Incremental Prognostic Power of Coronary Flow Reserve and Artificial Intelligence-Enabled ECG-Derived Filling Pressure in Angina With Nonobstructive Coronary Arteries

Clinical Snapshot

Key Findings

• P Value: CMD: P=0.002; AIEFP: P<0.001

• Effect Size: CMD HR=1.42; AIEFP HR=2.28 in adjusted models

• Primary Outcome: Major adverse cardiovascular events over 8-year follow-up

• Nnt Or Sensitivity: Highest risk group (CMD+/AIEFP+): 12.7% 1-year risk, 51.8-57% 8-year cumulative risk

• Confidence Interval: CMD: 95% CI=1.13-1.78; AIEFP: 95% CI=1.67-3.11

Clinical Application

Requires invasive coronary reactivity testing and AI-enabled ECG analysis, limiting routine application Relevant to Australian cardiology practice, though invasive testing availability varies. AI-ECG technology would require TGA approval and PBS consideration for broader implementation Patients with angina and nonobstructive coronary arteries undergoing invasive coronary assessment

Abstract

BACKGROUND: Coronary flow reserve reflects microvascular function, whereas filling pressure indicates myocardial hemodynamic burden. In angina with nonobstructive coronary arteries, abnormalities in either could contribute to a supply-demand mismatch; however, their combined prognostic significance remains unclear. METHODS: Patients with angina with nonobstructive coronary arteries who underwent invasive coronary reactivity tests were studied retrospectively. Coronary microvascular dysfunction (CMD) was coronary flow reserve <2.5. An artificial intelligence-enabled ECG marker of elevated filling pressure (AIEFP) was applied as a scalable prognostic marker of myocardial hemodynamic stress. Associations with major adverse cardiovascular events were evaluated using Cox regression and propensity score overlap weighting. RESULTS: Among 1957 patients with angina with nonobstructive coronary arteries (median age, 51.8 years; 67.2% women), CMD+ was identified in 26.4% and AIEFP+ in 6.6%. Over 8 years, 204 patients (10.4%) experienced major adverse cardiovascular events. In nested models, both CMD (hazard ratio [HR]=1.42 [95% CI=1.13-1.78]; P=0.002) and AIEFP (HR=2.28 [95% CI=1.67-3.11]; P<0.001) remained associated with major adverse cardiovascular events after adjustment. Adding AIEFP to the base model improved model fitness (Δχ2=23.65, P<0.001; ΔAkaike Information Criterion=-21.7) and modestly improved discrimination (ΔHarrell concordance index=0.02, P=0.02). In a 4-group model, adjusted risks increased in a graded manner. Overlap-weighted analysis after covariate balance demonstrated distinct temporal risk trajectories: CMD+/AIEFP+ had the highest 1-year risk (12.7%), and AIEFP+ had the highest cumulative 8-year risk (57% in CMD- and 51.8% in CMD+). CONCLUSIONS: CMD and AIEFP provide complementary prognostic information, with AIEFP remaining significantly associated with outcomes after multivariable adjustment. A dual-axis framework integrating myocardial hemodynamic stress (AIEFP) and microvascular function (coronary flow reserve) may enhance risk stratification and enable identification of clinically distinct angina with nonobstructive coronary arteries phenotypes with divergent trajectories.

References

1. 1.Kalhor, P., Manzato, M., Hellou, E., Nogami, K., Nardi, V., Borlaug, B. A., Lerman, L. O., & Lerman, A. (2026). Incremental prognostic power of coronary flow reserve and artificial intelligence-enabled ECG-derived filling pressure in angina with nonobstructive coronary arteries. Journal of the American Heart Association, Article e047946. https://doi.org/10.1161/JAHA.125.047946