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NCT Number: NCT07079592

A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension

This study aims to validate the use of an artificial intelligence-enabled electrocardiogram (AI-ECG) to screen for elevated PAP. We hypothesize that the AI-ECG model can early identify patients with pulmonary hypertension in high-risk patients, prompting further evaluation through echocardiography, potentially resulting in improving cardiovascular outcomes.

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Key information

Conditions

Artificial Intelligence (AI) Artificial Intelligence (AI) in Diagnosis Cardiovascular Diseases Hypertension Hypertension, Pulmonary Lung Diseases Respiratory Tract Diseases Vascular Diseases

Age range

50 year–85 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

National Defense Medical Center

Taipei, Taiwan

Location status: Recruiting

Location contact

CONTACT

[email protected]

886+287923311 ext. 16118

About this study

Pulmonary hypertension is often underdiagnosed due to extensive category of etiology. The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Men or women, ≥ 50 to 85 years of age
  • At least one 12-lead ECG within 3 months

Exclusion criteria

  • A diagnosis of PH WHO Groups 1, 2, 3, 4, or 5
  • A diagnosis of hypertrophic cardiomyopathy, restrictive cardiomyopathy, constrictive pericarditis, cardiac amyloidosis, or infiltrative cardiomyopathy
  • Prior heart, lung, or heart-lung transplants
  • Any systolic pulmonary artery pressure >50 mmHg by echocardiography before
  • Echocardiography in 3 months before index ECG

Treatment and study plan

AI-ECG Guidance

Diagnostic Test

Participants undergo screening using the AI-ECG system. Those identified as high-risk for pulmonary hypertension receive echocardiography to confirm the diagnosis and guide subsequent management.

Primary outcomes

  1. Pulmonary arterial pressure > 50 mmHg

    Time frame: 90 days

    The composite endpoint is defined as detecting pulmonary hypertension > 50mmHg by echocardiography, indicating high risk for pulmonary hypertension.

Secondary outcomes

  1. Left atrial enlargement on a parasternal long axis view

    Time frame: Within 90 days after randomization.

    The endpoint measures the size of left atrium > 40mm on a parasternal long axis view by echocardiography.

  2. Left atrial enlargement by left atrium volume index

    Time frame: Within 90 days after randomization.

    The endpoint measures the size of left atrium volume index > 29 mL/m2 in sinus rhythm or > 40 mL/m2 in AF by echocardiography.

  3. Right ventricular enlargement on a parasternal long axis view

    Time frame: Within 90 days after randomization.

    The endpoint measures the size of right ventricular basal dimension > 27mm by echocardiography.

  4. New onset of left ventricular dysfunction

    Time frame: Within 90 days after randomization.

    The endpoint measures the number and proportion of LVEF < 50%.

Study contacts

Contact information is provided by the study sponsor or research team.

Chin Lin, Associate Professor

CONTACT

[email protected]

886+2-87923311 ext. 16118

Sponsors and collaborators

Lead sponsor

National Defense Medical Center, Taiwan

Other

Registry information

Official study title

A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension: A Randomized Controlled Trial

Acronym: ADDPH

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jul 23, 2025
Registry last updated
Feb 24, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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