Artificial Intelligence-Enabled Electrocardiogram for Elevated Left Ventricular Filling Pressure
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Background
Left ventricular filling pressure (LVFP) is associated with heart failure symptoms, a key prognostic marker, and a therapeutic target, but is difficult to measure non-invasively. We aimed to develop and validate a deep learning-based artificial intelligence (AI) model using a standard 12-lead electrocardiogram (ECG) to detect elevated LVFP and assess its prognostic value.
Methods
We trained an AI model to detect increased LVFP. Septal E/e’ >15 on Doppler echocardiography was used to define increased LVFP and guide AI-ECG model training. The model was built upon a foundation model trained with >1 million multi-ethnic ECGs and fine-tuned through a development cohort of 225737 ECGs and 115982 echocardiogram data from 92775 unique patients from two tertiary hospitals. The model performance was assessed in a separate internal population from the development cohort (n=9278) and an independent external cohort from another tertiary hospital (n=17926). The prognostic significance of the AI-ECG output value was evaluated via survival analyses using the internal and external hospital cohorts, as well as the UK Biobank (n=43347).
Results
The AI-ECG model detected increased LVFP with an area under the curve of 0·868 (95% confidence interval [CI] 0·859–0·877) and 0·850 (95% CI 0·841–0·858) in the internal and external test cohorts, respectively. The model output was an independent predictor of mortality in all three cohorts (adjusted hazard ratio per 10-point increment: internal 1.31 [95% CI 1·23–1·38]; external 1·32 [95% CI 1·28–1·35]; UK Biobank 1.16 [95% CI 1·07–1·26]; all p<0·001). Its prognostic capability was comparable or superior to traditional echocardiographic parameters, particularly in patients with comorbidities.
Conclusions
The AI-ECG may enable identification of patients with increased LVFP and provide powerful prognostic information. Further prospective studies are warranted to evaluate its clinical utility.
CLINICAL PERSPECTIVE
What Is New?
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By using a specific, broadly applicable echocardiographic marker, E/e’ > 15 as the training target, our model circumvents the well-documented problems of indeterminate classifications and the exclusion of patients with atrial fibrillation, that have constrained previous models.
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The most significant added value is the extensive external validation. We built our model upon a state-of-the-art, multi-ethnic foundation model pre-trained on >1 million ECGs, and demonstrated the model’s consistent high performance not only in an internal cohort but also in two independent, racially and geographically distinct external cohorts. This robust external validation directly confronts the critical challenge of generalizability.
What Are the Clinical Implications?
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The AI-ECG output value provides independent and meaningful prognostic information, with performance comparable or numerically superior to established traditional echocardiographic parameters. This was particularly evident in patients with comorbidities, where the role of traditional echocardiographic markers is often limited.
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The AI-ECG may enable both population-level screening and enhance longitudinal management, offering an opportunity to identify at-risk individuals earlier and implement preventive strategies.
