability (61.5%) of the patient being identified with ACS. *, P<0.05; ***, P<0.001. LAD, left anterior descending artery; LAD6mm, FAI around the proximal LAD within a 6-mm diameter from the outer wall of the vessel; LADref, FAI around the proximal LAD within the reference diameter from the outer vessel wall; ACS, acute coronary syndrome; HU, Hounsfield units; FAI, fat attenuation index.

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  Figure 7. The prediction models for identifying patients with ACS. (A) ROC curves of the different models for identifying patients with ACS; (B) DCA for evaluating the 4 models for identifying patients with ACS. ROC, receiver operator characteristic; Cli, clinical features; G, Gensini score; LADref, FAI around the proximal LAD within reference diameter from the outer vessel wall; LAD6mm, FAI around the proximal LAD within a 6-mm diameter from the outer vessel wall; AUC, area under the curve; CI, confidence interval; DCA, decision curve analysis; ACS, acute coronary syndrome; FAI, fat attenuation index.