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Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning

      Highlights

      • This work describes the development and evaluation of a deep neural network “artificial intelligence” algorithm that shows high accuracy to detect pulmonary hypertension and several subtypes from 12-lead ECG alone.
      • It shows that pulmonary hypertension can even be detected with this approach up to 2 years before the clinical diagnosis was made in the standard clinical workflow.
      • This provides a new approach to detect pulmonary hypertension patients that can benefit from directed treatments in a widely-accessible non-invasive way in higher risk populations.

      Abstract

      Background

      Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes.

      Research Question

      Does an automated deep learning approach to ECG interpretation detect PH and its clinically important subtypes.

      Study Design and Methods

      Adults with right heart catheterization (RHC) or an echocardiogram within 90 days of an ECG at the University of California, San Francisco (2012–2019) were retrospectively identified as PH or non-PH. A deep convolutional neural network was trained on patients’ 12-lead ECG voltage data. Patients were divided into training, development, and test sets in a ratio of 7:1:2.

      Results

      Overall, 5016 PH and 19,454 non-PH patients were used in the study. Mean (SD) age at time of ECG was 62.29 (17.58) years and 49.88% were female. Mean interval between ECG and RHC or echocardiogram was 3.66 and 2.23 days for PH and non-PH patients, respectively. In the test dataset, the model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, respectively of 0.89, 0.79, and 0.84 to detect PH; 0.91, 0.83, and 0.84 to detect pre-capillary PH; 0.88, 0.81, and 0.81 to detect PAH, and 0.80, 0.73, and 0.76 to detect Group 3 PH. We additionally applied the trained model on ECGs from participants in the test dataset that were obtained from up to 2 years before diagnosis of PH: AUC was ≥0.79.

      Interpretation

      A deep learning ECG algorithm can detect PH and PH subtypes around the time of diagnosis and can detect PH using ECGs that were done up to 2 years before RHC/echocardiogram diagnosis. This approach has the potential to reduce diagnostic delay in PH.

      Keywords

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