Article Impact Level: HIGH Data Quality: STRONG Summary of Journal of the American Heart Association, e034603. https://doi.org/10.1161/JAHA.124.034603 Dr. Göran Bergström et al.
Points
- The study aimed to evaluate if self-reported data could effectively identify individuals with moderate to severe coronary atherosclerosis, potentially reducing the need for imaging techniques.
- Researchers used Swedish CardioPulmonary BioImage Study (SCAPIS) data to analyze individuals aged 50-64 without prior ischemic heart disease. They developed a self-report tool with 14 variables and a clinical tool with 23.
- Both tools were tested for their ability to identify individuals with significant coronary atherosclerosis. The self-report tool achieved an area under the curve (AUC) of 0.79, and the clinical tool achieved an AUC of 0.80, outperforming the pooled cohort equation’s AUC of 0.76.
- The self-report tool identified 65% of individuals with a high segment involvement score (SIS ≥4) within the top 30% of the highest-risk group, indicating its potential for effective prescreening.
- The self-report tool shows promise as a cost-effective and efficient prescreening method for identifying individuals at risk for moderate to severe coronary atherosclerosis, optimizing resource use, and minimizing unnecessary radiation exposure.
Summary
The study aimed to determine if non-imaging data, particularly self-reported information, could identify individuals with moderate to severe coronary atherosclerosis, potentially reducing the need for resource-intensive and radiation-exposing imaging techniques. Utilizing data from the Swedish CardioPulmonary BioImage Study (SCAPIS), the researchers analyzed individuals aged 50 to 64 years without prior ischemic heart disease who underwent coronary computed tomography angiography (n=25,182) and coronary artery calcification scoring (n=28,701). They developed two predictive tools: a self-report tool with 14 variables and a clinical tool incorporating 23 variables from lab tests, physical exams, and self-reports.
Both tools were assessed for their ability to identify individuals with a segment involvement score (SIS) of ≥4 using receiver operating characteristic curve analysis, external validation, and comparison with the pooled cohort equation factors. The self-report tool achieved an area under the curve (AUC) of 0.79, while the clinical tool achieved an AUC of 0.80, both outperforming the pooled cohort equation’s AUC of 0.76 (P<0.001). Additionally, the tools demonstrated greater net benefits in clinical decision-making at relevant threshold probabilities. The self-report tool successfully identified 65% of individuals with an SIS ≥4 within the top 30% of the highest-risk group, with similar performance observed for coronary artery calcification scores of ≥100.
In conclusion, the self-report tool shows significant promise in identifying individuals at risk for moderate to severe coronary atherosclerosis. It could be an effective prescreening method for more cost-effective and targeted computed tomography-based screening programs. This tool could potentially streamline the identification of high-risk individuals, optimizing resource allocation and minimizing unnecessary radiation exposure.
Link to the article: https://www.ahajournals.org/doi/10.1161/JAHA.124.034603
References Bergström, G., Hagberg, E., Björnson, E., Adiels, M., Bonander, C., Strömberg, U., Andersson, J., Brunström, M., Carlhäll, C., Engström, G., Erlinge, D., Goncalves, I., Gummesson, A., Hagström, E., Hjelmgren, O., James, S., Janzon, M., Jonasson, L., Lind, L., … Jernberg, T. (2024). Self‐report tool for identification of individuals with coronary atherosclerosis: The swedish cardiopulmonary bioimage study. Journal of the American Heart Association, e034603. https://doi.org/10.1161/JAHA.124.034603
