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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-ECG guidance | Experimental | Participants in this arm 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. |
|
| Standard clinical care | No Intervention | Participants in this arm are screened using the AI-ECG system, but diagnosis and management follow the usual clinical practice without echocardiography. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Pulmonary arterial pressure > 50 mmHg | The composite endpoint is defined as detecting pulmonary hypertension > 50mmHg by echocardiography, indicating high risk for pulmonary hypertension. | 90 days |
| Measure | Description | Time Frame |
|---|---|---|
| Left atrial enlargement on a parasternal long axis view | The endpoint measures the size of left atrium > 40mm on a parasternal long axis view by echocardiography. | Within 90 days after randomization. |
| Left atrial enlargement by left atrium volume index |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chin Lin, Associate Professor | Contact | 886+2-87923311 | 16118 | up6fup0629@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Chin Lin, associate professor | National Defense Medical Center, Taiwan | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Defense Medical Center | Recruiting | Taipei | Taiwan |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39136826 | Background | Liu PY, Hsing SC, Tsai DJ, Lin C, Lin CS, Wang CH, Fang WH. A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension. J Imaging Inform Med. 2025 Apr;38(2):747-756. doi: 10.1007/s10278-024-01225-4. Epub 2024 Aug 13. |
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| ID | Term |
|---|---|
| D006976 | Hypertension, Pulmonary |
| ID | Term |
|---|---|
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D006973 | Hypertension |
| D014652 | Vascular Diseases |
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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.
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The endpoint measures the size of left atrium volume index > 29 mL/m2 in sinus rhythm or > 40 mL/m2 in AF by echocardiography. |
| Within 90 days after randomization. |
| Right ventricular enlargement on a parasternal long axis view | The endpoint measures the size of right ventricular basal dimension > 27mm by echocardiography. | Within 90 days after randomization. |
| New onset of left ventricular dysfunction | The endpoint measures the number and proportion of LVEF < 50%. | Within 90 days after randomization. |
| D002318 |
| Cardiovascular Diseases |