ESC Heart Failure (Feb 2025)
Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model
Abstract
Abstract Aims Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs). Methods and results A cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network‐long short‐term memory (CNN‐LSTM) DLM was employed. HFpEF risk was determined by left ventricular end‐diastolic pressure (LVEDP) and clinical symptoms. The DLM was trained by ECGs. LVEDP for each patient was collected through invasive left ventricular catheterisation. Cohort A and B comprised data from individuals at high risk for HFpEF (LVEDP > 12 mmHg) and low risk for HFpEF (LVEDP ≤ 12 mmHg). The model was trained on Cohort A and prospectively validated on Cohort B. Results A total of 238 patients underwent ECG and left ventricular catheterisation for model training in Cohort A, and 117 patients for validation in Cohort B. The DLM achieved 78% accuracy in assessing HFpEF risk in Cohort A, while in Cohort B, it demonstrated 78% accuracy, 71.9% specificity, and 71.7% sensitivity. In the validation Cohort B, the DLM‐identified high‐risk HFpEF group exhibited significantly higher prevalence of diabetes (22.03%–11.86%, P < 0.01), higher BMI indices (25.92–24.22 kg/cm2, P < 0.01), and lower usage history of calcium channel blockers (CCB) (11.76%–28.81%, P < 0.01) compared with the DLM‐identified low‐risk HFpEF group. Traditional HFpEF indicators, including B‐type natriuretic peptide (BNP) (22–20 pg/mL, P = 0.71) and E/E′ (8.25–8.5, P = 0.66), did not exhibit significant differences between the two groups. Conclusions The DLM offers an accurate, cost‐effective tool for HFpEF risk assessment, potentially facilitating early detection and improved clinical management.
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