Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Hypertension, Pulmonary
Conditions
Keywords
Artificial intelligence, electrocardiogram, deep learning, pulmonary hypertension
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTAI-ECG Guidance
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.
Sponsors
National Defense Medical Center, Taiwan
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
NONE
Intervention model description
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.
Eligibility
Sex/Gender
ALL
Age
50 Years to 85 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Pulmonary arterial pressure > 50 mmHg | 90 days | The composite endpoint is defined as detecting pulmonary hypertension \> 50mmHg by echocardiography, indicating high risk for pulmonary hypertension. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Left atrial enlargement on a parasternal long axis view | Within 90 days after randomization. | The endpoint measures the size of left atrium \> 40mm on a parasternal long axis view by echocardiography. |
| Left atrial enlargement by left atrium volume index | 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. |
| Right ventricular enlargement on a parasternal long axis view | Within 90 days after randomization. | The endpoint measures the size of right ventricular basal dimension \> 27mm by echocardiography. |
| New onset of left ventricular dysfunction | Within 90 days after randomization. | The endpoint measures the number and proportion of LVEF \< 50%. |
Countries
Taiwan
Contacts
CONTACTChin Lin, Associate Professor
STUDY_DIRECTORChin Lin, associate professor
National Defense Medical Center, Taiwan
Outcome results
None listed