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. We asked: does an automated
deep learning approach to ECG interpretation detect PH and its clinically important
subtypes?
Methods and Results
Adults with right heart catheterization 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. Overall, 5016 PH and 19,454 patients without PH were used
in the study. The mean age at the time of ECG was 62.29 ± 17.58 years and 49.88% were
female. The mean interval between ECG and right heart catheterization or echocardiogram
was 3.66 and 2.23 days for patients with PH and patients without PH, respectively.
In the test dataset, the model achieved an area under the receiver operating characteristic
curve, sensitivity, and specificity, respectively of 0.89, 0.79, and 0.84 to detect
PH; 0.91, 0.83, and 0.84 to detect precapillary PH; 0.88, 0.81, and 0.81 to detect
pulmonary arterial hypertension, 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; the area under the receiver
operating characteristic curve was 0.79 or greater.
Conclusions
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 right heart catheterization/echocardiogram
diagnosis. This approach has the potential to decrease diagnostic delays in PH.
Key Words
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Article info
Publication history
Published online: January 24, 2023
Accepted:
December 25,
2022
Received in revised form:
November 23,
2022
Received:
June 1,
2022
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2023 Elsevier Inc. All rights reserved.
