Performance of the Traditional Age, Sex, and Angina Typicality–Based Approach for Estimating Pretest Probability of Angiographically Significant Coronary Artery Disease in Patients Undergoing Coronary Computed Tomographic Angiography

Abstract

Background— Guidelines for the management of patients with suspected coronary artery disease (CAD) rely on the age, sex, and angina typicality–based pretest probabilities of angiographically significant CAD derived from invasive coronary angiography (guideline probabilities). Reliability of guideline probabilities has not been investigated in patients referred to noninvasive CAD testing. Methods and Results— We identified 14048 consecutive patients with suspected CAD who underwent coronary computed tomographic angiography. Angina typicality was recorded with the use of accepted criteria. Pretest likelihoods of CAD with ≥50 diameter stenosis (CAD50) and ≥70 diameter stenosis (CAD70) were calculated from guideline probabilities. Computed tomographic angiography images were evaluated by ≥1 expert reader to determine the presence of CAD50 and CAD70. Typical angina was associated with the highest prevalence of CAD50 (40 in men, 19 in women) and CAD70 (27 men, 11 women) compared with other symptom categories ( P <0.001 for all). Observed CAD50 and CAD70 prevalences were substantially lower than those predicted by guideline probabilities in the overall population (18 versus 51 for CAD50, 10 versus 42 for CAD70; P <0.001), driven by pronounced differences in patients with atypical angina (15 versus 47 for CAD50, 7 versus 37 for CAD70) and typical angina (29 versus 86 for CAD50, 19 versus 71 for CAD70). Marked overestimation of disease prevalence by guideline probabilities was found at all participating centers and across all sex and age subgroups. Conclusion— In this multinational study of patients referred for coronary computed tomographic angiography, determination of pretest likelihood of angiographically significant CAD by the invasive angiography-based guideline probabilities greatly overestimates the actual prevalence of disease.