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Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Artificial intelligence-guided quantitative coronary CT assessment to rule-in or rule-out myocardial ischaemia
Putri Annisa Kamila1,2, Nick S Nurmohamed3,4, Ibrahim Danad5
1Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, The Netherlands.
Insights
AI-based quantitative CT (AI-QCT) parameters, including percent atheroma volume (PAV) and average lumen area (ALA), effectively stratify myocardial ischemia risk. This approach reliably rules out non-obstructive lesions and rules in significant stenoses, improving diagnostic accuracy.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Radiology
Background:
- Coronary artery disease (CAD) diagnosis relies on assessing lesion severity.
- Coronary computed tomography angiography (CCTA) provides anatomical information, but functional significance often requires invasive assessment.
- AI-based quantitative CT (AI-QCT) offers novel parameters for non-invasive risk stratification.
Purpose of the Study:
- To evaluate AI-QCT parameters: diameter stenosis, percent atheroma volume (PAV), and average lumen area (ALA).
- To determine the ability of these parameters to rule-in or rule-out myocardial ischemia.
- To establish a simplified framework for clinical decision-making based on CCTA findings.
Main Methods:
- Post-hoc analysis of patients with suspected CAD from CREDENCE and PACIFIC-1 studies.
- Inclusion of CCTA and invasive fractional flow reserve (FFR) data.
- Evaluation of diameter stenosis, PAV, and ALA as predictors of ischemia, with dichotomization based on median values from the CREDENCE cohort.
Main Results:
- A simplified framework using diameter stenosis, PAV (>14.7%), and ALA (<3.9 mm2) effectively stratified ischemia risk.
- In the CREDENCE study, vessels with 1-24% stenosis were ruled out; 74% of 25-49% stenoses were ruled out.
- In PACIFIC-1, 86% of vessels with <50% stenosis were ruled out, and 61% of vessels with 50-99% stenosis were ruled in.
Conclusions:
- AI-QCT parameters provide a practical approach to stratify myocardial ischemia risk.
- This framework enhances the diagnostic utility of CCTA for non-obstructive and obstructive coronary lesions.
- Streamlined clinical decision-making is achievable through improved non-invasive assessment of CAD severity.
Aims:
To evaluate the ability of artificial intelligence-based quantitative CT (AI-QCT) parameters, diameter stenosis, percent atheroma volume (PAV) and average lumen area (ALA) to rule-in or rule-out ischaemia.
Methods And Results:
This post-hoc, vessel-level analysis included patients with suspected coronary artery disease from the computed tomographic evaluation of atherosclerotic determinants of myocardial ischaemia (CREDENCE) (612 patients; 1727 vessels) and PACIFIC-1 (208 patients; 612 vessels) studies who underwent CCTA and invasive fractional flow reserve (FFR). In addition to diameter stenosis, PAV and ALA were evaluated as key predictors of ischaemia. We report abnormal FFR prevalence based on these variables and define rule-out (<15% ischaemia prevalence, defer further testing), rule-in (>75% prevalence, ischaemia highly likely; further testing typically unnecessary), and intermediate risk (15-75%, consider additional functional assessment). PAV and ALA were dichotomized using median values derived from the CREDENCE cohort (14.7% and 3.9 mm2) and validated in PACIFIC-1. In CREDENCE, all vessels with 1-24% stenosis were ruled-out. Among vessels with 25-49% stenosis, 74% met rule-out criteria, while 26%, characterized by large PAV and small ALA, were intermediate risk. Within the proposed9723 framework vessels with 50-69% stenosis were classified as intermediate risk. For 70-99% stenosis, 93% met rule-in criteria, except a small subset with small PAV and large ALA. In PACIFIC-1, 86% of vessels with <50% stenosis were ruled-out, and 61% of those with 50-99% stenosis were ruled-in.
Conclusion:
A simplified framework incorporating AI-QCT parameters including diameter stenosis, PAV (>14.7%), and ALA (<3.9 mm2), stratifies myocardial ischaemia risk. Most non-obstructive lesions can be ruled-out, while most stenoses >70% are reliably ruled-in. This practical approach enhances the diagnostic utility of CCTA and streamlines clinical decision-making.
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