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

NCT05942859 | Enrolling By Invitation DMC

• 1 other identifier: RD2651
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

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:

1. 1.Can AI technology in the 12-lead ECG accurately predict the presence of PH?
2. 2.Can AI technology in the 12-lead ECG identify specific sub-types of PH?
3. 3.Can AI technology in the 12-lead ECG predict mortality in patients with PH?

Conditions

Pulmonary Hypertension (Diagnosis)

Keywords

Artificial Intelligence; Machine Learning; Electrocardiogram; convolutional neural network

Outcome Measures

Primary Outcomes (1)

• 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)

  baseline

Secondary Outcomes (3)

• Pulmonary Hypertension sub-type

  baseline

• Mortality

  3 years

• Morbidity

  baseline

Study Arms (2)

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

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

Interventions

Artificial Intelligence and Machine Learning technology DIAGNOSTIC_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.

Prospective Cohort; Retrospective Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Royal United Hospitals Bath NHS Foundation Trust: lead
• Liverpool John Moores University: collaborator

Study Sites (1)

Royal United Hospital Bath NHS Trust

Bath, United Kingdom

Location

MeSH Terms

Conditions

Hypertension, Pulmonary; Disease

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Hypertension; Vascular Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Dan Augustine, BSc, MBBS, MRCP

  Royal United Bath NHS Foundation Trust

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 3 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 4, 2023
• First Posted: July 12, 2023
• Study Start: October 1, 2023
• Primary Completion: August 1, 2024
• Study Completion (Estimated): August 1, 2027
• Last Updated: October 5, 2023

Record last verified: 2023-06

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)