Performance of the coronary calcium score in an outpatient chest pain clinic and strategies for risk stratification. Issue 2 (12th January 2021) - Huang, Weiting; Lim, Leon Ming Hsien; Aurangzeb, Amirzeb S/O.; Wong, Cheney Jianlin; Koh, Natalie Si Ya; Huang, Zijuan; Teo, Hooi Khee; Chua, Terrance Siang Jin; Tan, Swee Yaw
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.