Coronary computed tomography angiography equals invasive angiography for the prediction of coronary revascularization

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Abstract

Introduction: Growing role of coronary computed tomography angiography (CTA) as a diagnostic tool in patients with suspected coronary artery disease (CAD) calls for better recognition of its value in clinical decision making as compared to the gold standard of invasive coronary angiography (ICA). Aim: To assess the diagnostic value of quantitative coronary computed tomography angiography (QCT) as compared to quantitative coronary angiography (QCA) for the prediction of coronary revascularization. Material and methods: In this prospective observational study we included 100 patients who underwent ICA following CTA. Quantitative diameter stenosis analysis (qCTA) was performed with Syngo.via (Siemens Medical Systems) software by an experienced investigator blinded to results of ICA. Quantitative Coronary Angiography (QCA) was chosen to define %DS in a repetitive manner. ICA images were submitted to Qangio XA (Medis, Leiden, The Netherlands) software for QCA analysis. Results: Eighty out of 400 analysed vessels were revascularized. Per-vessel diagnostic accuracy, sensitivity, specificity, PPV an NPV were 80%, 98%, 73%, 48% and 99% for QCT and 81%, 99%, 73%, 48% and 100% for QCA, respectively, for the prediction of revascularization. AUC was similar: 0.88 for QCT and 0.89 for QCA (p = NS). Conclusions: These real-world data support the concept that CTA is as precise in prediction of coronary revascularization as ICA. This may add to the discussion about CTA having the potential to replace ICA for diagnosing vessels qualified for intervention, reserving the invasive diagnostic approach for those with the highest probability of revascularization.

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Dȩbski, M., Kruk, M., Bujak, S., Dzielińska, Z., Demkow, M., & Kȩpka, C. (2019). Coronary computed tomography angiography equals invasive angiography for the prediction of coronary revascularization. Postepy w Kardiologii Interwencyjnej, 15(3), 308–313. https://doi.org/10.5114/aic.2019.84475

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