Point of Care Clinical Risk Score to Improve the Negative Diagnostic Utility of an Agatston Score of Zero: Averting the Need for Coronary Computed Tomography Angiography

Background: Coronary artery calcification is a marker of underlying atherosclerotic vascular disease. The absence of coronary artery calcification is associated with a low prevalence of obstructive coronary artery disease (CAD), but it cannot be ruled out completely. We sought to develop a clinical tool that can be added to Agatston score of zero to rule out obstructive CAD with high accuracy. Methods: We developed a clinical score retrospectively from a cohort of 4903 consecutive patients with an Agatston score of zero. Patients with prior diagnosis of CAD, coronary percutaneous coronary intervention, or surgical revascularization were excluded. Obstructive CAD was defined as any epicardial vessel diameter narrowing of ≥50%. The score was validated using an external cohort of 4290 patients with an Agatston score of zero from a multinational registry. Results: The score consisted of 7 variables: Age, sex, typical chest pain, dyslipidemia, hypertension, family history, and diabetes mellitus. The model was robust with an area under the curve of 0.70 (95% CI, 0.65-0.76) in the derivation cohort and 0.69 (95% CI, 0.65-0.72) in the validation cohort. Patients were divided into 3 risk groups based on the score: low (≤6), intermediate (7-13), and high (≥14). Patients who score ≤6 have a negative likelihood ratio of 0.42 for obstructive CAD, whereas those who score ≥14 have a positive likelihood ratio of >5.5 for obstructive CAD. The outcome was ruled out in >98% of patients with a score ≤6 in the validation cohort. Conclusions: We developed a score that may be used to identify the likelihood of obstructive CAD in patients with an Agatston score of zero, which may be used to direct the need for additional testing. However, the results of this retrospective analysis are hypothesis generating and before clinical implementation should be validated in a trial with a prospectively collected data.