Performance of the coronary calcium score in an outpatient chest pain clinic and strategies for risk stratification.

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.