Applying Artificial Intelligence to the 12 Lead ECG for the Diagnosis of Pulmonary Hypertension: an Observational Study

The goal of this observational study is to apply Artificial Intelligence (AI) and machine learning technology to the resting 12-lead electrocardiogram (ECG) and assess whether it can assist doctors in the early diagnosis of Pulmonary Hypertension (PH). Early and accurate diagnosis is an important step for patients with PH. It helps provide effective treatments early which improve prognosis and quality of life. The main questions our study aims to answer are: 1. Can AI technology in the 12-lead ECG accurately predict the presence of PH? 2. Can AI technology in the 12-lead ECG identify specific sub-types of PH? 3. Can AI technology in the 12-lead ECG predict mortality in patients with PH? In this study, the investigators will recruit 12-lead ECGs from consenting participants who have undergone Right heart Catheterisation (RHC) as part of their routine clinical care. AI technology will be applied to these ECGs to assess whether automated technology can predict the presence of PH and it's associated sub-types.

NCT Number	NCT05942859
Lead Sponsor	Royal United Hospitals Bath NHS Foundation Trust Collaborators: Liverpool John Moores University
Conditions	Pulmonary Hypertension (Diagnosis)
Enrollment	600 participants
Start Date	2023-10
Primary Completion	2024-08 (estimated)
Study Completion	2027-08 (estimated)
Updated on ClinicalTrials.gov	2023-10-05