An observational study in Coronary Artery Disease and Coronary Atheroscleroses, sponsored by Universidade do Porto. Not yet recruiting at 1 site in Portugal. Open to participants aged 18 Years to 100 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-05-31.
Sponsored by Universidade do Porto · Observational
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.
5,676 studies on the registry are indexed under Coronary Artery Disease; 958 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 335 across 1,978 observational studies indexed under Coronary Artery Disease.
Browse Coronary Artery Disease studies →Universidade do Porto is the lead sponsor of 172 studies on the registry; 53 are open to participants now.
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Stable chest pain patients with unknown CAD who underwent a CTCA with paired non-contrast CT
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- Patient with stable chest pain who underwent a CTCA
Exclusion Criteria:
CAD: Presence of minimal coronary artery disease (i.e., coronary stenosis 0-25%) Normal coronary arteries: No visible coronary atherosclerosis
Diagnostic Test: CT coronary angiography and non-contrast CT
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.
Also known as: Non-contrast computed tomography
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
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
Plan to share: Undecided
No publications or documents are linked to this record.
Start is estimated for Jun 2024; primary completion for Jun 2027, 36 months later.
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This study is not yet recruiting, as verified in Feb 2024. You cannot join it, but the record below documents what was studied.
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