Atrial Fibrillation (AF)FAST-AFNCT07486739
Functional And STructural Assesment of the Heart by Artificial Intelligence-enabled Electrocardiogram for the Management of Atrial Fibrillation
A study testing Artificial intelligence-enabled electrocardiography and Transthoracic Echocardiography-Guided Assessment in people with Atrial Fibrillation (AF), run by Seoul National University Hospital at 28 sites in South Korea.
Trial summary
The objective of this study is to evaluate whether an AI-ECG based screening strategy for detecting cardiac functional and structural abnormalities preserves clinical effectiveness and safety, compared with a conventional strategy of routine echocardiography in patients with AF, thereby demonstrating the… Read more about this study
- Who is it for?
- Ages 19 years and older
- Conditions the study is for
- Atrial Fibrillation (AF)
- What is being tested?
- Artificial intelligence-enabled electrocardiography and Transthoracic Echocardiography-Guided Assessment
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,724 participants
- Where
- 28 sitesSouth Korea
- Study dates
- 29 Jul 2026to 31 Dec 2030
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- AF documented by electrocardiography within the past 12 months
- AF documented on a 12-lead electrocardiogram or recorded for ≥30 seconds on a single-lead or multi-lead electrocardiogram.
- Patients for whom an initial or repeat transthoracic echocardiographic evaluation is clinically indicated.
- A CHA₂DS₂-VA score of ≥2.
- Aged ≥19 years at the time of enrollment and able to provide written informed consent voluntarily.
Who cannot join
- Transthoracic echocardiography performed within the past 6 months.
- Ventricular rate ≥110 beats per minute during atrial fibrillation.
- Atrial fibrillation due to a reversible cause.
- New York Heart Association (NYHA) functional class IV or European Heart Rhythm Association (EHRA) class IV symptoms.
- Known history of structural heart disease or clinical findings suggestive of structural heart disease based on medical history and physical examination. (e.g., presence of a cardiac murmur of Levine scale grade 3 or higher on auscultation, or murmurs suggestive of moderate to severe mitral stenosis, such as an opening snap or diastolic rumbling murmur).
- Baseline electrocardiographic conduction abnormalities or significant electrocardiographic findings suggestive of clinically meaningful structural heart disease (e.g., Mobitz type II second-degree atrioventricular block, third-degree atrioventricular block, or QTc ≥480 ms).
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- History of prior cardiac surgery.
- History of acute coronary syndrome or coronary revascularization within the past 90 days.
- History of intracardiac thrombosis or systemic thromboembolism within the past 90 days.
- History of transient ischemic attack, ischemic stroke, or intracranial hemorrhage within the past 90 days.
- History of ventricular tachycardia or ventricular fibrillation.
- Severe liver disease associated with coagulopathy (e.g., AST or ALT >3× the upper limit of normal, or total bilirubin >2× the upper limit of normal).
- Severe chronic kidney disease (stage V), requiring or imminently requiring dialysis.
- Contraindication to anticoagulation therapy.
- Pregnancy, breastfeeding, or planning pregnancy during the study period.
- Life expectancy of less than 1 year.
- Current participation in another randomized clinical trial.
Trial locations and contacts
28 sites in 1 country. Contact the study team or a site near you to ask about taking part.
28 sites in 1 country
South Korea28 sites
Korea University Ansan Hospital
Ansan
Seung-Yong Shin, theshin04@korea.ac.kr
Hallym University Sacred Heart Hospital
Anyang
Sang-Jin Han, medhan69@hallym.or.kr
Dong-A University Hospital
Busan
Jong-Sung Park, thinkmed@naver.com
Samsung Changwon Hospital
Changwon
Kwak Hye-Bin, tgfbk@naver.com
Dankook University Hospital
Cheonan
Dong-Min Kim, kdongmin@yahoo.com
Daegu Catholic University Medical Center
Daegu
Young-Soo Lee, mdleeys@cu.ac.kr
Keimyung University Dongsan Hospital
Daegu
Hyung-Sup Park, drparkgyver@gmail.com
Kyungpook National University Chilgok Hospital
Daegu
Park Yoon-Jung, blue8685@gmail.com
Chonnam National University Hospital
Gwangju
Yoon Nam-Sik, nsaids77@daum.net
Gachon University Gil Medical Center
Incheon
Ye-Min Park, ypruimin@gilhospital.com
Jeonbuk National University Hospital
Jeonju
Jung Rae-Young, youngjoin@naver.com
Presbyterian Medical Center (Jesus Hospital)
Jeonju
Sun-Hwa Kim, epcardiokim@gmail.com
CHA Bundang Medical Center
Seongnam
Jung-Hoon Sung, atropin5@cha.ac.kr
Asan Medical Center
Seoul
Cho Min-Soo, d070294@gmail.com
Boramae Medical Center
Seoul
Woo-Hyun Lim, woosion@gmail.com
Eunpyeong St. Mary's Hospital
Seoul
Sung-Jin Park, ferryst@naver.com
Ewha Womans University Seoul Hospital
Seoul
Ye-Ji Kim, lexie6169@gmail.com
Gangnam Severance Hospital
Seoul
Kim In-Soo, insooya1203@yuhs.ac
Hanyang University Hospital
Seoul
Jin-Kyu Park, cardiohy@hanyang.ac.kr
Kangbuk Samsung Hospital
Seoul
Min Kim, minpupets01.kim@samsung.com
Korea University Guro Hospital
Seoul
Kyung-Yeon Lee, managingheart@gmail.com
Kyung Hee University Hospital at Gangdong
Seoul
Eun-Sun Jin, esjin@khu.ac.kr
Kyung Hee University Medical Center
Seoul
Jin-Bae Kim, jinbbai@khu.ac.kr
Samsung Medical Center
Seoul
Young-Keun Ohn, yk.on@samsung.com
Seoul National University Hospital
Seoul
Eue-Keun Choi, MD, PhD, choiek417@gmail.com
Severance Hospital
Seoul
Dae-Hoon Kim, kimdhoon@yuhs.ac
Ajou University Hospital
Suwon
Do-Young Kim, sneeze_kr@naver.com
Pusan National University Yangsan Hospital
Yangsan
Hwang Ki-Won, korea.hwang@gmail.com
About this study
The objective of this study is to evaluate whether an AI-ECG based screening strategy for detecting cardiac functional and structural abnormalities preserves clinical effectiveness and safety, compared with a conventional strategy of routine echocardiography in patients with AF, thereby demonstrating the non-inferiority of AI-ECG guided care.
Read the detailed description
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, with its prevalence having more than doubled over the past decade. AF is associated with an increased risk of stroke, heart failure, and mortality, thereby imposing a substantial burden on both patients and healthcare systems. Accordingly, contemporary clinical guidelines emphasize accurate diagnosis and early, integrated management of AF. In this context, transthoracic echocardiography has become a standard diagnostic tool for the assessment of structural heart disease and cardiac function.
Despite being non-invasive and relatively low-cost, echocardiography is subject to several system-level limitations in routine clinical practice, including dependence on specialized equipment and trained personnel, scheduling delays, and inefficiencies related to repeated examinations. These constraints may create bottlenecks in the timely initiation and optimization of AF management.
In real-world practice, a considerable proportion of patients with AF undergo echocardiography primarily to confirm the absence of significant structural heart disease or impaired function. A uniform strategy of performing echocardiography in all patients with AF may not be optimal from the perspectives of patient convenience and healthcare resource utilization. Moreover, depending on healthcare system capacity, access to echocardiography may delay the timely selection of optimal AF management. Conversely, selectively performing echocardiography in patients with a higher likelihood of structural or functional cardiac abnormalities may allow for a more efficient, timely, and targeted diagnostic approach.
Artificial intelligence-enabled electrocardiography (AI-ECG) offers several practical advantages, including very short acquisition time, patients' convenience, substantially lower cost, and feasibility for repeated assessments during follow-up. AI-ECG may enable sensitive detection of changes in a patient's cardiac status over time. Positioning AI-ECG as an initial screening tool to identify patients with suspected structural or functional heart disease could facilitate a "screening-confirmation" diagnostic pathway, in which echocardiography is reserved for patients with abnormal or suspicious findings on AI-ECG. Such an approach has the potential to streamline initial and follow-up evaluations while maintaining patient safety.
- Conditions
- Atrial Fibrillation (AF)
- Being tested
- Artificial intelligence-enabled electrocardiography, Transthoracic Echocardiography-Guided Assessment
- Keywords
- atrial fibrillation, artificial intelligence, structural heart disease, electrocardiogram
- Medical subject headings
- Atrial Fibrillation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence-enabled electrocardiographyDiagnostic test
An artificial intelligence algorithm applied to standard 12-lead electrocardiography designed to predict cardiac structural or functional abnormalities. This tool guides the decision to perform or withhold downstream echocardiography.
Transthoracic Echocardiography-Guided AssessmentDiagnostic test
Standard Transthoracic Echocardiography used to assess cardiac structure and function, serving as the reference standard for guiding clinical management in this study arm.
| Study group | Role | What they receive |
|---|---|---|
| Transthoracic Echocardiography-Guided Assessment Group (TTE group) | Active comparator | Participants in this group will receive a standard-of-care evaluation. Cardiac function and structure will be evaluated using Transthoracic Echocardiography (TTE) regardless of ECG findings. Management (anticoagulation, rate/rhythm control) is initiated or adjusted based on TTE parameters. TTE is performed at least once annually during follow-up. Receives: Diagnostic Test: Transthoracic Echocardiography-Guided Assessment |
| AI-ECG-Guided Assessment Group (AI-ECG group) | Experimental | Participants in this group will undergo a conditional diagnostic strategy. Cardiac function and structure are initially screened using an AI-enabled ECG.
1. Predicted Normal: TTE is withheld. Management is based on clinical evaluation and AI-ECG results.
2. Predicted Abnormal: Verification TTE is performed. Management is guided by TTE findings.
Safety Note: Protocol-defined rescue TTE is permitted at the investigator's discretion for worsening symptoms or prior to procedures (cardioversion, ablation), regardless of AI-ECG results. Receives: Diagnostic Test: Artificial intelligence-enabled electrocardiography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Composite of all-cause Mortality, Stroke, CV Hospitalization, and AAD-Related SAEs
Evaluation of the effectiveness of the strategy based on a composite endpoint comprising the following clinical events:
- All-cause mortality;
- Stroke or systemic thromboembolism;
- Hospitalization due to worsening heart failure or acute coronary syndrome;
- Serious adverse events related to antiarrhythmic drug therapy. The endpoint is defined as the time to the first occurrence of any of these components.
Measured: up to 10 years
Secondary outcomes (15)
All-cause mortality
Measured: up to 10 years
Stroke or systemic thromboembolism
Measured: up to 10 years
Heart failure worsening
Heart failure worsening:
An outpatient heart failure episode requiring intravenous diuretic therapy or initiation or escalation of oral diuretics, or hospitalization for heart failure (defined as heart failure being the primary reason for admission or requiring treatment in a healthcare facility for ≥12 hours with intravenous diuretics).
Measured: up to 10 years
Hospitalization due to acute coronary syndrome
Measured: up to 10 years
Serious adverse events related to antiarrhythmic drug therapy
Serious adverse events related to antiarrhythmic drug therapy:
Hypotension, symptomatic drug-induced bradycardia, atrioventricular block, drug-induced atrial flutter or atrial tachycardia, torsade de pointes, ventricular tachycardia, ventricular fibrillation, or syncope.
Measured: up to 10 years
Proportion of patients receiving rhythm control therapyc after the initial diagnosis of AF
Rhythm control therapy:
Use of antiarrhythmic drugs, electrical cardioversion, or catheter ablation for AF.
Measured: up to 10 years
Time from initial diagnosis of AF to first rhythm control therapy
Rhythm control therapy:
Use of antiarrhythmic drugs, electrical cardioversion, or catheter ablation for AF.
Measured: up to 10 years
Changes in oral anticoagulation from warfarin to a DOAC or vice versa, based on the reassessment of cardiac function and structure
Measured: up to 10 years
Changes in the class of antiarrhythmic drugs (AADs) prescribed, based on the reassessment of cardiac function and structure
i.e., modification of Class Ic AAD to Class III AAD. Changes in antiarrhythmic drug therapy due solely to inadequate AF rate or rhythm control are not included.
Measured: up to 10 years
Changes in heart failure medications resulting from reassessment of cardiac function and structure
Initiation, dose escalation, or dose reduction of heart failure medication classes including beta-blockers, mineralocorticoid receptor antagonists (MRAs), renin-angiotensin-aldosterone system (RAAS) inhibitors or angiotensin receptor-neprilysin inhibitors (ARNIs), sodium-glucose cotransporter 2 inhibitors (SGLT2i), or other agents (e.g., ivabradine, vericiguat, hydralazine/nitrate
Measured: up to 10 years
The proportion of patients maintaining sinus rhythm
Measured: up to 10 years
Quality of life assessed by European Quality of Life-5 Dimensions (EQ-5D) at baseline, 12 months, and 24 months
EQ-5D scores range from 0 to 100, with higher scores indicating better health status.
Measured: up to 10 years
NT-proBNP levels at baseline, 12 months, and 24 months
Measured: up to 10 years
Diagnostic performance of the AI-ECG algorithm for detecting cardiac functional and structural abnormalities
Assessment of the AI-ECG algorithm's ability to detect functional and structural cardiac abnormalities. Performance metrics will include Accuracy, Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value.
Measured: up to 10 years
Investigator satisfaction with the use of AI-ECG
Measured: up to 10 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Mar 2026
- Study start29 Jul 2026 (estimated)
- Primary completion31 Dec 2030 (estimated)
- Study completion31 Dec 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Atrial Fibrillation (AF)Studies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 1,724 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Medical AI
- Responsible party
- Principal investigator, Eue-Keun Choi, Seoul National University Hospital
- Investigators
- Eue-Keun Choi, M.D. Ph.D., Principal investigator, Seoul National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07486739
- Sponsor's protocol ID
- FAST-AF
- Acronym
- FAST-AF
- Status last verified
- Jul 2026
- First submitted
- 10 Feb 2026
- Last updated
- 23 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 23 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Functional And STructural Assesment of the Heart by Artificial Intelligence-enabled Electrocardiogram for the Management of Atrial Fibrillation
- ClinicalTrials.gov ID
- NCT07486739
- Acronym / sponsor ID
- FAST-AF / FAST-AF
- Conditions
- Atrial Fibrillation (AF)
- Keywords
- atrial fibrillation, artificial intelligence, structural heart disease, electrocardiogram
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,724 participants (estimated)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Collaborators
- Medical AI
- Study start
- 29 Jul 2026 (estimated)
- Primary completion
- 31 Dec 2030 (estimated)
- Study completion
- 31 Dec 2030 (estimated)
- First posted
- 20 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The objective of this study is to evaluate whether an AI-ECG based screening strategy for detecting cardiac functional and structural abnormalities preserves clinical effectiveness and safety, compared with a conventional strategy of routine echocardiography in patients with AF, thereby demonstrating the non-inferiority of AI-ECG guided care.
Detailed description
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, with its prevalence having more than doubled over the past decade. AF is associated with an increased risk of stroke, heart failure, and mortality, thereby imposing a substantial burden on both patients and healthcare systems. Accordingly, contemporary clinical guidelines emphasize accurate diagnosis and early, integrated management of AF. In this context, transthoracic echocardiography has become a standard diagnostic tool for the assessment of structural heart disease and cardiac function.
Despite being non-invasive and relatively low-cost, echocardiography is subject to several system-level limitations in routine clinical practice, including dependence on specialized equipment and trained personnel, scheduling delays, and inefficiencies related to repeated examinations. These constraints may create bottlenecks in the timely initiation and optimization of AF management.
In real-world practice, a considerable proportion of patients with AF undergo echocardiography primarily to confirm the absence of significant structural heart disease or impaired function. A uniform strategy of performing echocardiography in all patients with AF may not be optimal from the perspectives of patient convenience and healthcare resource utilization. Moreover, depending on healthcare system capacity, access to echocardiography may delay the timely selection of optimal AF management. Conversely, selectively performing echocardiography in patients with a higher likelihood of structural or functional cardiac abnormalities may allow for a more efficient, timely, and targeted diagnostic approach.
Artificial intelligence-enabled electrocardiography (AI-ECG) offers several practical advantages, including very short acquisition time, patients' convenience, substantially lower cost, and feasibility for repeated assessments during follow-up. AI-ECG may enable sensitive detection of changes in a patient's cardiac status over time. Positioning AI-ECG as an initial screening tool to identify patients with suspected structural or functional heart disease could facilitate a "screening-confirmation" diagnostic pathway, in which echocardiography is reserved for patients with abnormal or suspicious findings on AI-ECG. Such an approach has the potential to streamline initial and follow-up evaluations while maintaining patient safety.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Transthoracic Echocardiography-Guided Assessment Group (TTE group) | Active comparator | Diagnostic Test: Transthoracic Echocardiography-Guided AssessmentParticipants in this group will receive a standard-of-care evaluation. Cardiac function and structure will be evaluated using Transthoracic Echocardiography (TTE) regardless of ECG findings. Management (anticoagulation, rate/rhythm control) is initiated or adjusted based on TTE parameters. TTE is performed at least once annually during follow-up. |
| AI-ECG-Guided Assessment Group (AI-ECG group) | Experimental | Diagnostic Test: Artificial intelligence-enabled electrocardiographyParticipants in this group will undergo a conditional diagnostic strategy. Cardiac function and structure are initially screened using an AI-enabled ECG. 1. Predicted Normal: TTE is withheld. Management is based on clinical evaluation and AI-ECG results. 2. Predicted Abnormal: Verification TTE is performed. Management is guided by TTE findings. Safety Note: Protocol-defined rescue TTE is permitted at the investigator's discretion for worsening symptoms or prior to procedures (cardioversion, ablation), regardless of AI-ECG results. |
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- AF documented by electrocardiography within the past 12 months
- Patients for whom an initial or repeat transthoracic echocardiographic evaluation is clinically indicated.
- A CHA₂DS₂-VA score of ≥2.
- Aged ≥19 years at the time of enrollment and able to provide written informed consent voluntarily.
Key exclusion criteria (17 listed)
- Transthoracic echocardiography performed within the past 6 months.
- Ventricular rate ≥110 beats per minute during atrial fibrillation.
- Atrial fibrillation due to a reversible cause.
- New York Heart Association (NYHA) functional class IV or European Heart Rhythm Association (EHRA) class IV symptoms.
- Known history of structural heart disease or clinical findings suggestive of structural heart disease based on medical history and physical examination. (e.g., presence of a cardiac murmur of Levine scale grade 3 or higher on auscultation, or murmurs suggestive of moderate to severe mitral stenosis, such as an opening snap or diastolic rumbling murmur).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence-enabled electrocardiographyAn artificial intelligence algorithm applied to standard 12-lead electrocardiography designed to predict cardiac structural or functional abnormalities. This tool guides the decision to perform or withhold downstream echocardiography. | Diagnostic test | AI-ECG-Guided Assessment Group (AI-ECG group) |
| Transthoracic Echocardiography-Guided AssessmentStandard Transthoracic Echocardiography used to assess cardiac structure and function, serving as the reference standard for guiding clinical management in this study arm. | Diagnostic test | Transthoracic Echocardiography-Guided Assessment Group (TTE group) |
Primary outcome
Composite of all-cause Mortality, Stroke, CV Hospitalization, and AAD-Related SAEs
Time frame: up to 10 years
Evaluation of the effectiveness of the strategy based on a composite endpoint comprising the following clinical events: 1. All-cause mortality; 2. Stroke or systemic thromboembolism; 3. Hospitalization due to worsening heart failure or acute coronary syndrome; 4. Serious adverse events related to antiarrhythmic drug therapy. The endpoint is defined as the time to the first occurrence of any of these components.
15 secondary outcomes
All-cause mortality
Time frame: up to 10 years
Stroke or systemic thromboembolism
Time frame: up to 10 years
Heart failure worsening
Time frame: up to 10 years
Hospitalization due to acute coronary syndrome
Time frame: up to 10 years
Serious adverse events related to antiarrhythmic drug therapy
Time frame: up to 10 years
Proportion of patients receiving rhythm control therapyc after the initial diagnosis of AF
Time frame: up to 10 years
Time from initial diagnosis of AF to first rhythm control therapy
Time frame: up to 10 years
Changes in oral anticoagulation from warfarin to a DOAC or vice versa, based on the reassessment of cardiac function and structure
Time frame: up to 10 years
Changes in the class of antiarrhythmic drugs (AADs) prescribed, based on the reassessment of cardiac function and structure
Time frame: up to 10 years
Changes in heart failure medications resulting from reassessment of cardiac function and structure
Time frame: up to 10 years
The proportion of patients maintaining sinus rhythm
Time frame: up to 10 years
Quality of life assessed by European Quality of Life-5 Dimensions (EQ-5D) at baseline, 12 months, and 24 months
Time frame: up to 10 years
NT-proBNP levels at baseline, 12 months, and 24 months
Time frame: up to 10 years
Diagnostic performance of the AI-ECG algorithm for detecting cardiac functional and structural abnormalities
Time frame: up to 10 years
Investigator satisfaction with the use of AI-ECG
Time frame: up to 10 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jul 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Eue-Keun Choi, M.D. Ph.D. principal investigator
Seoul National University Hospital
28 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence-enabled electrocardiography and Transthoracic Echocardiography-Guided Assessment in people with Atrial Fibrillation (AF), run by Seoul National University Hospital at 28 sites in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 17 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 23 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 28 sites in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Eue-Keun Choi, MD, PhD (82-2-2072-0688, choiek417@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Composite of all-cause Mortality, Stroke, CV Hospitalization, and AAD-Related SAEs (time frame: up to 10 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jul 2026.