PT  - JOURNAL ARTICLE
AU  - DuBrock, Hilary M.
AU  - Wagner, Tyler E.
AU  - Carlson, Katherine
AU  - Carpenter, Corinne L.
AU  - Awasthi, Samir
AU  - Attia, Zachi I.
AU  - Frantz, Robert P.
AU  - Friedman, Paul A.
AU  - Kapa, Suraj
AU  - Annis, Jeffrey
AU  - Brittain, Evan L.
AU  - Hemnes, Anna R.
AU  - Asirvatham, Samuel J.
AU  - Babu, Melwin
AU  - Prasad, Ashim
AU  - Yoo, Unice
AU  - Barve, Rakesh
AU  - Selej, Mona
AU  - Agron, Peter
AU  - Kogan, Emily
AU  - Quinn, Deborah
AU  - Dunnmon, Preston
AU  - Khan, Najat
AU  - Soundararajan, Venky
TI  - An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension
AID  - 10.1183/13993003.00192-2024
DP  - 2024 Jul 01
TA  - European Respiratory Journal
PG  - 2400192
VI  - 64
IP  - 1
4099  - https://publications.ersnet.org//content/64/1/2400192.short
4100  - https://publications.ersnet.org//content/64/1/2400192.full
SO  - Eur Respir J2024 Jul 01; 64
AB  - Background Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG.Methods The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage–time data, with patients classified as “PH-likely” or “PH-unlikely” (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6–18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites.Results Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis.Conclusion The PH-EDA can detect PH at diagnosis and 6–18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. The PH-EDA is a noninvasive, ECG-based algorithm that has the potential to accelerate the diagnosis and management of patients with PH. https://bit.ly/3KdYF55Neither the data nor the algorithm can be shared due to legal reasons, as it is patent pending.