Coronary artery disease (CAD) constitutes one of the primary drivers of cardiovascular mortality worldwide [1]. The functional assessment of coronary artery stenosis is typically performed in a catheterization laboratory, using invasive angiography to measure the Fractional Flow Reserve (FFR) [2], the gold standard for this task. Non-invasive FFR assessments often rely on fluid dynamics simulations of blood flow in patient arteries. From such simulations, practitioners can also extract relevant hemodynamic indices, such as the Time-Averaged Wall Shear Stress (TAWSS). However, 3D CFD simulations of coronary arteries are computationally expensive and time-consuming, limiting their utility for real-time clinical decision-making. To address this, 1D reduced-order models have been introduced. While previous works have investigated the predictive capabilities of these models [3], such comparisons have not yet been performed on a large database of patient-specific geometries. Moreover, the wall shear stress computable from the 1D simulation has rarely been systematically validated against 3D models. In this work, we perform a statistical comparison of 1D and 3D unsteady rigid-wall hemodynamic simulations across single coronary vessels from hundreds of patient-specific geometries. The geometries, reconstructed from the FAME 2 clinical dataset [4], feature varying degrees of stenosis. We apply a physiological aortic pressure wave at the inlets and a 0D lumped parameter model [5] at the outlets. We assess the agreement of FFR and TAWSS predictions between the 1D and 3D models. Results show that, within the attained Reynolds number range, 1D simulations are reliable predictors for both metrics across the entire dataset. These findings advocate for their adoption in clinical pipelines where computational speed is paramount. REFERENCES [1] K. Okrainec et al., Coronary artery disease in the developing world, Am. Heart J., 148(1):7–15, 2004. [2] N.H. Pijls et al., Experimental basis of determining maximum coronary..., Circulation, 87(4):1354–1367, 1993. [3] P.J. Blanco et al., Comparison of 1D and 3D Models for the Estimation of FFR, Sci. Rep., 8(1):17275, 2018. [4] B. De Bruyne et al., Fractional Flow Reserve–Guided PCI versus Medical Therapy..., NEJM, 367(11):991–1001, 2012. [5] H.J. Kim et al., Patient-Specific Modeling of Blood Flow..., Ann. Biomed. Eng., 38(10):3195–3209, 2010.