Abstract

Coronary artery disease (CAD) is a leading cause of morbidity and mortality globally. The early and accurate diagnosis of CAD is essential for effective treatment. While Coronary Computed Tomography Angiography (CCTA) offers detailed visualization of coronary anatomy, its predictive power for hemodynamically significant coronary stenosis remains under investigation. This study aimed to develop and validate a CCTA-based morphologic index to enhance the diagnostic accuracy for significant coronary stenosis, potentially reducing the need for invasive diagnostic procedures. Materials and Methods: This prospective, observational study involved 250 patients referred for CCTA as part of their CAD diagnostic workup. Excluding 30 due to various criteria, 220 patients were analyzed. CCTA images were assessed for morphologic parameters, including plaque type, lesion length, degree of stenosis, and remodeling index. A multivariable logistic regression model was used to develop the morphologic index, whose predictive performance was validated against invasive fractional flow reserve (FFR) measurements. Results: The morphologic index demonstrated high sensitivity (85%) and specificity (90%), with an area under the ROC curve of 0.92. It accurately predicted 85% of lesions deemed hemodynamically significant by FFR. The application of the index potentially avoided 68.2% of unnecessary invasive procedures and led to correct management decisions in 95.5% of cases. Notably, 100 patients were advised to intensify medical therapy based on the index's recommendations. Conclusion: The CCTA-based morphologic index presents a reliable, non-invasive tool for predicting hemodynamically significant coronary stenosis, significantly influencing patient management decisions and potentially reducing unnecessary invasive procedures. Its integration into the diagnostic pathway for CAD may refine treatment strategies, aligning with precision medicine principles. Further validation in broader populations is warranted.

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