ADOPT: Improving Diagnosis of Pulmonary Hypertension With AI and Echo

Pulmonary Hypertension Clinical Trial

— ADOPT  

Official title:

Artificial Intelligence: Improving Early Detection of Pulmonary Hypertension by Transthoracic Echocardiography: ADOPT

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06145880
• Other study ID #: RUH Bath - ADOPT
• Status: Recruiting
• Start date: December 1, 2023
• Est. completion date: December 1, 2025

Study information

• Verified date: August 2023
• Source: Royal United Hospitals Bath NHS Foundation Trust
• Contact: Daniel X Augustine, MBBS BSc MSc MD FHEA FRCP FBSE
• Phone: 01225 826133
• Email: daniel.augustine[@]nhs.net
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Pulmonary Hypertension (PH) is a condition caused by high blood pressure in the blood vessels that carry blood to the lungs. It can cause severe breathlessness and failure of the right side of the heart. Sadly it is often fatal, and life expectancy ranges from months to years. For some subtypes of PH, effective treatments exist which can improve life expectancy and quality-of-life. Accurate tools for the assessment of PH are therefore essential so that life-saving medications can be started earlier. In existing diagnostic pathways, evidence for the suspicion of PH is frequently overlooked, significantly delaying the time to diagnosis. Echocardiography (echo) is a quick, safe and well-tolerated test requested to investigate breathless patients, and which can provide useful information about the suspicion of PH. However, outside of specialist PH centres, doctors may not routinely look for and comment on the presence of clues to possible PH. The investigators think that using Artificial Intelligence (AI) techniques to read echo's could make their interpretation faster and more reliable. There may also be subtle clues to the presence or severity of PH on echo, less recognisable to the human eye, which AI can identify. In this study the investigators will gather echo images from 5 specialist PH hospitals across the UK which have all been anonymised (patient's name and personal details removed). These will all be historic scans (i.e. have already taken place) and will be grouped into those with PH present (including PH sub-type) or absent. These anonymised echo images will be used to develop and train an AI tool to identify scans where PH is present, including which specific type of PH may be present. The developed AI tool will then be tested on a separate group of scans (not used in the training stage) to validate its performance.

Description:

In this study the investigators will gather retrospective echo images from 5 specialist PH hospitals across the UK (Royal Free Hospital NHS FT; Sheffield Teaching Hospitals NHS FT; Royal Papworth Hospital NHS FT; NHS Golden Jubilee National Hospital Glasgow; Royal United Hospitals Bath NHS FT). These will all be historic scans (i.e. have already taken place) and will be grouped into those with PH present (including PH sub-type) or PH absent. Inclusion criteria involve patients aged ≥18 who have undergone both a transthoracic echo (TTE) and a right heart catheter (RHC) as part of their clinical care. Exclusion Criteria will involve patients aged <18, known or suspected congenital heart disease and patients who have opted out of allowing their information to be used for research and planning (via the national data opt-out choice). A clinical case report form (CRF) will be used to capture patient demographics, clinical data with regards to the PH assessment including previous TTE results. Where available, mortality data will be recorded within 5 years of the RHC. These anonymised echo images will be collated and labelled centrally in a core lab at the RUH Bath, who will work with Janssen to develop and train an AI tool to identify scans where PH is present, including which specific type of PH may be present. AI tool training will be based on 5 groups (each group anticipated to contain 415 echocardiograms): mild pre-capillary PH; moderate pre-capillary PH; severe pre-capillary PH; post capillary PH; no PH. The tool will then be validated in a separate pool made up of 425 echocardiograms (a combination of pre-capillary, post capillary PH and no PH). The validation cohort will not have been used in the training stage.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2500
• Est. completion date: December 1, 2025
• Est. primary completion date: December 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients aged =18
• Have undergone a transthoracic echo and right heart catheter as part of their routine clinical care.

Exclusion Criteria:

• Patients aged <18
• Known or suspected congenital heart disease
• Patient opted out of allowing their information to be used for research and planning (via the national data opt-out choice).

Related Conditions & MeSH terms

• Hypertension
• Hypertension, Pulmonary
• Pulmonary Hypertension

Intervention

Diagnostic Test:
• Artificial intelligence tool for transthoracic echocardiography
Pre-defined cohorts will be used to train the artificial intelligence tool for transthoracic echocardiography to improve the diagnosis of PH on TTE.

Locations

Country	Name	City	State
United Kingdom	Royal United Hospitals Bath NHS Foundation Trust	Bath

Sponsors (6)

Lead Sponsor	Collaborator
Royal United Hospitals Bath NHS Foundation Trust	Janssen Research & Development, LLC, NHS Golden Jubilee National Hospital Glasgow, Royal Free Hospital NHS Foundation Trust, Royal Papworth Hospital NHS Foundation Trust, Sheffield Teaching Hospitals NHS Foundation Trust

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Detect patients with pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT)	Measure the proportion of patients the developed AIT correctly identifies as having PH.	Month 24
Primary	Detect patients without pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT)	Measure the proportion of patients the developed AIT correctly identifies as not having PH.	Month 24
Primary	Detect patients with pre-capillary pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT)	Measure the proportion of patients the developed AIT correctly identifies as having pre-capillary PH.	Month 24
Primary	Detect patients with post-capillary pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT)	Measure the proportion of patients the developed AIT correctly identifies as having post-capillary PH.	Month 24
Primary	Compare the artificial intelligence tool (AIT) performance for detecting pulmonary hypertension (PH) with the current probability criteria	Compare the proportion of patients identified by the AI tool as having PH with the current guideline criteria for diagnosing PH from a TTE.	Month 24
Primary	Evaluate early detection capabilities of the artificial intelligence tool (AIT) compared to standard of care clinical diagnosis	Compare the proportion of patients identified by the AI tool as having PH with current standard clinical practice	Month 24
Secondary	The novel artificial intelligence tool (AIT) is able to assess the severity of pulmonary hypertension (PH)	Measure the proportion of patients tested where the AIT accurately diagnoses PH severity	Month 24
Secondary	The artificial intelligence tool (AIT) is able to predict mortality	Measure the proportion of patients where the AIT correctly predicted risk of PH-related mortality	Month 24