Abstract
BACKGROUND: Coronary artery calcium score (CAC) is an objective marker of atherosclerosis. The primary aim is to assess CAC as a risk classifier in stable coronary artery disease (CAD). HYPOTHESIS: CAC improves CAD risk prediction, compared to conventional risk scoring, even in the absence of cardiovascular risk factor inputs. METHODS: Outpatients presenting to a cardiology clinic (n = 3518) were divided into two cohorts: derivation (n = 2344 patients) and validation (n = 1174 patients). Adding logarithmic transformation of CAC, we built two logistic regression models: Model 1 with chest pain history and risk factors and Model 2 including chest pain history only without risk factors simulating patients with undiagnosed comorbidities. The CAD I Consortium Score (CCS) was the conventional reference risk score used. The primary outcome was the presence of coronary artery disease defined as any epicardial artery stenosis≥50% on CT coronary angiogram. RESULTS: Area under curve (AUC) of CCS in our validation cohort was 0.80. The AUC of Models 1 and 2 were significantly improved at 0.88 (95%CI 0.86-0.91) and 0.87 (95%CI 0.84-0.90), respectively. Integrated discriminant improvement was >15% for both models. At a pre-specified cut-off of ≤10% for excluding coronary artery disease, the sensitivity and specificity were 89.3% and 74.7% for Model 1, and 88.1% and 71.8% for Model 2. CONCLUSION: CAC helps improve risk classification in patients with chest pain, even in the absence of prior risk factor screening.