Coronary artery calcium score and pre-test probabilities as gatekeepers to predict and rule out perfusion defects in positron emission tomography

Abstract

Background: Little is known about the gatekeeper performance of coronary artery calcium score (CACS) before myocardial perfusion positron emission tomography (PET), compared with updated pre-test probabilities from American and European guidelines (pre-test-AHA/ACC, pre-test-ESC).

Methods: We enrolled participants without known coronary artery disease undergoing CACS and Rubidium-82 PET. Abnormal perfusion was defined as summed stress score ≥ 4. Using Bayes' formula, pre-test probabilities and CACS were combined into post-test probabilities.

Results: We included 2050 participants (54% male, mean age 64.6 years) with median CACS 62 (IQR 0-380), pre-test-ESC 17% (11-26), pre-test-AHA/ACC 27% (16-44), and abnormal perfusion in 437 participants (21%). To predict abnormal perfusion, area under the curve of CACS was 0.81, pre-test-AHA/ACC 0.68, pre-test-ESC 0.69, post-test-AHA/ACC 0.80, and post-test-ESC 0.81 (P < 0.001 for CACS vs. each pre-test, and each post-test vs. pre-test). CACS = 0 had 97% negative predictive value (NPV), pre-test-AHA/ACC ≤ 5% 100%, pre-test-ESC ≤ 5% 98%, post-test-AHA/ACC ≤ 5% 98%, and post-test-ESC ≤ 5% 96%. Among participants, 26% had CACS = 0, 2% pre-test-AHA/ACC ≤ 5%, 7% pre-test-ESC ≤ 5%, 23% post-test-AHA/ACC ≤ 5%, and 33% post-test-ESC ≤ 5% (all P < 0.001).

Conclusions: CACS and post-test probabilities are excellent predictors of abnormal perfusion and can rule it out with very high NPV in a substantial proportion of participants. CACS and post-test probabilities may be used as gatekeepers before advanced imaging. Coronary artery calcium score (CACS) predicted abnormal perfusion (SSS ≥ 4) in myocardial positron emission tomography (PET) better than pre-test probabilities of coronary artery disease (CAD), while pre-test-AHA/ACC and pre-test-ESC performed similarly (left). Using Bayes' formula, pre-test-AHA/ACC or pre-test-ESC were combined with CACS into post-test probabilities (middle). This calculation reclassified a substantial proportion of participants to low probability of CAD (0-5%), not needing further imaging, as shown for AHA/ACC probabilities (2% with pre-test-AHA/ACC to 23% with post-test-AHA/ACC, P < 0.001, right). Very few participants with abnormal perfusion were classified under pre-test or post-test probabilities 0-5%, or under CACS 0. AUC: area under the curve. Pre-test-AHA/ACC: Pre-test probability of the American Heart Association/American College of Cardiology. Post-test-AHA/ACC: Post-test probability combining pre-test-AHA/ACC and CACS. Pre-test-ESC: Pre-test probability of the European Society of Cardiology. SSS: Summed stress score.

Figure 1

Prevalence of endpoints by CACS and pre-test probability categories. The prevalence of all endpoints increased across categories of predictors. Endpoints were rare in the lowest category of each predictor, except low MFR. Pre-test probabilities cannot be > 52% and therefore do not reach rule-in levels for coronary artery disease (> 85%). Only high CACS identifies patients with high prevalence of endpoints. CACS, coronary artery calcium score; MFR, myocardial flow reserve; pre-test-AHA/ACC, pre-test probability from American Heart Association/American College of Cardiology guidelines; pre-test-ESC, pre-test probability from European Society of Cardiology guidelines

Figure 2

Receiver operating characteristic analysis. To predict all endpoints, CACS had a significantly higher AUC than pre-test probabilities. To predict low MFR, pre-test-ESC had a significantly higher AUC than pre-test-AHA/ACC, but pre-test probabilities did not differ from each other for abnormal perfusion and severe ischemia. AUC, area under the curve; CACS, coronary artery calcium score; MFR, myocardial flow reserve; pre-test-AHA/ACC, pre-test probability from American Heart Association/American College of Cardiology guidelines; pre-test-ESC, pre-test probability from European Society of Cardiology guidelines

Figure 3

Bayesian probability revision using coronary artery calcium score. Probability revision curves were calculated with Bayes’ formula and interval likelihood ratios for CACS. To use these graphs, start on the x-axis at the pre-test probability (by any method), move up to the line for measured CACS, then move left to the post-test probability on the y-axis. CACS 0 and ≥ 2500 have a strong impact on post-test probabilities, particularly CACS 0 for severe ischemia (post-test probability < 5% for pre-test probabilities up to 52%, the maximal value). CACS, coronary artery calcium score

Figure 4

Bayesian combined post-test probabilities. Pre-test probabilities from AHA/ACC or ESC were combined with CACS using Bayes’ formula into post-test probabilities. Endpoint prevalence increased steeply across categories, up to rule-in levels for coronary artery disease. AUC were significantly higher with post-test-ESC than with post-test-AHA/ACC. AUC, area under the curve; CACS, coronary artery calcium score; post-test-AHA/ACC, post-test probability from American Heart Association/American College of Cardiology guidelines; post-test-ESC, pre-test probability from European Society of Cardiology guidelines

Figure 5

Patient reclassification by predictors. Patient flows through categories of pre-test probability, CACS, and post-test probability are shown by endpoint. More patients were classified as low probability of CAD with ESC vs. AHA/ACC probabilities, and with post-test vs. pre-test probabilities (all P < 0.001 using McNemar’s test). Very few participants with abnormal perfusion or severe ischemia were classified under pre-test or post-test probabilities 0-5%, or under CACS 0. AHA/ACC: American Heart Association/American College of Cardiology. CACS, coronary artery calcium score; ESC, European Society of Cardiology; post-test, post-test probability; pre-test, pre-test probability