Screening Coronary Artery Disease Using artiFicial intelligencE in Non-contrast Computed Tomography (SAFE-CT)

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

Study Overview

• Status: Not yet recruiting
• Conditions: Coronary Artery Disease; Coronary Atheroscleroses
• Intervention / Treatment: Diagnostic test: CT coronary angiography and non-contrast CT

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 Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

Study Locations

• Portugal
  • Porto, Portugal
    • Faculty of Medicine of Porto

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Stable chest pain patients with unknown CAD who underwent a CTCA with paired non-contrast CT

Description

Inclusion Criteria:

- Patient with stable chest pain who underwent a CTCA

Exclusion Criteria:

• Missing non-contrast CT image (coronary calcium score image)
• Known coronary artery disease
• Prior myocardial infarction
• Prior PCI or CABG

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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

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.; Other Names: Non-contrast computed tomography

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Secondary Outcome Measures

Outcome Measure	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

Collaborators and Investigators

• Sponsor: Universidade do Porto
• Collaborators: University of Oxford; University of Edinburgh

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2024
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): December 1, 2027

Study Registration Dates

• First Submitted: May 27, 2024
• First Submitted That Met QC Criteria: May 27, 2024
• First Posted (Actual): May 31, 2024

Study Record Updates

• Last Update Posted (Actual): May 31, 2024
• Last Update Submitted That Met QC Criteria: May 27, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Keywords: Coronary artery disease; Artificial intelligence; Deep learning; Radiomics; Non-contrast computed tomography
• Additional Relevant MeSH Terms: Heart Diseases; Cardiovascular Diseases; Vascular Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Coronary Artery Disease; Myocardial Ischemia; Coronary Disease; Atherosclerosis
• Other Study ID Numbers: SAFE-CT

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No