TY - JOUR
T1 - Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis
T2 - The Swedish CardioPulmonary BioImage Study
AU - Bergström, Göran
AU - Hagberg, Eva
AU - Björnson, Elias
AU - Adiels, Martin
AU - Bonander, Carl
AU - Strömberg, Ulf
AU - Andersson, Jonas
AU - Brunström, Mattias
AU - Carlhäll, Carl-Johan
AU - Engström, Gunnar
AU - Erlinge, David
AU - Goncalves, Isabel
AU - Gummesson, Anders
AU - Hagström, Emil
AU - Hjelmgren, Ola
AU - James, Stefan
AU - Janzon, Magnus
AU - Jonasson, Lena
AU - Lind, Lars
AU - Magnusson, Martin
AU - Oskarsson, Viktor
AU - Sundström, Johan
AU - Svensson, Per
AU - Söderberg, Stefan
AU - Themudo, Raquel
AU - Östgren, Carl Johan
AU - Jernberg, Tomas
PY - 2024/7/3
Y1 - 2024/7/3
N2 - 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.
AB - 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.
U2 - 10.1161/JAHA.124.034603
DO - 10.1161/JAHA.124.034603
M3 - Article
C2 - 38958022
SN - 2047-9980
SP - 1
EP - 13
JO - Journal of the American Heart Association
JF - Journal of the American Heart Association
M1 - e034603
ER -