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. 2025 Jun 30;26(7):1163-1173.
doi: 10.1093/ehjci/jeaf121.

Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography

Sarah Bär  1   2 , Juhani Knuuti  1   3 , Antti Saraste  1   4 , Riku Klén  1 , Tanja Kero  5 , Takeru Nabeta  6 , Jeroen J Bax  6 , Ibrahim Danad  7 , Nick S Nurmohamed  7 , Ruurt A Jukema  7 , Paul Knaapen  7 , Teemu Maaniitty  1   3
Affiliations

Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography

Sarah Bär et al. Eur Heart J Cardiovasc Imaging. .

Abstract

Aims: Artificial intelligence (AI) has enabled accurate and fast plaque quantification from coronary computed tomography angiography (CCTA). However, AI detects any coronary plaque in up to 97% of patients. To avoid overdiagnosis, a plaque burden safety cut-off for future coronary events is needed.

Methods and results: Percent atheroma volume (PAV) was quantified with AI-guided quantitative computed tomography in a blinded fashion. Safety cut-off derivation was performed in the Turku CCTA registry (Finland), and pre-defined as ≥90% sensitivity for acute coronary syndrome (ACS). External validation was performed in the Amsterdam CCTA registry (the Netherlands). In the derivation cohort, 100/2271 (4.4%) patients experienced ACS (median follow-up 6.9 years). A threshold of PAV ≥ 2.6% was derived with 90.0% sensitivity and negative predictive value (NPV) of 99.0%. In the validation cohort 27/568 (4.8%) experienced ACS (median follow-up 6.7 years) with PAV ≥ 2.6% showing 92.6% sensitivity and 99.0% NPV for ACS. In the derivation cohort, 45.2% of patients had PAV < 2.6 vs. 4.3% with PAV 0% (no plaque) (P < 0.001) (validation cohort: 34.3% PAV < 2.6 vs. 2.6% PAV 0%; P < 0.001). Patients with PAV ≥ 2.6% had higher adjusted ACS rates in the derivation [Hazard ratio (HR) 4.65, 95% confidence interval (CI) 2.33-9.28, P < 0.001] and validation cohort (HR 7.31, 95% CI 1.62-33.08, P = 0.010), respectively.

Conclusion: This study suggests that PAV up to 2.6% quantified by AI is associated with low-ACS risk in two independent patient cohorts. This cut-off may be helpful for clinical application of AI-guided CCTA analysis, which detects any plaque in up to 96-97% of patients.

Keywords: acute coronary syndrome; artificial intelligence; coronary computed tomography angiography; plaque burden.

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Conflict of interest statement

Conflict of interest: S.B. received research grants to the institution from Medis Medical Imaging Systems, Bangerter-Rhyner Stiftung (Basel, Switzerland), and Abbott outside the submitted work, speaker fees from Cleerly Inc. and travel fees from Sanofi. J.K. received consultancy fees from GE Healthcare and Synektik and speaker fees from Bayer, Lundbeck, Boehringer Ingelheim, Pfizer, and Siemens, outside of the submitted work. A.S. received consultancy fees from Astra Zeneca and Pfizer, and speaker fees from Abbott, Astra Zeneca, Janssen, Novartis, and Pfizer. J.J.B. received speaker fees from Abbott. N.S.N. reports grants from the Dutch Heart Foundation (Dekker 03-007-2023-0068), European Atherosclerosis Society (2023), research funding/speaker fees from Cleerly, Daiichi Sankyo and Novartis, and is co-founder of Lipid Tools. I.D. is a member of Cleerly Scientific Advisory Board and Associate Editor of the European Heart Journal Cardiovascular Imaging. P.K. has received research grants from Cleerly, Inc. and HeartFlow. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

Graphical Abstract
Graphical Abstract
Figure 1
Figure 1
Patient flowchart. AI-QCT , artificial intelligence-guided quantitative computed tomography; CABG, coronary artery bypass grafting; CAD, coronary artery disease; CCTA, coronary computed tomography, angiography; MI, myocardial infarction; PCI, percutaneous coronary intervention.
Figure 2
Figure 2
ROC analysis continuous PAV to predict ACS. (A) ROC analysis for continuous PAV to predict ACS in the derivation cohort (n = 2271). (B) Time-dependent AUC. Dashed lines represent 95% CI.
Figure 3
Figure 3
Kaplan–Meier curves for PAV 2.6%. Kaplan–Meier Curves for ACS in (A) the derivation and (B) the external validation cohort. HRadj, hazard ratio adjusted for age, sex, hypertension, and typical angina; PAV, percent atheroma volume.
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