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

Sponsor

Royal United Hospitals Bath NHS Foundation Trust (Other)

Overall Status

Enrolling by invitation

CT.gov ID

NCT05942859

Collaborator

Liverpool John Moores University (Other)

600

Enrollment

Location

Anticipated Duration (Months)

12.5

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

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:

Can AI technology in the 12-lead ECG accurately predict the presence of PH?
Can AI technology in the 12-lead ECG identify specific sub-types of PH?
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.

Condition or Disease	Intervention/Treatment	Phase
Pulmonary Hypertension (Diagnosis)	Diagnostic Test: Artificial Intelligence and Machine Learning technology

Detailed Description

This study will be led by Royal United Hospital Bath NHS Trust and Liverpool John Moore's University. The aim of this study is to utilise Artificial Intelligence (AI) and machine learning technology to assist clinicians in the early diagnosis of Pulmonary Hypertension (PH). We hypothesise that the AI technologies can improve the quantification and interpretation of the parameters involved in detecting PH. This is either through highlighting significant abnormalities in the 12-lead ECG, or by rapidly providing fully automated measures of the features on the 12-lead ECG which indicate PH. The combination of these electrocardiographic features with clinical data may provide highly accurate predictive tools.

This observational study will have a retrospective and prospective arm with a 3 year follow-up period. Participants will not require any additional tests or procedures at any point during the study. Any ECGs performed within the 12 months prior to a participant's right heart catheterisation (RHC) will undergo Artificial Intelligence analysis to establish if early indicators of PH are identifiable.

For all recruited participants, an anonymised clinician case report form will be used to capture details relating to their demographics and routine clinical care. Follow-up times and outcomes including mortality and morbidity will also be recorded.

Study Design

Study Type:

Observational [Patient Registry]

Anticipated Enrollment :

600 participants

Observational Model:

Cohort

Time Perspective:

Other

Official Title:

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

Anticipated Study Start Date :

Jul 1, 2023

Anticipated Primary Completion Date :

Jul 1, 2024

Anticipated Study Completion Date :

Jul 1, 2027

Arms and Interventions

Arm	Intervention/Treatment
Retrospective Cohort Patients who have previously been seen by the local Pulmonary Hypertension service, between 2007 and June 2023, for a suspected diagnosis of pulmonary hypertension, and undergone Right Heart Catheterisation (RHC) will be invited to participate in the study by a member of the direct clinical care team. Their ECG will be analysed using AI technology to develop an algorithm to aid the diagnosis of PH.	Diagnostic Test: Artificial Intelligence and Machine Learning technology Artificial Intelligence describes computer software designed to mimic human cognitive function. Machine learning is a type of artificial intelligence in which the model created is exposed to data, identifies patterns, and recognises relationships between features seen in the data and the 'ground truth'. This technology will be applied to participants ECGs.
Prospective Cohort Patients who are referred to the local PH service, from July 2023, with a suspected diagnosis of pulmonary hypertension, and undergo Right Heart Catheterisation will be invited to participate in the study by a member of the direct clinical care team. Their ECG will be analysed using AI technology to develop an algorithm to aid the diagnosis of PH.	Diagnostic Test: Artificial Intelligence and Machine Learning technology Artificial Intelligence describes computer software designed to mimic human cognitive function. Machine learning is a type of artificial intelligence in which the model created is exposed to data, identifies patterns, and recognises relationships between features seen in the data and the 'ground truth'. This technology will be applied to participants ECGs.

Arm

Intervention/Treatment

Retrospective Cohort

Patients who have previously been seen by the local Pulmonary Hypertension service, between 2007 and June 2023, for a suspected diagnosis of pulmonary hypertension, and undergone Right Heart Catheterisation (RHC) will be invited to participate in the study by a member of the direct clinical care team. Their ECG will be analysed using AI technology to develop an algorithm to aid the diagnosis of PH.

Diagnostic Test: Artificial Intelligence and Machine Learning technology

Artificial Intelligence describes computer software designed to mimic human cognitive function. Machine learning is a type of artificial intelligence in which the model created is exposed to data, identifies patterns, and recognises relationships between features seen in the data and the 'ground truth'. This technology will be applied to participants ECGs.

Prospective Cohort

Patients who are referred to the local PH service, from July 2023, with a suspected diagnosis of pulmonary hypertension, and undergo Right Heart Catheterisation will be invited to participate in the study by a member of the direct clinical care team. Their ECG will be analysed using AI technology to develop an algorithm to aid the diagnosis of PH.

Diagnostic Test: Artificial Intelligence and Machine Learning technology

Outcome Measures

Primary Outcome Measures

Pulmonary Hypertension diagnosis [baseline]
The investigators will calculate the area under the receiver operating characteristic curve (AUROC) for PH diagnosis by artificial intelligence technology and compare this to RHC (the gold standard)

Secondary Outcome Measures

Pulmonary Hypertension sub-type [baseline]
The investigators will assess the diagnostic test accuracy of Artificial Intelligence technology to categorise participant ECGs according to Pulmonary Hypertension sub-type and compare this to standard clinical assessment
Mortality [3 years]
The investigators will calculate the area under the receiver operating characteristics curve (AUROC) for mortality as predicted by Artificial Intelligence technology
Morbidity [baseline]
The investigators will calculate the area under the receiver operating characteristics curve for morbidity as predicted by Artificial Intelligence technology and compare this to current measures (NYHA functional class, 6MWT, Pulmonary function tests)

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

Inclusion Criteria:

prospective cohort: From July 2023, all patients aged 18 or over who are referred to the Bath Pulmonary Hypertension shared care service with clinical suspicion of PH and, who through their routine clinical care, undergo a RHC and 12-lead ECG.
Retrospective cohort: All patients aged 18 or over who were referred to the local Pulmonary Hypertension shared care service between 2007 and June 2023, and through their routine clinical care, have undergone RHC within a year of a 12-lead ECG. This cohort will also include patients who are deceased.