Optimal Approach to Implementing an Echocardiography-based Artificial Intelligence Algorithm for Diagnosing Hemodynamically Confirmed HFpEF in a Referral Population.

Authors

Muhammad Shariq Usman, Vinayak Subramanian, Matthew W Segar, Zain Khera, Clara Rodrigues Alves DE Oliveira, Katarina Yaros, Alvin Chandra, Ross Upton, Ashley Akerman, Javed Butler, Ambarish Pandey

Published in

Journal of cardiac failure. Volume 32. Issue 11. Pages 1863-1874.

Abstract

Diagnosing heart failure with preserved ejection fraction (HFpEF) in outpatients remains challenging. An AI model (EchoGo HF) that analyzes resting apical 4-chamber echocardiographic clips shows promise, but its diagnostic performance relative to gold-standard exercise hemodynamic testing is unknown. Additionally, the optimal approach to integrating the AI-HFpEF model with clinical risk scores has not been established.
This study included outpatients referred for suspected HFpEF, who underwent exercise hemodynamic testing. HFpEF was defined by elevated filling pressures on exercise right-heart catheterization, pulmonary capillary wedge pressure [PCWP] ≥15 mmHg at rest, ≥25 mmHg during exercise, or PCWP/CO ≥2 mmHg/L/min), or resting/exercise echocardiography (E/e' >14). AI-HFpEF was applied to resting echocardiograms, and diagnostic performance (area under the receiver operating characteristic [AUROC] and F1) was compared with the H₂FPEF score. Associations with disease severity (PCWP, peak exercise oxygen uptake [VO₂peak]) were evaluated using multivariable regression. Additionally, the performance of a combined model (H₂FPEF + AI-HFpEF) was assessed using AUROC. Among 280 patients, 222 (79%) had hemodynamically confirmed HFpEF. AI-HFpEF classified 57% (n = 159/280) as HFpEF, 38% (n = 106/280) as no HFpEF and 5% (15/280) as uncertain. AI-HFpEF-detected HFpEF independently predicted elevated PCWP (OR [95% CI] = 2.48[1.17, 5.22]; P = 0.017), and lower VO₂peak (β = -1.38 [-2.64, -0.13] mL/kg/min; P = 0.031). The AI-HFpEF model had good model discrimination (AUROC = 0.702) with an F1 score of 76.8%, a sensitivity of 67.3% (false-negative rate = 32.7%) and a specificity of 68.5% (false-positive rate = 31.5%). Combining H₂FPEF with AI-HFpEF significantly improved performance to detect hemodynamically confirmed HFpEF vs H₂FPEF score alone (0.724 vs 0.656; DeLong P = 0.021). Incorporating the AI-HFpEF model with H₂FPEF score led to greater improvement in detecting hemodynamically confirmed HFpEF among patients with low/intermediate than those with high H₂FPEF scores (AUROC = 0.746 vs 0.535). A 2-step strategy, combining upfront H₂FPEF score assessment with selective AI-HFpEF assessment in participants with low/intermediate H₂FPEF, identified a larger proportion of patients with hemodynamically confirmed HFpEF (78%) vs H₂FPEF score alone (48%). This strategy resulted in a net reclassification improvement of 15.5% and a relative integrated discrimination improvement of 94%.
AI-HFpEF is independently associated with markers of disease severity, accurately identifies hemodynamically confirmed HFpEF, and improves diagnostic performance when integrated with H₂FPEF, particularly at low/intermediate scores.

PMID:
43009239
Bibliographic data and abstract were imported from PubMed on 11 Nov 2026.

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