Patient characteristics as predictors of image quality and diagnostic accuracy of MDCT compared with conventional coronary angiography for detecting coronary artery stenoses: CORE-64 Multicenter International Trial

Abstract

Objective: The purpose of the study was to investigate patient characteristics associated with image quality and their impact on the diagnostic accuracy of MDCT for the detection of coronary artery stenosis.

Materials and methods: Two hundred ninety-one patients with a coronary artery calcification (CAC) score of <or=600 Agatston units (214 men and 77 women; mean age, 59.3+/-10.0 years [SD]) were analyzed. An overall image quality score was derived using an ordinal scale. The accuracy of quantitative MDCT to detect significant (>or=50%) stenoses was assessed using quantitative coronary angiography (QCA) per patient and per vessel using a modified 19-segment model. The effect of CAC, obesity, heart rate, and heart rate variability on image quality and accuracy were evaluated by multiple logistic regression. Image quality and accuracy were further analyzed in subgroups of significant predictor variables. Diagnostic analysis was determined for image quality strata using receiver operating characteristic (ROC) curves.

Results: Increasing body mass index (BMI) (odds ratio [OR]=0.89, p<0.001), increasing heart rate (OR=0.90, p<0.001), and the presence of breathing artifact (OR=4.97, p<or=0.001) were associated with poorer image quality whereas sex, CAC score, and heart rate variability were not. Compared with examinations of white patients, studies of black patients had significantly poorer image quality (OR=0.58, p=0.04). At a vessel level, CAC score (10 Agatston units) (OR=1.03, p=0.012) and patient age (OR=1.02, p=0.04) were significantly associated with the diagnostic accuracy of quantitative MDCT compared with QCA. A trend was observed in differences in the areas under the ROC curves across image quality strata at the vessel level (p=0.08).

Conclusion: Image quality is significantly associated with patient ethnicity, BMI, mean scan heart rate, and the presence of breathing artifact but not with CAC score at a patient level. At a vessel level, CAC score and age were associated with reduced diagnostic accuracy.

Fig. 1

Assessment of image quality as shown on four representative curved multiplanar reformations along left anterior descending coronary artery. A, Optimal image quality was defined as absence of motion artifact and optimal contrast opacification, as shown in this MDCT image of 63-year-old man. B, Adequate image quality was defined as minor imaging artifacts and noisier image, as shown in this MDCT image of 54-year-old woman. C, Poor image quality was defined as significant motion artifact, calcification artifact, or poor contrast opacification, as shown in this MDCT image of 58-year-old man. D, Nonassessable was defined as absence of contrast opacification or incomplete scan, as shown in this MDCT image of 66-year-old man.

Fig. 2

To assess diagnostic accuracy of MDCT compared with invasive angiography stratified by image quality, we considered output from MDCT as continuous measure and used the area under receiver operating characteristic (ROC) curve as a measure of diagnostic accuracy. ROC curve analysis is a method of describing the intrinsic accuracy of a diagnostic test apart from decision thresholds. An ROC curve is a plot of a diagnostic test’s sensitivity (plotted on y-axis) versus its false-positive rate (1 – specificity) (plotted on x-axis). An ROC analysis plots the relationship between sensitivity and specificity across all cut points of a test and calculates the area under the ROC curve (AUC) and its standard error. A diagnostic test with an AUC of 1 is perfectly accurate, whereas one with an AUC of 0.5 is performing no better than chance. Use of ROC curve as a measure of accuracy allows the following interpretations [50]: first, the average value of sensitivity for all possible values of specificity; second, the average value of specificity for all possible values of sensitivity; and, third, the probability that randomly selected patient with the condition of interest has diagnostic test result indicating greater suspicion than that of randomly chosen patient without the condition of interest. A, ROC curve shows diagnostic ability of coronary MDCT angiography to distinguish patient with—as opposed to without—at least one ≥ 50% coronary stenosis as defined by quantitative coronary angiography (QCA). Curve has been stratified by image quality. Difference was not observed in AUCs between image quality strata (p = 0.562). B, ROC curve shows diagnostic ability of coronary MDCT angiography to distinguish vessel with—as opposed to without—at least one ≥ 50% coronary stenosis as defined by QCA. Curve has been stratified by image quality. Statistical trend was observed in differences in AUCs between image quality strata (p = 0.079).