A self-report tool for identification of individuals with coronary atherosclerosis – The Swedish CArdioPulmonary BioImage Study

Abstract

Background: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether non-imaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis. Methods: We used data from the population based Swedish CArdioPulmonary BioImage Study (SCAPIS) in individuals with coronary computed tomography angiography (CCTA, n=25,182) and coronary artery calcification score (CACS, n=28,701), aged 50-64 years without previous ischemic heart disease. We developed a risk prediction tool utilizing variables that could be assessed from home (a so-called self-report tool). For comparison, we also developed a tool utilizing variables from laboratory tests, physical examinations and self-report (a so-called clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and bench-marked against factors in the Pooled Cohort Equation (PCE). Results: The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify SIS ?4 (AUC 0.79 and 0.80, respectively) and significantly better than PCE (AUC 0.76, p<0.001). The tools showed a larger net benefit in clinical decision making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with SIS ?4 in the top 30% of the highest-risk individuals. Tools developed for CACS ?100 performed similarly. Conclusions: We have developed a self-report tool which effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as pre-screening tool towards a cost-effective CT-based screening program for high-risk individuals.