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

Author:

Bergström Göran12ORCID,Hagberg Eva12ORCID,Björnson Elias1ORCID,Adiels Martin3ORCID,Bonander Carl34ORCID,Strömberg Ulf35ORCID,Andersson Jonas6ORCID,Brunström Mattias6ORCID,Carlhäll Carl‐Johan78ORCID,Engström Gunnar9ORCID,Erlinge David10,Goncalves Isabel1112ORCID,Gummesson Anders113ORCID,Hagström Emil1415ORCID,Hjelmgren Ola116ORCID,James Stefan1415ORCID,Janzon Magnus17ORCID,Jonasson Lena17ORCID,Lind Lars18ORCID,Magnusson Martin9111920ORCID,Oskarsson Viktor621ORCID,Sundström Johan2223ORCID,Svensson Per2425ORCID,Söderberg Stefan6ORCID,Themudo Raquel2627ORCID,Östgren Carl Johan728ORCID,Jernberg Tomas29

Affiliation:

1. Department of Molecular and Clinical Medicine Institute of Medicine, Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden

2. Department of Clinical Physiology Region Västra Götaland, Sahlgrenska University Hospital Gothenburg Sweden

3. School of Public Health and Community Medicine Institute of Medicine, University of Gothenburg Gothenburg Sweden

4. Centre for Societal Risk Research Karlstad University Karlstad Sweden

5. Department of Research and Development Region Halland Halmstad Sweden

6. Department of Public Health and Clinical Medicine Umeå University Umeå Sweden

7. Center for Medical Image Science and Visualization (CMIV) Linköping University Linköping Sweden

8. Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring Sciences Linköping University Linköping Sweden

9. Department of Clinical Sciences in Malmö Lund University Malmö Sweden

10. Department of Clinical Sciences Lund, Cardiology Lund University, Skåne University Hospital Lund Sweden

11. Department of Cardiology Skåne University Hospital Malmö Sweden

12. Cardiovascular Research Translational Studies, Department of Clinical Sciences Malmö Lund University Malmö Sweden

13. Department of Clinical Genetics and Genomics Sahlgrenska University Hospital Gothenburg Sweden

14. Department of Medical Sciences Cardiology, Uppsala University Uppsala Sweden

15. Uppsala Clinical Research Center Uppsala University Uppsala Sweden

16. Pediatric Heart Centre, Queen Silvias Childrens hospital Sahlgrenska University Hospital Gothenburg Sweden

17. Department of Cardiology and Department of Health, Medicine and Caring Sciences, Unit of Cardiovascular Sciences Linköping University Linköping Sweden

18. Department of Medical Sciences, Clinical Epidemiology Uppsala University Uppsala Sweden

19. North‐West University Potchefstroom South Africa

20. Wallenberg Center for Molecular Medicine Lund University Lund Sweden

21. Piteå Research Unit Region Norrbotten Piteå Sweden

22. Department of Medical Sciences Uppsala University Uppsala Sweden

23. The George Institute for Global Health University of New South Wales Sydney New South Wales Australia

24. Department of Clinical Science and Education, Södersjukhuset Karolinska Institutet Stockholm Sweden

25. Department of Cardiology Södersjukhuset Stockholm Sweden

26. Department of Clinical Science, Intervention and Technology, Division of Medical Imaging and Technology Karolinska Institute Stockholm Sweden

27. Department of Radiology Karolinska University Hospital in Huddinge Stockholm Sweden

28. Department of Health, Medicine and Caring Sciences Linköping University Linköping Sweden

29. Department of Clinical Sciences Danderyd University Hospital, Karolinska Institutet Stockholm Sweden

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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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