The AI HFpEF model (EchoGo Heart Failure v2) showed higher classification performance than H2FPEF and HFA-PEFF scores; a positive AI result indicated a 2-fold increased risk of adverse outcomes.
Case-Control (n=496)
Does an updated AI HFpEF model improve diagnostic performance and prognostic associations compared to existing clinical scores in patients with HFpEF and matched controls?
Integrating an AI HFpEF model into clinical pathways improves the identification of HFpEF and patients at risk of adverse outcomes compared to existing clinical scores.
Effect estimate: 2-fold increased risk
Artificial intelligence (AI) models to identify heart failure (HF) with preserved ejection fraction (HFpEF) based on deep-learning of echocardiograms could help address under-recognition in clinical practice, but they require extensive validation, particularly in representative and complex clinical cohorts for which they could provide most value. In this study enrolling patients with HFpEF (cases; n = 240), and age, sex, and year of echocardiogram matched controls (n = 256), we compare the diagnostic performance (discrimination, calibration, classification, and clinical utility) and prognostic associations (mortality and HF hospitalization) between an updated AI HFpEF model (EchoGo Heart Failure v2) and existing clinical scores (H2FPEF and HFA-PEFF). The AI HFpEF model and H2FPEF score demonstrate similar discrimination and calibration, but classification is higher with AI than H2FPEF and HFA-PEFF, attributable to fewer intermediate scores, due to discordant multivariable inputs. The continuous AI HFpEF model output adds information beyond the H2FPEF, and integration with existing scores increases correct management decisions. Those with a diagnostic positive result from AI have a two-fold increased risk of the composite outcome. We conclude that integrating an AI HFpEF model into the existing clinical diagnostic pathway would improve identification of HFpEF in complex clinical cohorts, and patients at risk of adverse outcomes.
Akerman et al. (2025) conducted a case-control in Heart failure with preserved ejection fraction (HFpEF) (n=496). AI HFpEF model (EchoGo Heart Failure v2) vs. H2FPEF and HFA-PEFF scores was evaluated on Diagnostic performance and prognostic associations (mortality and HF hospitalization) (2-fold increased risk). The AI HFpEF model (EchoGo Heart Failure v2) showed higher classification performance than H2FPEF and HFA-PEFF scores; a positive AI result indicated a 2-fold increased risk of adverse outcomes.