Relation of Coronary Artery Disease and High-Sensitivity Cardiac Troponin: Evaluation with CCTA and AI-enabled Plaque Quantification.

Purpose To evaluate the relationship between artificial intelligence (AI)-quantified coronary plaque characteristics derived from coronary CT angiography (CCTA), stenosis severity, and high-sensitivity cardiac troponin T (hs-cTnT) levels in predicting adverse cardiovascular outcomes in emergency department patients. Materials and Methods This single-center retrospective cohort study included patients who presented acutely to the emergency department and underwent hs-cTnT testing (February 2016-March 2021). Based on peak hs-cTnT levels, patients were categorized into three groups: undetectable (<5 ng/L), intermediate (5-13 ng/L), and elevated (≥14 ng/L). All patients underwent CCTA, and those with Coronary Artery Disease Reporting and Data System score > 0 underwent plaque quantification using an AI-based plaque tool. Patients were followed up for major adverse cardiovascular events (MACE), including acute coronary syndrome, stroke, all-cause mortality, and late revascularization. Statistical analysis included nonparametric tests, χ² tests, and Cox hazards regression. Results Among 527 patients (291 [55%] male; mean age, 56 years ± 12 [SD]), 141 had undetectable, 275 had intermediate, and 111 had elevated hs-cTnT levels. Coronary artery disease prevalence at CCTA was 59% overall and 55% in patients with nonelevated hs-cTnT levels. Total, calcified, noncalcified, and low-density noncalcified plaque volumes increased significantly with higher troponin levels (P < .001). Over a median 29-month follow-up period, 22 MACE occurred. Elevated hs-cTnT level was not associated with increased MACE risk, whereas total plaque volume > 250 mm³ was a significant predictor of both MACE (hazard ratio [HR], 2.62 [95% CI: 1.13, 6.07]; P = .02) and all-cause mortality (HR, 3.62 [95% CI: 1.25, 10.50]; P = .02). Conclusion In this cohort, AI-quantified total plaque volume predicted MACE whereas troponin level did not. This study supports the use of CCTA with AI-based plaque quantification for risk stratification in a real-world population. Keywords: CT Angiography, Coronary Arteries, Arteriosclerosis, Coronary Artery Disease, Plaque Quantification, Troponin, Coronary Computed Tomography Angiography, Artificial Intelligence Supplemental material is available for this article. © RSNA, 2025.