Original Article
Quantification of plaque characteristics detected by dual source computed tomography angiography to predict myocardial ischemia as assessed by single photon emission computed tomography myocardial perfusion imaging
Abstract
Background: We aim to evaluate the relationship between quantitative plaque characteristics detected by dual-source computed tomography angiography (DSCTA) and myocardial ischemia as assessed by single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI).
Methods: In this study, 460 consecutive patients with suspected coronary artery disease (CAD) underwent DSCTA and stress/rest SPECT-MPI, and 179 patients with coronary artery plaques were quantitatively analyzed. Quantitative coronary artery plaque measurements including total plaque volume, the volume of non-calcified plaque, calcified plaque volume, low-density noncalcified plaque volume, total plaque burden, calcified plaque burden, non-calcified plaque burden, low-density non-calcified plaque (LDNCP) burden, remodeling index, plaque length, maximum diameter stenosis were provided by the automated software (Release 5.6.5, Circle Cardiovascular Imaging, Canada). Univariate and multivariate logistic regression analysis was performed to assess the correlation between quantitative plaque characteristics and myocardial ischemia to determine if plaque characteristics were independent of clinical risk factors and significant CAD.
Results: One hundred and seventy-nine patients (65% males) with suspected-CAD, undergoing DSCTA and stress/rest SPECT-MPI and single vessel ischemia were considered. There were significant correlations between quantitative assessment of plaque features and myocardial ischemia with details as follow: total plaque volume [25.2 (17.8–37.8) vs. 15.6 (10.3–24.9) mm3, P<0.001], calcified plaque volume (1.6±7.1 vs. 2.3±6.4 mm3, P=0.019), non-calcified plaque volume [23.6 (16.6–35.9) vs. 14.6 (10.3–22.8) mm3, P<0.001)], LDNCP volume [4.9 (2.1–8.2) vs. 2.2 (1.0–5.5) mm3, P=0.003], total plaque burden (47.6%±17.1% vs. 36.2%±17.3%, P=0.002), calcified plaque burden (1.5%±5.5% vs. 2.9%±6.9%, P=0.014), non-calcified plaque burden (46.1%±18.8% vs. 33.3%±16.4%, P=0.001), LDNCP burden [12.3% (6.4–17.7) vs. 3.3% (1.6–5.3), P<0.001], remodeling index [1.2 (1.1–1.4) vs. 1.0 (1.1-1.2), P<0.001], plaque length [4.0 (3.2–6.1) vs. 3.3 (2.8–3.8) mm, P=0.009], maximum diameter stenosis [18.1% (10.0–52.9) vs. 12.9% (6.5–18.5), P=0.011]. In a multivariate analysis, low-density noncalcified plaque burden (OR 1.33; 95% CI, 1.16–1.53, P<0.001) remained a significant predictor of myocardial ischemia after adjusting for stenosis ≥50% and gender. The area under curve (AUC) of the model containing LDNCP burden, stenosis ≥50% and gender was 0.875 (95% CI, 0.812–0.938), which was significantly better than the model with stenosis ≥50% and gender (AUC 0.729; 95% CI, 0.633–0.825).
Conclusions: Quantitative plaque characteristics detected by DSCTA are independently correlated with the incidence of myocardial ischemia by SPECT-MPI in patients with suspected CAD.
Methods: In this study, 460 consecutive patients with suspected coronary artery disease (CAD) underwent DSCTA and stress/rest SPECT-MPI, and 179 patients with coronary artery plaques were quantitatively analyzed. Quantitative coronary artery plaque measurements including total plaque volume, the volume of non-calcified plaque, calcified plaque volume, low-density noncalcified plaque volume, total plaque burden, calcified plaque burden, non-calcified plaque burden, low-density non-calcified plaque (LDNCP) burden, remodeling index, plaque length, maximum diameter stenosis were provided by the automated software (Release 5.6.5, Circle Cardiovascular Imaging, Canada). Univariate and multivariate logistic regression analysis was performed to assess the correlation between quantitative plaque characteristics and myocardial ischemia to determine if plaque characteristics were independent of clinical risk factors and significant CAD.
Results: One hundred and seventy-nine patients (65% males) with suspected-CAD, undergoing DSCTA and stress/rest SPECT-MPI and single vessel ischemia were considered. There were significant correlations between quantitative assessment of plaque features and myocardial ischemia with details as follow: total plaque volume [25.2 (17.8–37.8) vs. 15.6 (10.3–24.9) mm3, P<0.001], calcified plaque volume (1.6±7.1 vs. 2.3±6.4 mm3, P=0.019), non-calcified plaque volume [23.6 (16.6–35.9) vs. 14.6 (10.3–22.8) mm3, P<0.001)], LDNCP volume [4.9 (2.1–8.2) vs. 2.2 (1.0–5.5) mm3, P=0.003], total plaque burden (47.6%±17.1% vs. 36.2%±17.3%, P=0.002), calcified plaque burden (1.5%±5.5% vs. 2.9%±6.9%, P=0.014), non-calcified plaque burden (46.1%±18.8% vs. 33.3%±16.4%, P=0.001), LDNCP burden [12.3% (6.4–17.7) vs. 3.3% (1.6–5.3), P<0.001], remodeling index [1.2 (1.1–1.4) vs. 1.0 (1.1-1.2), P<0.001], plaque length [4.0 (3.2–6.1) vs. 3.3 (2.8–3.8) mm, P=0.009], maximum diameter stenosis [18.1% (10.0–52.9) vs. 12.9% (6.5–18.5), P=0.011]. In a multivariate analysis, low-density noncalcified plaque burden (OR 1.33; 95% CI, 1.16–1.53, P<0.001) remained a significant predictor of myocardial ischemia after adjusting for stenosis ≥50% and gender. The area under curve (AUC) of the model containing LDNCP burden, stenosis ≥50% and gender was 0.875 (95% CI, 0.812–0.938), which was significantly better than the model with stenosis ≥50% and gender (AUC 0.729; 95% CI, 0.633–0.825).
Conclusions: Quantitative plaque characteristics detected by DSCTA are independently correlated with the incidence of myocardial ischemia by SPECT-MPI in patients with suspected CAD.
