Pulmonary Hypertension (Diagnosis)NCT05942859
Applying Artificial Intelligence to the 12 Lead ECG for the Diagnosis of Pulmonary Hypertension: an Observational Study
An observational study of Pulmonary Hypertension (Diagnosis), run by Royal United Hospitals Bath NHS Foundation Trust at 1 site in United Kingdom.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Pulmonary Hypertension (Diagnosis)
- What is being tested?
- Artificial Intelligence and Machine Learning technology
- How many people
- 600 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- Oct 2023to Aug 2027
- How long will I take part?
- 3 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients, aged 18 or over, who have a clinical suspicion of Pulmonary Hypertension and undergo Right Heart Catheterisation within 12 months of an ECG.
Who may be able to join
- 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.
Who cannot join
- Patient's less than 18 years-old
- Patients who do not give valid consent (except deceased patients; REC approved)
- Patients who have not undergone RHC to assess for PH
- Patients who have not had an ECG within 12 months of their RHC
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this 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:
- 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.
Read the 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.
- Conditions
- Pulmonary Hypertension (Diagnosis)
- Being tested
- Artificial Intelligence and Machine Learning technology
- Keywords
- Artificial Intelligence, Machine Learning, Electrocardiogram, convolutional neural network
- Medical subject headings
- Hypertension, Pulmonary, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence and Machine Learning technologyDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Artificial Intelligence and Machine Learning technology | |
| 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. Receives: Diagnostic Test: Artificial Intelligence and Machine Learning technology |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pulmonary Hypertension diagnosis
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)
Measured: baseline
Secondary outcomes (3)
Pulmonary Hypertension sub-type
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
Measured: baseline
Mortality
The investigators will calculate the area under the receiver operating characteristics curve (AUROC) for mortality as predicted by Artificial Intelligence technology
Measured: 3 years
Morbidity
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)
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jul 2023
- Study startOct 2023 (estimated)
- Primary completionAug 2024 (estimated)
- Study completionAug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pulmonary Hypertension (Diagnosis)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 600 participants (estimated)
- Target duration
- 3 Years
Sponsor and investigators
- Lead sponsor
- Royal United Hospitals Bath NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Collaborators
- Liverpool John Moores University
- Responsible party
- Sponsor
- Investigators
- Dan Augustine, BSc, MBBS, MRCP, Principal investigator, Royal United Bath NHS Foundation Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05942859
- Sponsor's protocol ID
- RD2651
- Status last verified
- Jun 2023
- First submitted
- 4 Jul 2023
- Last updated
- 5 Oct 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Applying Artificial Intelligence to the 12 Lead ECG for the Diagnosis of Pulmonary Hypertension: an Observational Study
- ClinicalTrials.gov ID
- NCT05942859
- Acronym / sponsor ID
- RD2651
- Conditions
- Pulmonary Hypertension (Diagnosis)
- Keywords
- Artificial Intelligence, Machine Learning, Electrocardiogram, convolutional neural network
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Royal United Hospitals Bath NHS Foundation TrustUniversity, hospital or other
- Collaborators
- Liverpool John Moores University
- Study start
- Oct 2023 (estimated)
- Primary completion
- Aug 2024 (estimated)
- Study completion
- Aug 2027 (estimated)
- First posted
- 12 Jul 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 3 Years
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retrospective Cohort | – | Diagnostic Test: Artificial Intelligence and Machine Learning technologyPatients 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. |
| Prospective Cohort | – | Diagnostic Test: Artificial Intelligence and Machine Learning technologyPatients 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients, aged 18 or over, who have a clinical suspicion of Pulmonary Hypertension and undergo Right Heart Catheterisation within 12 months of an ECG.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (4 listed)
- Patient's less than 18 years-old
- Patients who do not give valid consent (except deceased patients; REC approved)
- Patients who have not undergone RHC to assess for PH
- Patients who have not had an ECG within 12 months of their RHC
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence and Machine Learning technologyArtificial 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. | Diagnostic test | Prospective Cohort, Retrospective Cohort |
Primary outcome
Pulmonary Hypertension diagnosis
Time frame: 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)
3 secondary outcomes
Pulmonary Hypertension sub-type
Time frame: baseline
Mortality
Time frame: 3 years
Morbidity
Time frame: baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Oct 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Dan Augustine, BSc, MBBS, MRCP principal investigator
Royal United Bath NHS Foundation Trust
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Hypertension (Diagnosis), run by Royal United Hospitals Bath NHS Foundation Trust at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 5 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pulmonary Hypertension diagnosis (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Oct 2023; AzTrials last refreshed its copy on 26 Oct 2026.