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

NCT05942859 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 600

Last updated 2023-10-05

No results posted yet for this study

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. 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.

Conditions

  • Pulmonary Hypertension (Diagnosis)

Interventions

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.

Sponsors & Collaborators

  • Liverpool John Moores University

    collaborator OTHER

  • Royal United Hospitals Bath NHS Foundation Trust

    lead OTHER

Principal Investigators

  • Dan Augustine, BSc, MBBS, MRCP · Royal United Bath NHS Foundation Trust

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-10-31
Primary Completion
2024-08-31
Completion
2027-08-31

Countries

  • United Kingdom

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05942859 on ClinicalTrials.gov