Structural Heart AbnormalityCACTUSNCT07843004
Capitalizing on AI to CapTure Undiagnosed Structural Heart Disease
A study testing EHR Alert in people with Structural Heart Abnormality and Valve Heart Disease, run by Pierre Elias at 1 site in United States.
Trial summary
Doctors are testing a new tool called EchoNext to see if it can help find heart problems earlier. EchoNext looks at the heart's electrical test, called an ECG, and uses artificial intelligence (AI) to check for signs of structural heart disease (SHD). Read more about this study
- Who is it for?
- Ages 40 years and older
- Conditions the study is for
- Structural Heart Abnormality, Valve Heart Disease, Heart Diseases
- What is being tested?
- EHR Alert
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 4,000 participants
- Where
- 1 siteUnited States
- Study dates
- 16 Sep 2026to 1 Jun 2028
- 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 40 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 40 years of age or older
- Received an ECG upon presentation to the Emergency Department
Who cannot join
- Had an echocardiogram within the prior 2 years.
- Has a history of structural heart disease.
- Not able to receive follow-up based on Community Tele-Paramedicine care manager review.
- Quality of life will not improve over the next five years.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Columbia University Irving Medical Center / NewYork-Presbyterian
New York, New York, 10032
Michelle Castillo, MS, 732-277-7644, mc5067@cumc.columbia.edu
Pierre Elias, MD
About this study
Doctors are testing a new tool called EchoNext to see if it can help find heart problems earlier. EchoNext looks at the heart's electrical test, called an ECG, and uses artificial intelligence (AI) to check for signs of structural heart disease (SHD). SHD includes conditions like weak heart pumping, heart valve problems, or extra thickening of the heart muscle. These problems are common but often go undiagnosed until they cause serious issues like heart failure or stroke.
The main questions this study will answer are:
Read more
Can EchoNext alerts help emergency doctors find hidden heart problems sooner?
Does this lead to more follow-up heart tests, like an echocardiogram (heart ultrasound)?
What this means for patients: If a person has an ECG in the Emergency Department, the EchoNext tool may be used to check their heart. If the tool finds something unusual, their doctor may receive an alert and may then recommend further heart tests or follow-up care.
This study will help researchers learn if using EchoNext improves early diagnosis and treatment of heart disease.
Read the detailed description
Structural heart disease (SHD) is a major cause of illness and death, especially in older adults. SHD includes conditions such as valvular heart disease (e.g., aortic stenosis, mitral regurgitation), left or right ventricular dysfunction, left ventricular hypertrophy, pulmonary artery hypertension, and pericardial effusions. These conditions are often underdiagnosed, and delayed recognition can lead to complications such as heart failure, stroke, and cardiomyopathy. Early detection and treatment can improve outcomes, but current approaches often miss patients in the early stages of disease.
EchoNext is an artificial intelligence model trained on over 400,000 ECG-echocardiogram pairs. It analyzes the standard 12-lead ECG to predict the presence of structural heart disease, as defined by echocardiographic findings. The model has demonstrated strong diagnostic performance, with an area under the receiver operating characteristic (AUROC) curve of 0.86 across diverse populations.
The purpose of this trial is to evaluate whether deploying EchoNext into the Emergency Department (ED) electronic health record (EHR) as a clinical decision support alert can increase the detection of undiagnosed SHD. The ED setting is ideal because: (1) ECGs are widely obtained for many presenting complaints, (2) abnormal ECG findings related to SHD are often overlooked in the acute setting, and (3) EDs provide access to disadvantaged populations and a unique opportunity to deliver population-level interventions.
In this study, ECGs obtained in the ED will be analyzed in real time by the EchoNext model. If the model predicts moderate or severe SHD, an EHR alert will be displayed to the treating provider (attending physician, fellow, resident, physician assistant, or nurse practitioner). The alert will inform providers of the potential for underlying SHD and recommend consideration of follow-up echocardiography or specialty referral. Outcomes will include rates of new SHD diagnoses confirmed by echocardiography, follow-up testing and referrals, and downstream patient outcomes.
The Community Tele-Paramedicine (CTP) program, an established post-discharge care initiative at NewYork-Presbyterian, will also serve as a follow-up resource for patients identified in the ED. CTP combines home visits by community paramedics, telehealth visits by emergency physicians, and nurse care management to coordinate outpatient testing and follow-up care. Integration with CTP will facilitate timely echocardiography and referral for patients flagged by EchoNext, particularly for high-risk or underserved populations.
This trial will determine whether AI-driven ECG interpretation can improve early detection of SHD and lead to better patient outcomes compared to standard care.
- Conditions
- Structural Heart Abnormality, Valve Heart Disease, Heart Diseases, Cardiovascular Diseases
- Being tested
- EHR Alert
- Keywords
- Structural Heart Disease, Heart Disease, Machine Learning, Electrocardiogram, artificial intelligence, echocardiogram, electronic health record, emergency department
- Medical subject headings
- Heart Valve Diseases, Heart Diseases, Cardiovascular Diseases, Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
EHR AlertDevice
Also called EchoNext
EKG Algorithm to identify structural heart disease
| Study group | Role | What they receive |
|---|---|---|
| Standard Treatment - No EHR Alert | No intervention | Emergency department providers will not be alerted in the electronic health record (EHR) if a patient's electrocardiogram (ECG) ordered in the ED is identified by the EchoNext algorithm as suggesting structural heart disease (SHD) and potentially benefit from an echocardiogram. |
| Intervention - EHR Alert & Community Tele-paramedicine | Experimental | Emergency department providers will be alerted in the electronic health record (EHR) if a patient's electrocardiogram (ECG) ordered in the ED is identified by the EchoNext algorithm as suggesting structural heart disease (SHD) and potentially benefit from an echocardiogram. These patients will also be referred to Community Tele-paramedicine for follow-up for potential echocardiogram. Receives: Device: EHR Alert |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of new diagnoses of Structural Heart Disease (SHD)
Number of patients with a new diagnosis of structural heart disease (SHD), including moderate or greater valvular disease, left or right ventricular systolic dysfunction, left ventricular hypertrophy, pulmonary artery hypertension, or moderate/large pericardial effusions, confirmed by echocardiography or other clinical evaluation.
Measured: 180 days post index ED visit
Secondary outcomes (4)
Rate of echocardiography ordering
Proportion of patients who received an echocardiogram following the index ED visit.
Measured: 180 days post index ED visit
New Medical Treatments for SHD
Number of patients initiating new cardiovascular medications (e.g., antihypertensives, diuretics) specifically for treatment of structural heart disease.
Measured: 180 days post index ED visit
New Surgical or Procedural Interventions for SHD
Number of patients undergoing procedural interventions for SHD, including valve repair/replacement, cardiac surgery, drainage of pericardial effusion, or implanted defibrillator placement.
Measured: 180 days post-index ED visit
Long-Term Medical Treatments for SHD
Number of patients who initiated new cardiovascular medications (e.g., antihypertensives, diuretics) for treatment of structural heart disease within 1 year.
Measured: 1 year post-index ED visit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2026
- Study start16 Sep 2026
- Primary completion1 Jun 2027 (estimated)
- Study completion1 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Structural Heart AbnormalityStudies of the same condition, open and under way.
- 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
- Screening
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- This study uses a two-period, cluster-randomized crossover design. Eight emergency departments will be randomized to one of two treatment sequences: intervention followed by usual care, or usual care followed by intervention. Four sites will be assigned to each sequence. The emergency department is the unit of randomization, and each site will serve as both an intervention and control site during different study periods. Patients will receive the condition assigned to the emergency department at the time of their eligible encounter and will be analyzed according to site and study-period assignment.
- Masking
- None (open label)
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Pierre Elias
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Pierre Elias, Columbia University
- Investigators
- Pierre Elias, MD, Principal investigator, Columbia University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07843004
- Sponsor's protocol ID
- AAAU9699
- Other IDs
- 1R01HL177055-01
- Acronym
- CACTUS
- Status last verified
- Sep 2026
- First submitted
- 21 Sep 2026
- Last updated
- 25 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No: Individual participant data (IPD) from this study will not be shared. The data contain protected health information (PHI) from electronic health records (EHR) and are linked to specific patients and providers. Sharing this information could risk patient privacy and confidentiality, even with de-identification. Additionally, the data are collected under institutional policies and HIPAA regulations that restrict external access.
Study at a glance
From the registry record, last updated by the sponsor on 25 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Capitalizing on AI to CapTure Undiagnosed Structural Heart Disease
- ClinicalTrials.gov ID
- NCT07843004
- Acronym / sponsor ID
- CACTUS / AAAU9699
- Conditions
- Structural Heart Abnormality, Valve Heart Disease, Heart Diseases, Cardiovascular Diseases
- Keywords
- Structural Heart Disease, Heart Disease, Machine Learning, Electrocardiogram, artificial intelligence, echocardiogram, electronic health record, emergency department
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- Pierre EliasUniversity, hospital or other
- Collaborators
- Edwards Lifesciences, National Heart, Lung, and Blood Institute (NHLBI)
- Study start
- 16 Sep 2026 (actual)
- Primary completion
- 1 Jun 2027 (estimated)
- Study completion
- 1 Jun 2028 (estimated)
- First posted
- 25 Sep 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.
Doctors are testing a new tool called EchoNext to see if it can help find heart problems earlier. EchoNext looks at the heart's electrical test, called an ECG, and uses artificial intelligence (AI) to check for signs of structural heart disease (SHD). SHD includes conditions like weak heart pumping, heart valve problems, or extra thickening of the heart muscle. These problems are common but often go undiagnosed until they cause serious issues like heart failure or stroke.
The main questions this study will answer are:
Can EchoNext alerts help emergency doctors find hidden heart problems sooner?
Does this lead to more follow-up heart tests, like an echocardiogram (heart ultrasound)?
What this means for patients: If a person has an ECG in the Emergency Department, the EchoNext tool may be used to check their heart. If the tool finds something unusual, their doctor may receive an alert and may then recommend further heart tests or follow-up care.
This study will help researchers learn if using EchoNext improves early diagnosis and treatment of heart disease.
Detailed description
Structural heart disease (SHD) is a major cause of illness and death, especially in older adults. SHD includes conditions such as valvular heart disease (e.g., aortic stenosis, mitral regurgitation), left or right ventricular dysfunction, left ventricular hypertrophy, pulmonary artery hypertension, and pericardial effusions. These conditions are often underdiagnosed, and delayed recognition can lead to complications such as heart failure, stroke, and cardiomyopathy. Early detection and treatment can improve outcomes, but current approaches often miss patients in the early stages of disease.
EchoNext is an artificial intelligence model trained on over 400,000 ECG-echocardiogram pairs. It analyzes the standard 12-lead ECG to predict the presence of structural heart disease, as defined by echocardiographic findings. The model has demonstrated strong diagnostic performance, with an area under the receiver operating characteristic (AUROC) curve of 0.86 across diverse populations.
The purpose of this trial is to evaluate whether deploying EchoNext into the Emergency Department (ED) electronic health record (EHR) as a clinical decision support alert can increase the detection of undiagnosed SHD. The ED setting is ideal because: (1) ECGs are widely obtained for many presenting complaints, (2) abnormal ECG findings related to SHD are often overlooked in the acute setting, and (3) EDs provide access to disadvantaged populations and a unique opportunity to deliver population-level interventions.
In this study, ECGs obtained in the ED will be analyzed in real time by the EchoNext model. If the model predicts moderate or severe SHD, an EHR alert will be displayed to the treating provider (attending physician, fellow, resident, physician assistant, or nurse practitioner). The alert will inform providers of the potential for underlying SHD and recommend consideration of follow-up echocardiography or specialty referral. Outcomes will include rates of new SHD diagnoses confirmed by echocardiography, follow-up testing and referrals, and downstream patient outcomes.
The Community Tele-Paramedicine (CTP) program, an established post-discharge care initiative at NewYork-Presbyterian, will also serve as a follow-up resource for patients identified in the ED. CTP combines home visits by community paramedics, telehealth visits by emergency physicians, and nurse care management to coordinate outpatient testing and follow-up care. Integration with CTP will facilitate timely echocardiography and referral for patients flagged by EchoNext, particularly for high-risk or underserved populations.
This trial will determine whether AI-driven ECG interpretation can improve early detection of SHD and lead to better patient outcomes compared to standard care.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- This study uses a two-period, cluster-randomized crossover design. Eight emergency departments will be randomized to one of two treatment sequences: intervention followed by usual care, or usual care followed by intervention. Four sites will be assigned to each sequence. The emergency department is the unit of randomization, and each site will serve as both an intervention and control site during different study periods. Patients will receive the condition assigned to the emergency department at the time of their eligible encounter and will be analyzed according to site and study-period assignment.
| Arm | Type | Receives |
|---|---|---|
| Standard Treatment - No EHR Alert | No intervention | –Emergency department providers will not be alerted in the electronic health record (EHR) if a patient's electrocardiogram (ECG) ordered in the ED is identified by the EchoNext algorithm as suggesting structural heart disease (SHD) and potentially benefit from an echocardiogram. |
| Intervention - EHR Alert & Community Tele-paramedicine | Experimental | Device: EHR AlertEmergency department providers will be alerted in the electronic health record (EHR) if a patient's electrocardiogram (ECG) ordered in the ED is identified by the EchoNext algorithm as suggesting structural heart disease (SHD) and potentially benefit from an echocardiogram. These patients will also be referred to Community Tele-paramedicine for follow-up for potential echocardiogram. |
Who may qualify
- Ages
- 40 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 40 years of age or older
- Received an ECG upon presentation to the Emergency Department
Key exclusion criteria (4 listed)
- Had an echocardiogram within the prior 2 years.
- Has a history of structural heart disease.
- Not able to receive follow-up based on Community Tele-Paramedicine care manager review.
- Quality of life will not improve over the next five years.
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 |
|---|---|---|
| EHR AlertAlso: EchoNextEKG Algorithm to identify structural heart disease | Device | Intervention - EHR Alert & Community Tele-paramedicine |
Primary outcome
Number of new diagnoses of Structural Heart Disease (SHD)
Time frame: 180 days post index ED visit
Number of patients with a new diagnosis of structural heart disease (SHD), including moderate or greater valvular disease, left or right ventricular systolic dysfunction, left ventricular hypertrophy, pulmonary artery hypertension, or moderate/large pericardial effusions, confirmed by echocardiography or other clinical evaluation.
4 secondary outcomes
Rate of echocardiography ordering
Time frame: 180 days post index ED visit
New Medical Treatments for SHD
Time frame: 180 days post index ED visit
New Surgical or Procedural Interventions for SHD
Time frame: 180 days post-index ED visit
Long-Term Medical Treatments for SHD
Time frame: 1 year post-index ED visit
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 25 Sep 2026.
- Individual participant dataSharing planned: No. Individual participant data (IPD) from this study will not be shared. The data contain protected health information (PHI) from electronic health records (EHR) and are linked to specific patients and providers. Sharing this information could risk patient privacy and confidentiality, even with de-identification. Additionally, the data are collected under institutional policies and HIPAA regulations that restrict external access.
Related trials for Structural Heart Abnormality
Enrolling by invitation
Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation
Structural Heart Abnormality, Structural Heart Disease
RecruitingObservational study
Prince of Wales Hospital Structural Heart Registry
Structural Heart Abnormality
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.
Pierre Elias, MD principal investigator
Columbia University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing EHR Alert in people with Structural Heart Abnormality and Valve Heart Disease, run by Pierre Elias at 1 site in United States.
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 2 inclusion and 4 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: looking for participants now, as of the sponsor's last update on 25 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. 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 Michelle S Castillo, MS (7322777644, mc5067@cumc.columbia.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of new diagnoses of Structural Heart Disease (SHD) (time frame: 180 days post index ED visit). 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 25 Sep 2026.