Machine learning-driven echocardiographic assessment of filling pressures in patients with preserved ejection fraction

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a prevalent condition with a poor prognosis. Assessment of left ventricular end-diastolic pressure (LVEDP) is important for diagnosis and management, but current non-invasive methods lack accuracy. Purpose This study aimed to train and evaluate a deep learning (DL) model for estimating LVEDP using echocardiographic video input. Methods This multicenter, retrospective, cross-sectional study included patients from 2 large referral hospitals in Belgium. Patients with an ejection fraction ≥50% were included if they had an invasive measurement of LVEDP and echocardiography performed within 7 days. Data from one hospital was used for training and internal validation of a DL model (PyTorch 3D convolutional neural network with spatial and temporal attention mechanisms). Performance metrics and an area under the receiver operating characteristics curve (AUC) were calculated and compared to current European Association of Cardiovascular Imaging recommendations using DeLong’s test. Data from the second hospital was used as external validation. Results A total of 209 patients were included in the derivation set (54% female, mean age 72 ± 15 years), of these 106 (51%) had LVEDP ≥ 16 mmHg. Current recommendations had an accuracy of 49%, a sensitivity of 19%, a specificity of 77%, and an AUC of 0.48 in estimating LVEDP. The DL model had an accuracy of 80%, a sensitivity of 94%, a specificity of 67% and an AUC of 0.88. The DL model was superior in predicting LVEDP compared to current recommendations (p