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. 2024 Jul 16;13(14):e034603.
doi: 10.1161/JAHA.124.034603. Epub 2024 Jul 3.

Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study

Göran Bergström  1   2 , Eva Hagberg  1   2 , Elias Björnson  1 , Martin Adiels  3 , Carl Bonander  3   4 , Ulf Strömberg  3   5 , Jonas Andersson  6 , Mattias Brunström  6 , Carl-Johan Carlhäll  7   8 , Gunnar Engström  9 , David Erlinge  10 , Isabel Goncalves  11   12 , Anders Gummesson  1   13 , Emil Hagström  14   15 , Ola Hjelmgren  1   16 , Stefan James  14   15 , Magnus Janzon  17 , Lena Jonasson  17 , Lars Lind  18 , Martin Magnusson  9   11   19   20 , Viktor Oskarsson  6   21 , Johan Sundström  22   23 , Per Svensson  24   25 , Stefan Söderberg  6 , Raquel Themudo  26   27 , Carl Johan Östgren  7   28 , Tomas Jernberg  29
Affiliations

Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study

Göran Bergström et al. J Am Heart Assoc. .

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 nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis.

Methods and results: We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 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 a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly.

Conclusions: We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.

Keywords: coronary artery calcium score; coronary atherosclerosis; risk prediction tool; segment involvement score; self‐reported data.

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Figures

Figure 1
Figure 1. Flow diagram of study inclusion.
CACS indicates coronary artery calcification score; CCTA, coronary computed tomography angiography; CT, computed tomography; MI, myocardial infarction; PCI, percutaneous coronary intervention; SCAPIS, Swedish CardioPulmonary BioImage Study; and SIS, segment involvement score.
Figure 2
Figure 2. Receiver operating characteristic curve for the self‐report tool's assessment of SIS≥4 in the internal and external validation group compared with PCE.
A, Self‐report tool vs PCE, P<0.001, P<0.001 for internal and external validation respectively. Variable importance of the self‐ report tool (B). ROC curve for the clinical tool's assessment of SIS≥4 compared with PCE (C, clinical tool vs PCE, P<0.001, P<0.001 for internal and external validation respectively). Variable importance of the clinical tool (D). The DeLong test was used for statistical comparison. AUC indicates area under the curve; HbA1c, glycated hemoglobin; HDL, high‐density lipoprotein; MI, myocardial infarction; PCE, pooled cohort equation; and ROC, receiver operating characteristic.
Figure 3
Figure 3. Receiver operating characteristic curves for the self‐report tool and the clinical tool assessing SIS≥4 vs PCE, stratified by sex.
Female; self‐report tool vs PCE (P<0.001), clinical tool vs PCE (P<0.001). Male; self‐report tool vs PCE (P<0.001), clinical tool vs PCE (NS). The DeLong test was used for statistical comparison. AUC, indicates area under the curve; ns, nonsignificant; PCE, pooled cohort equation; and SIS, segment involvement score.
Figure 4
Figure 4. Receiver operating characteristic curves for the self‐report tool and the clinical tool assessing SIS≥4 vs PCE, stratified by age.
Age 50 to 54.9 (NS, NS for self‐report and clinical tool respectively), 55 to 59.9 (P<0.05, P<0.001 for self‐report and clinical tool respectively), 60–65.9 (P<0.001, P<0.001 for self‐report and clinical tool respectively). The DeLong test was used for statistical comparison. AUC, indicates area under the curve; NS, nonsignificant; PCE, pooled cohort equation; and SIS, segment involvement score.
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