Coronary Artery DiseaseSAFE-CTNCT06438393
Screening Coronary Artery Disease Using artiFicial intelligencE in Non-contrast Computed Tomography
An observational study of Coronary Artery Disease and Coronary Atheroscleroses, run by Universidade do Porto at 1 site in Portugal.
Trial summary
This project aims to improve direct patient care by reducing the risks of futile exposure to ionizing radiation and iodinated contrast in patients referred for coronary computed tomography angiography Read more about this study
- Who is it for?
- Ages 18 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Coronary Artery Disease, Coronary Atheroscleroses
- What is being tested?
- CT coronary angiography and non-contrast CT
- How many people
- 1,000 participants
- Where
- 1 sitePortugal
- Study dates
- Jun 2024to Dec 2027
- Phase
- Observational
- 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 18 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Stable chest pain patients with unknown CAD who underwent a CTCA with paired non-contrast CT
Who may be able to join
- Patient with stable chest pain who underwent a CTCA
Who cannot join
- Missing non-contrast CT image (coronary calcium score image)
- Known coronary artery disease
- Prior myocardial infarction
- Prior PCI or CABG
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
This project aims to improve direct patient care by reducing the risks of futile exposure to ionizing radiation and iodinated contrast in patients referred for coronary computed tomography angiography
Read the detailed description
Since the last NICE guidelines update recommending computed tomography coronary angiography (CTCA) as the first line of investigation for patients with suspected coronary artery disease (CAD), there has been a high burden in the healthcare system and unnecessary exposition to radiation and iodine-containing contrast medium, especially in the youngest. Around 35% of patients who currently undergo CTCA have normal coronaries which means those patients were unnecessary exposed to radiation and contrast. A CTCA screening strategy to rule out CAD is needed to comply with the ALARA ("As Low As Reasonable Achievable") principles preventing radiation risks, reducing unnecessary scans and directing healthcare resources to those who will benefit from a CTCA.
We designed the SAFE-CT (Screening coronary Artery disease using artiFicial intelligencE in noncontrast Computed Tomography) study to develop a state-of-art artificial intelligence method to detect CAD as defined on CTCA using high-dimensional data (radiomics) extracted from the non-contrast cardiac computed tomography (CT). The model will be trained in 15,000 subjects scanned with paired non-contrast CT and CTCA and externally validated in an independent cohort of 1,000 subjects. In a preliminary analysis, non-contrast CT radiomics improved calcium score performance and discriminated CAD with an AUC of 0.91 (95% CI: 0.83-1.00). The algorithm will be converted into a user-friendly plugin to automatically decide whether the patient needs contrast. A real-world multicentre cohort study will be planned for software prospective validation and the creation of a large-scale proteomic biobank to support the translation of imaging biomarkers worldwide.
SAFE-CT can change the current CT scanning workflow by creating software that accurately rules out any CAD in >1/3 of patients referred for CTCA with low radiation and no contrast. This accurate machine learning model will be optimized to reach >90% sensitivity and negative predictive value and will bring several advantages for patients and the healthcare system:
- Prevention of radiation and contrast exposition.
- Increased CTCA scanning capacity for complex cases.
- Widespread use of CT for CAD exclusion in the emergency department and in outpatient clinics of centres with no CTCA.
- Improved screening tool for CAD in asymptomatic subjects.
- Up- and downstream cost reduction.
The SAFE-CT project proposes a safer, low-cost, and personalized CTCA scanning strategy that fosters scientific and technological innovation with the potential to bring improvement to patient care and clinical practice, and, thereby, societal, and economic impact.
- Conditions
- Coronary Artery Disease, Coronary Atheroscleroses
- Being tested
- CT coronary angiography and non-contrast CT
- Keywords
- Artificial intelligence, Radiomics, Deep learning, Coronary artery disease, Non-contrast computed tomography
- Medical subject headings
- Coronary Artery Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CT coronary angiography and non-contrast CTDiagnostic test
Also called Non-contrast computed tomography
A CTCA is an X-ray computed tomography of the coronary arteries that allows visualization of coronary plaques with high temporal and spatial resolution, however, it implies the use of iodine contrast and exposition to clinically significant ionizing radiation.
Non-contrast ECG-gated CT ("calcium score" - CCS image). A non-contrast cardiac CT for CCS can be performed very quickly with significantly lower radiation (~6 times lower) than CTCA and without the need for contrast.
| Study group | Role | What they receive |
|---|---|---|
| Stable chest pain and unknown CAD who underwent CTCA and CCS in the same scanning session | CAD: Presence of minimal coronary artery disease (i.e., coronary stenosis 0-25%) Normal coronary arteries: No visible coronary atherosclerosis Receives: Diagnostic Test: CT coronary angiography and non-contrast CT |
What is being measured
The primary outcome is the main question the study is designed to answer.
Build a non-contrast CT radiomic signature of CAD
Measured: 3 years
Implement a machine learning model to discriminate patients with no CAD from patients with at least minimal disease (CAD-RADS=0 vs. CAD-RADS>0).
Measured: 3 years
Implement a machine learning model to detect coronary inflammation as defined using the Fat Attenuation Index (FAI ≥ -70.1 HU) in patients with no visible coronary plaque (CAD-RADS=0).
Measured: 3 years
Build a user-friendly plugin to facilitate users experience and distribution of our technology in clinical practice.
Measured: 3 years
Evaluate the real-world operationality and performance of the plugin in an international multicentre prospective cohort study.
Measured: 3 years
Create a national registry of cardiac CT
Measured: 3 years
Secondary outcomes (2)
Setup a human blood biobank to identify the peripheral blood mononuclear cells (PBMCs) and plasma proteomics associated with CT data and clinical outcomes.
Measured: 3 years
Setup a public CT imaging repository
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 May 2024
- Study startJun 2024 (estimated)
- Primary completionJun 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Coronary Artery DiseaseStudies 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
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Universidade do Porto
- Sponsor type
- University, hospital or other
- Collaborators
- University of Oxford, University of Edinburgh
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06438393
- Sponsor's protocol ID
- SAFE-CT
- Acronym
- SAFE-CT
- Status last verified
- Feb 2024
- First submitted
- 27 May 2024
- Last updated
- 31 May 2024
- Results posted
- No
- Data monitoring committee
- No
- 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 31 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Screening Coronary Artery Disease Using artiFicial intelligencE in Non-contrast Computed Tomography
- ClinicalTrials.gov ID
- NCT06438393
- Acronym / sponsor ID
- SAFE-CT / SAFE-CT
- Conditions
- Coronary Artery Disease, Coronary Atheroscleroses
- Keywords
- Artificial intelligence, Radiomics, Deep learning, Coronary artery disease, Non-contrast computed tomography
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Universidade do PortoUniversity, hospital or other
- Collaborators
- University of Oxford, University of Edinburgh
- Study start
- Jun 2024 (estimated)
- Primary completion
- Jun 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 31 May 2024 (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.
This project aims to improve direct patient care by reducing the risks of futile exposure to ionizing radiation and iodinated contrast in patients referred for coronary computed tomography angiography
Detailed description
Since the last NICE guidelines update recommending computed tomography coronary angiography (CTCA) as the first line of investigation for patients with suspected coronary artery disease (CAD), there has been a high burden in the healthcare system and unnecessary exposition to radiation and iodine-containing contrast medium, especially in the youngest. Around 35% of patients who currently undergo CTCA have normal coronaries which means those patients were unnecessary exposed to radiation and contrast. A CTCA screening strategy to rule out CAD is needed to comply with the ALARA ("As Low As Reasonable Achievable") principles preventing radiation risks, reducing unnecessary scans and directing healthcare resources to those who will benefit from a CTCA.
We designed the SAFE-CT (Screening coronary Artery disease using artiFicial intelligencE in noncontrast Computed Tomography) study to develop a state-of-art artificial intelligence method to detect CAD as defined on CTCA using high-dimensional data (radiomics) extracted from the non-contrast cardiac computed tomography (CT). The model will be trained in 15,000 subjects scanned with paired non-contrast CT and CTCA and externally validated in an independent cohort of 1,000 subjects. In a preliminary analysis, non-contrast CT radiomics improved calcium score performance and discriminated CAD with an AUC of 0.91 (95% CI: 0.83-1.00). The algorithm will be converted into a user-friendly plugin to automatically decide whether the patient needs contrast. A real-world multicentre cohort study will be planned for software prospective validation and the creation of a large-scale proteomic biobank to support the translation of imaging biomarkers worldwide.
SAFE-CT can change the current CT scanning workflow by creating software that accurately rules out any CAD in >1/3 of patients referred for CTCA with low radiation and no contrast. This accurate machine learning model will be optimized to reach >90% sensitivity and negative predictive value and will bring several advantages for patients and the healthcare system:
- Prevention of radiation and contrast exposition.
- Increased CTCA scanning capacity for complex cases.
- Widespread use of CT for CAD exclusion in the emergency department and in outpatient clinics of centres with no CTCA.
- Improved screening tool for CAD in asymptomatic subjects.
- Up- and downstream cost reduction.
The SAFE-CT project proposes a safer, low-cost, and personalized CTCA scanning strategy that fosters scientific and technological innovation with the potential to bring improvement to patient care and clinical practice, and, thereby, societal, and economic impact.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Stable chest pain and unknown CAD who underwent CTCA and CCS in the same scanning session | – | Diagnostic Test: CT coronary angiography and non-contrast CTCAD: Presence of minimal coronary artery disease (i.e., coronary stenosis 0-25%) Normal coronary arteries: No visible coronary atherosclerosis |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Stable chest pain patients with unknown CAD who underwent a CTCA with paired non-contrast CT
Key inclusion criteria (1 listed)
- Patient with stable chest pain who underwent a CTCA
Key exclusion criteria (4 listed)
- Missing non-contrast CT image (coronary calcium score image)
- Known coronary artery disease
- Prior myocardial infarction
- Prior PCI or CABG
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 |
|---|---|---|
| CT coronary angiography and non-contrast CTAlso: Non-contrast computed tomographyA CTCA is an X-ray computed tomography of the coronary arteries that allows visualization of coronary plaques with high temporal and spatial resolution, however, it implies the use of iodine contrast and exposition to clinically significant ionizing radiation. Non-contrast ECG-gated CT ("calcium score" - CCS image). A non-contrast cardiac CT for CCS can be performed very quickly with significantly lower radiation (~6 times lower) than CTCA and without the need for contrast. | Diagnostic test | Stable chest pain and unknown CAD who underwent CTCA and CCS in the same scanning session |
Primary outcome
Build a non-contrast CT radiomic signature of CAD
Time frame: 3 years
Implement a machine learning model to discriminate patients with no CAD from patients with at least minimal disease (CAD-RADS=0 vs. CAD-RADS>0).
Time frame: 3 years
Implement a machine learning model to detect coronary inflammation as defined using the Fat Attenuation Index (FAI ≥ -70.1 HU) in patients with no visible coronary plaque (CAD-RADS=0).
Time frame: 3 years
Build a user-friendly plugin to facilitate users experience and distribution of our technology in clinical practice.
Time frame: 3 years
Evaluate the real-world operationality and performance of the plugin in an international multicentre prospective cohort study.
Time frame: 3 years
Create a national registry of cardiac CT
Time frame: 3 years
2 secondary outcomes
Setup a human blood biobank to identify the peripheral blood mononuclear cells (PBMCs) and plasma proteomics associated with CT data and clinical outcomes.
Time frame: 3 years
Setup a public CT imaging repository
Time frame: 3 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 31 May 2024.
- 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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Coronary Artery Disease and Coronary Atheroscleroses, run by Universidade do Porto at 1 site in Portugal.
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 1 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: opening soon, as of the sponsor's last update on 31 May 2024. 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 Portugal. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Build a non-contrast CT radiomic signature of CAD (time frame: 3 years). The study lists 6 primary outcomes in all. 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 31 May 2024.