Diagnostic Performance of Artificial Intelligence-Based Angiography-Derived Non-Hyperemic Pressure Ratio Using Pressure Wire as Reference

Abstract

Background: The angiography-derived non-hyperemic pressure ratio (angioNHPR) is a novel index of NHPR based on artificial intelligence (AI) that does not require pressure wires. We investigated the diagnostic accuracy of angioNHPR for detecting hemodynamically relevant coronary artery disease.

Methods and Results: In this retrospective single-center study, angioNHPR was assessed using the invasive NHPR as the reference standard. An angioNHPR ≤0.89 was defined as indicative of physiologically significant stenosis. Two angiographic projections ≥30° difference in angulation were selected. The lumen and centerline were automatically segmented by the prototype software, allowing for the calculation of the angioNHPR. We assessed 222 vessels from 178 patients. The accuracy of angioNHPR was 76.6% (95% confidence interval [CI] 70.4–82.0), with sensitivity 66.2% (95% CI 54.0–77.0), specificity 81.5% (95% CI 74.3–87.3), positive predictive value 62.7% (95% CI 53.6–70.9), and negative predictive value 83.7% (95% CI 78.6–87.7). The angioNHPR showed good correlation with invasive NHPR (r=0.72; 95% CI 0.64–0.77; P<0.001), and the agreement between angioNHPR and invasive NHPR was −0.01 (limits of agreement: −0.13, 0.11). The area under the curve (AUC) of angioNHPR was 0.81 (95% CI 0.75–0.86), which was significantly higher than that of 2-dimensional quantitative coronary angiography (AUC 0.69; 95% CI 0.62–0.75; P=0.007).

Conclusions: AI-based angioNHPR demonstrates good diagnostic performance using invasive NHPR as the reference standard.