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.
Lim et al. (Tue,) studied this question.
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