Diagnostic accuracy of computed tomography coronary angiography according to pre-test probability of coronary artery disease and severity of coronary arterial calcification. The CORE-64 (Coronary Artery Evaluation Using 64-Row Multidetector Computed Tomography Angiography) International Multicenter Study

Abstract

Objectives: The purpose of this study was to assess the impact of patient population characteristics on accuracy by computed tomography angiography (CTA) to detect obstructive coronary artery disease (CAD).

Background: The ability of CTA to exclude obstructive CAD in patients of different pre-test probabilities and in presence of coronary calcification remains uncertain.

Methods: For the CORE-64 (Coronary Artery Evaluation Using 64-Row Multidetector Computed Tomography Angiography) study, 371 patients underwent CTA and cardiac catheterization for the detection of obstructive CAD, defined as ≥50% luminal stenosis by quantitative coronary angiography (QCA). This analysis includes 80 initially excluded patients with a calcium score ≥600. Area under the receiver-operating characteristic curve (AUC) was used to evaluate CTA diagnostic accuracy compared to QCA in patients according to calcium score and pre-test probability of CAD.

Results: Analysis of patient-based quantitative CTA accuracy revealed an AUC of 0.93 (95% confidence interval [CI]: 0.90 to 0.95). The AUC remained 0.93 (95% CI: 0.90 to 0.96) after excluding patients with known CAD but decreased to 0.81 (95% CI: 0.71 to 0.89) in patients with calcium score ≥600 (p = 0.077). While AUCs were similar (0.93, 0.92, and 0.93, respectively) for patients with intermediate, high pre-test probability for CAD, and known CAD, negative predictive values were different: 0.90, 0.83, and 0.50, respectively. Negative predictive values decreased from 0.93 to 0.75 for patients with calcium score <100 or ≥100, respectively (p = 0.053).

Conclusions: Both pre-test probability for CAD and coronary calcium scoring should be considered before using CTA for excluding obstructive CAD. For that purpose, CTA is less effective in patients with calcium score ≥600 and in patients with a high pre-test probability for obstructive CAD.

Figure 1. Panels A and B. Receiver-Operator-Characteristics Curves for CTA Diagnostic Accuracy According to Calcium Scores and Pretest Probability

Panel A presents the receiver-operator-characteristic (ROC) curves for all patients, patients with calcium score less than 600, and patients with calcium score ≥ 600, describing the diagnostic performance of quantitative CT angiography (CTA) to identify a ≥ 50% coronary arterial stenosis in a patient when compared to quantitative coronary angiography (QCA). The dotted line is a calibration curve; to identify the corresponding CTA threshold point extend a vertical line from a point on the ROC curve to the calibration curve and then a horizontal line to the right ordinate, which gives the CTA threshold. For example, a sensitivity of 88% and a false positive rate (1 – specificity) of 13% correspond to a threshold point of 50% stenosis detected by CTA. The area under the curve (AUC) was 0.93 for all patients, 0.93 for patients with calcium score less than 600, and 0.80 for patients with calcium score ≥ 600 (p=0.063 vs. <600). Panel B shows the ROC curves for patients with intermediate pretest probability of coronary artery disease, high pretest probability, and known coronary artery disease. The area under the curve (AUC) was 0.93 for patients with intermediate pretest probability of coronary artery disease, 0.92 for patients with high pretest probability, and 0.93 for patients with known coronary artery disease.

Figure 2. Panels A and B. Predictive Values According to Pretest Probability and Presence/Extent of Coronary Calcification

Shown is a plot of positive and negative predictive values for patients with intermediate pretest probability of coronary artery disease (Panel A, n=172), and patients with either high pretest probability or known coronary artery disease (Panel B, n=196 combined), grouped into patients without (calcium score 0), mild (1-99), moderate (100-399), and severe coronary calcification (>400).

Figure 3. Predictive Values of a Diagnostic Test According to Disease Prevalence

Shown are positive and negative predictive values as a function of disease prevalences ranging from 0-100% for a diagnostic test with a sensitivity and specificity of 90%, chosen as arbitrary but typical values. The reference line indicates the disease prevalence observed in this study (63%). One can appreciate the large shifts in predictive values according to low vs. high disease prevalence.