Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model

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.