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Published on: August 28, 2018
AI-Guided Cardiac Computer Tomography in Type 1 Diabetes Patients with Low Coronary Artery Calcium Score
Peter Wohlfahrt1,2, Michal Pazderník3, Natália Marhefková2,4
1Department of Preventive Cardiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Insights
Artificial intelligence-guided coronary computed tomographic angiography (AI-QCT) detects significant atherosclerosis in type 1 diabetes (T1D) patients, even with low coronary artery calcium (CAC) scores. This advanced imaging challenges traditional risk assessment, revealing hidden cardiovascular risks in T1D.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence in Medicine
Background:
- Traditional cardiovascular risk factors lack precision for individual risk stratification.
- Coronary artery calcium (CAC) scoring detects calcified plaques but may underestimate risk in type 1 diabetes (T1D) patients with noncalcified plaques.
- Understanding noncalcified atherosclerosis prevalence in T1D is vital for improved screening.
Purpose of the Study:
- To assess subclinical atherosclerosis burden in T1D patients with CAC <100 using AI-guided quantitative coronary computed tomographic angiography (AI-QCT).
- To evaluate the utility of AI-QCT in identifying clinically significant atherosclerosis and high-risk plaques in T1D.
- To compare AI-QCT findings with traditional risk factors and CAC scoring.
Main Methods:
- Enrolled 167 T1D patients (age ≥30, duration ≥10 years, no manifest ASCVD).
- Assessed CAC and carotid ultrasound in all participants.
- Performed AI-QCT on a subset (n=52) with CAC 0 and carotid plaque or CAC 1-99.
Main Results:
- Only 21% of AI-QCT patients had no coronary artery disease.
- Significant coronary stenosis found in 17%; 73% had high-risk plaques.
- AI-QCT reclassified cardiovascular risk in 58% of patients compared to CAC and 21% compared to STENO1 risk categories.
- Fair agreement between AI-QCT and CAC (κ = 0.25); slight agreement with STENO1 (κ = 0.02).
Conclusions:
- AI-QCT reveals subclinical atherosclerosis and high-risk plaque features missed by CAC and traditional models in T1D.
- Low CAC scores do not reliably equate to low cardiovascular risk in T1D patients.
- AI-QCT offers a more sensitive approach to cardiovascular risk assessment in T1D.
Objective:
Cardiovascular risk stratification based on traditional risk factors lacks precision at the individual level. While coronary artery calcium (CAC) scoring enhances risk prediction by detecting calcified atherosclerotic plaques, it may underestimate risk in individuals with noncalcified plaques-a pattern common in younger type 1 diabetes (T1D) patients. Understanding the prevalence of noncalcified atherosclerosis in T1D is crucial for developing more effective screening strategies. Therefore, this study aimed to assess the burden of clinically significant atherosclerosis in T1D patients with CAC <100 using artificial intelligence (AI)-guided quantitative coronary computed tomographic angiography (AI-QCT).
Methods:
This study enrolled T1D patients aged ≥30 years with disease duration ≥10 years and no manifest or symptomatic atherosclerotic cardiovascular disease (ASCVD). CAC and carotid ultrasound were assessed in all participants. AI-QCT was performed in patients with CAC 0 and at least one plaque in the carotid arteries or those with CAC 1-99.
Results:
Among the 167 participants (mean age 52 ± 10 years; 44% women; T1D duration 29 ± 11 years), 93 (56%) had CAC = 0, 46 (28%) had CAC 1-99, 8 (5%) had CAC 100-299, and 20 (12%) had CAC ≥300. AI-QCT was performed in a subset of 52 patients. Only 11 (21%) had no evidence of coronary artery disease. Significant coronary stenosis was identified in 17% of patients, and 30 (73%) presented with at least one high-risk plaque. Compared with CAC-based risk categories, AI-QCT reclassified 58% of patients, and 21% compared with the STENO1 risk categories. There was only fair agreement between AI-QCT and CAC (κ = 0.25), and a slight agreement between AI-QCT and STENO1 risk categories (κ = 0.02).
Conclusion:
AI-QCT may reveal subclinical atherosclerotic burden and high-risk features that remain undetected by traditional risk models or CAC. These findings challenge the assumption that a low CAC score equates to a low cardiovascular risk in T1D.
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