Pulmonary Arterial Hypertension and Associated Cardiovascular Disease Detection Using Artificial Intelligence

NCT07147725 | Not Yet Recruiting

• 1 other identifier: 25NW0182
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

Cardiovascular disease (CVD) is a leading global cause of morbidity and mortality and excessive healthcare expenditures. Pulmonary hypertension (PH) represents an insidious and progressive subset of CVD affecting an estimated 1% of the general population, increasing to up to 10% in the population over the age of 65. Recent advancements in artificial intelligence (AI) have shown promise in transforming PH diagnosis by enabling the analysis of complex physiological data. Specifically, AI algorithms applied to electrocardiography (ECG) and phonocardiography (PCG) waveforms captured through novel medical devices, such as smart stethoscopes, have demonstrated potential in detecting PH and other cardiovascular conditions with high sensitivity and specificity. Despite the promising capabilities of AI algorithms, a significant barrier to their clinical implementation is the lack of high-quality, prospectively collected datasets for validation. Many existing AI algorithms have been trained on retrospective data, which may not capture the variability and complexity of real-world clinical scenarios. This limitation raises concerns about the generalisability and reliability of AI predictions across diverse patient populations. Therefore, there is a critical need for prospective validation studies to assess the performance of AI algorithms in realworld settings, ensuring their accuracy and applicability before widespread clinical deployment. Imperial College London's Health Impact Lab (Hi Lab) and collaborators continue to develop artificial intelligence (AI) algorithms that use cardiac waveforms to predict cardiovascular disease (CVD), including pulmonary hypertension (PH). The performance of these algorithms requires validation on prospectively collected patient data (waveforms) - where the ground truth for the algorithms under investigation is recorded during routine echocardiography as part of clinical care. This study aims to prospectively collect a large dataset of cardiovascular ECG and PCG data, along with corresponding gold-standard echocardiography findings. This dataset will be used to validate AI algorithms for important CVD, such as pulmonary hypertension enhancing their reliability and clinical applicability.

Conditions

Pulmonary Hypertension; Cardiovascular Diseases (CVD)

Keywords

Artificial Intelligence; Algorithms; Digital Health; Cardiology

Outcome Measures

Primary Outcomes (1)

• Performance characteristics for developed algorithms

  Performance characteristics for developed algorithms (external validation). Performance characteristics include area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio, negative likelihood ratio and and F1 Score of the AI algorithm in detecting the condition of interest e.g. PH, compared to echocardiography (ground truth).

  24 months

Interventions

AI Stethoscope DEVICE

Patients attending routine echocardiography who satisfy the inclusion and exclusion criteria will be approached before their echocardiography appointment to obtain informed consent to participate in the study. On providing informed consent, each patient will receive a non-invasive, external examination with a smart stethoscope that records a 3-lead electrocardiogram (ECG) and phonocardiogram (PCG) waveforms. This examination will require only one study visit (during routine echocardiography) and no additional visits. The stethoscope is a fully CE-marked device. In addition to echocardiography parameters and smart stethoscope waveforms, baseline demographics, clinical and medication history will be recorded. These data points will be re-examined at 24 months following enrolment (via chart review).

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

All-comers to echocardiography (n=1,000) will be approached for participation by a member of the research team i.e. identification by virtue of presence in the Imperial College NHS echocardiography department whilst attending routine care, gold-standard investigation.

You may qualify if:

• Age 18 years or above
• Able to give informed consent
• Attending for echocardiography at Imperial College NHS Trust

You may not qualify if:

• Any chest wound, skin pathology or other feature that would prohibit routine stethoscope examination

Sponsors & Collaborators

• Imperial College London: lead
• Imperial College Healthcare NHS Trust: collaborator

Study Sites (1)

Imperial College Healthcare NHS Trust

London, United Kingdom

Location

MeSH Terms

Conditions

Hypertension, Pulmonary; Cardiovascular Diseases

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Hypertension; Vascular Diseases

Study Officials

• Nicholas S Peters, MD

  Imperial College London

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Saloni Nakhare, MBChB, MSc

CONTACT

Email; s.nakhare@imperial.ac.uk

Patrik Bächtiger, MD

CONTACT

p.bachtiger@imperial.ac.uk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 22, 2025
• First Posted: August 29, 2025
• Study Start: October 1, 2025
• Primary Completion: December 1, 2025
• Study Completion (Estimated): August 1, 2027
• Last Updated: September 23, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)