Artificial Intelligence‐Enabled ECG for Elevated E/e' on Echocardiography: Hemodynamic Relevance and Prognostic Value

BACKGROUND: Left ventricular filling pressure is associated with heart failure symptoms and a key prognostic marker and therapeutic target, but a scalable, accessible, and affordable tool for its noninvasive, serial estimation remains lacking. We developed an artificial intelligence (AI) model using a standard 12-lead ECG to detect increased E/e', a general surrogate of elevated left ventricular filling pressure on echocardiography. METHODS: The AI model was built upon a foundation model trained with >1 million multiethnic ECGs and fine-tuned through a development cohort of 225 737 ECGs and 115 982 echocardiogram records from 92 775 unique patients across 2 tertiary hospitals. The model performance was assessed in a separate internal test set (n=9278) and an independent external cohort from another tertiary hospital (n=17 926). Prognostic significance of the AI-ECG output was evaluated in these hospital cohorts, as well as the UK Biobank (n=43 347). Finally, we validated the model output against invasively measured left ventricular filling pressure through cardiac catheterization (n=60). RESULTS: The AI-ECG model detected increased E/e' with an area under the curve of 0.868 (95% CI, 0.859-0.877) and 0.850 (95% CI, 0.841-0.858) in the internal and external test cohorts, respectively. The AI-ECG output value demonstrated a strong correlation with invasively measured left ventricular end-diastolic pressure (Pearson's r=0.655) and was significantly associated with incident heart failure and mortality. CONCLUSIONS: The AI-ECG may enable identification of patients with increased left ventricular filling pressure and provide powerful prognostic information. Further prospective studies are warranted to evaluate its clinical utility.