ArticleInternational journal of general medicine2021
Validation of Coronary Artery Disease Reporting and Data System (CAD-RADS) and Application of Coronary Artery Calcium Data and Reporting System (CAC-DRS) as New Standardized Tools in the Management of Coronary Artery Disease Patients.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Genetic Determinants of Coronary Artery Disease in Type 2 Diabetes Mellitus Among Asian Populations: A Meta-Analysis.Medical sciences (Basel, Switzerland) · 2026Pooled it
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesThe coronary artery disease reporting and data system (CAD-RADS) is intended to standardize the reporting of CCTA and the subsequent management guidelines of CAD. The present study was conducted to investigate the validation of CAD-RADS and the application of coronary calcium grading in CAD management. PATIENTS AND
methodsThe current study is a single-center prospective study that involved 177 participants with chest pain who were submitted to coronary CT angiography (CCTA). Two reviewers independently assessed CCTA results and gave each patient a CAD-RADS category. The reference standard for determining the clinical utility of CAD-RADS was invasive coronary angiography (ICA). The inter-reviewer agreement (IRA) was tested using the intra-class correlation (ICC).
resultsThe study enrolled 111 cases with non-significant CAD and 66 cases with significant CAD based on ICA findings. According to the reviewer, the CAD-RADS had a sensitivity, specificity, and accuracy of 90.9 to 100%, 89.2 to 94.6%, and 93.16 to 93.2%, respectively, for predicting severe CAD. The IRA for CAD-RADS categories was excellent (ICC = 0.960). The best cut-off value for predicting severe CAD was CAD-RADS > 3. Significant relation between Ca and severe CAD (p<0.001) was detected.
conclusionThe current study provides a good understanding of CAD-RADS as a standard tool with high diagnostic accuracy.
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