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Relation of Coronary Artery Disease and High-Sensitivity Cardiac Troponin: Evaluation with CCTA and AI-enabled Plaque Quantification
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DOI：10.1148/ryct.250002.png)
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Abstract
En 中文
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, chi 2 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
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