{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T23:11:54Z","timestamp":1791414714432,"version":"4.3.3"},"reference-count":37,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"14","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2024,7,16]]},"abstract":"<jats:sec id=\"jah39820-sec-0001\" xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">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\u2010reported, could be used to identify individuals with moderate to severe coronary atherosclerosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"jah39820-sec-0002\" xml:lang=\"en\">\n                    <jats:title>Methods and Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      We used data from the population\u2010based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25\u2009182) and coronary artery calcification score (n=28\u2009701), aged 50 to 64\u2009years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self\u2010report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self\u2010report (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\u2010report tool (n=14 variables) and the clinical tool (n=23 variables) showed high\u2010to\u2010excellent discriminative ability to identify a segment involvement score \u22654 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76,\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.001). The tools showed a larger net benefit in clinical decision\u2010making at relevant threshold probabilities. The self\u2010report tool identified 65% of all individuals with a segment involvement score \u22654 in the top 30% of the highest\u2010risk individuals. Tools developed for coronary artery calcification score \u2265100 performed similarly.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"jah39820-sec-0003\" xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">We have developed a self\u2010report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self\u2010report tool may serve as prescreening tool toward a cost\u2010effective computed tomography\u2010based screening program for high\u2010risk individuals.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.124.034603","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T05:03:59Z","timestamp":1719983039000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":9,"title":["Self\u2010Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study"],"prefix":"10.1161","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4289-5722","authenticated-orcid":false,"given":"G\u00f6ran","family":"Bergstr\u00f6m","sequence":"first","affiliation":[{"name":"Institute of Medicine, Sahlgrenska Academy, University of Gothenburg","place":["Gothenburg, Sweden"]},{"name":"Region V\u00e4stra G\u00f6taland, Sahlgrenska University 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