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| Name | Class |
|---|---|
| University of Oxford | OTHER |
| University of Edinburgh | OTHER |
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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
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:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CT coronary angiography and non-contrast CT | Diagnostic Test | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Build a non-contrast CT radiomic signature of CAD | 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). | 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). | 3 years | |
| Build a user-friendly plugin to facilitate users experience and distribution of our technology in clinical practice. | 3 years | |
| Evaluate the real-world operationality and performance of the plugin in an international multicentre prospective cohort study. | 3 years | |
| Create a national registry of cardiac CT | 3 years |
| Measure | Description | Time Frame |
|---|---|---|
| Setup a human blood biobank to identify the peripheral blood mononuclear cells (PBMCs) and plasma proteomics associated with CT data and clinical outcomes. | 3 years | |
| Setup a public CT imaging repository | 3 years |
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Inclusion Criteria:
- Patient with stable chest pain who underwent a CTCA
Exclusion Criteria:
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Stable chest pain patients with unknown CAD who underwent a CTCA with paired non-contrast CT
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Faculty of Medicine of Porto | Porto | Portugal |
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| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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Peripheral blood samples for proteomics analysis
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| D001161 |
| Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |