ABSTRACT
Background: Coronary pressure- and flow-derived parameters have prognostic value. Objectives: This study aims to investigate the individual and combined prognostic relevance of pressure and flow parameters reflecting resting and hyperemic conditions. Methods: A total of 1,971 vessels deferred from revascularization after invasive pressure and flow assessment were included from the international multicenter registry. Abnormal resting pressure and flow were defined as distal coronary pressure/aortic pressure ≤0.92 and high resting flow (1/resting mean transit time >2.4 or resting average peak flow >22.7 cm/s), and abnormal hyperemic pressure and flow as fractional flow reserve ≤0.80 and low hyperemic flow (1/hyperemic mean transit time <2.2 or hyperemic average peak flow <25.0 cm/s), respectively. The clinical endpoint was target vessel failure (TVF), myocardial infarction (MI), or cardiac death at 5 years. Results: The mean % diameter stenosis was 46.8% ± 16.5%. Abnormal pressure and flow were independent predictors of TVF and cardiac death/MI (all P < 0.05). The risk of 5-year TVF or MI/cardiac death increased proportionally with neither, either, and both abnormal resting pressure and flow, and abnormal hyperemic pressure and flow (all P for trend < 0.001). Abnormal resting pressure and flow were associated with a higher rate of TVF or MI/cardiac death in vessels with normal fractional flow reserve; this association was similar for abnormal hyperemic pressure and flow in vessels with normal resting distal coronary pressure/aortic pressure (all P < 0.05). Conclusions: Abnormal resting and hyperemic pressure and flow were independent prognostic predictors. The abnormal flow had an additive prognostic value for pressure in both resting and hyperemic conditions with complementary prognostic between resting and hyperemic parameters.
ABSTRACT
Aims: Coronary flow reserve (CFR) assessment has proven clinical utility, but Doppler-based methods are sensitive to noise and operator bias, limiting their clinical applicability. The objective of the study is to expand the adoption of invasive Doppler CFR, through the development of artificial intelligence (AI) algorithms to automatically quantify coronary Doppler quality and track flow velocity. Methods and results: A neural network was trained on images extracted from coronary Doppler flow recordings to score signal quality and derive values for coronary flow velocity and CFR. The outputs were independently validated against expert consensus. Artificial intelligence successfully quantified Doppler signal quality, with high agreement with expert consensus (Spearman's rho: 0.94), and within individual experts. Artificial intelligence automatically tracked flow velocity with superior numerical agreement against experts, when compared with the current console algorithm [AI flow vs. expert flow bias -1.68 cm/s, 95% confidence interval (CI) -2.13 to -1.23 cm/s, P < 0.001 with limits of agreement (LOA) -4.03 to 0.68 cm/s; console flow vs. expert flow bias -2.63 cm/s, 95% CI -3.74 to -1.52, P < 0.001, 95% LOA -8.45 to -3.19 cm/s]. Artificial intelligence yielded more precise CFR values [median absolute difference (MAD) against expert CFR: 4.0% for AI and 7.4% for console]. Artificial intelligence tracked lower-quality Doppler signals with lower variability (MAD against expert CFR 8.3% for AI and 16.7% for console). Conclusion: An AI-based system, trained by experts and independently validated, could assign a quality score to Doppler traces and derive coronary flow velocity and CFR. By making Doppler CFR more automated, precise, and operator-independent, AI could expand the clinical applicability of coronary microvascular assessment.
