Prognostic Value of a Coronary Computed Tomography Angiography-Derived Ischemia Algorithm: Comparison Against Hybrid Coronary Computed Tomography Angiography/Positron Emission Tomography Imaging

Teemu Maaniitty; Sarah Bär; Takeru Nabeta; Jeroen J Bax; Antti Saraste; Juhani Knuuti

doi:10.1161/JAHA.124.040726

Prognostic Value of a Coronary Computed Tomography Angiography-Derived Ischemia Algorithm: Comparison Against Hybrid Coronary Computed Tomography Angiography/Positron Emission Tomography Imaging

J Am Heart Assoc. 2025 Nov 18;14(22):e040726. doi: 10.1161/JAHA.124.040726. Epub 2025 Nov 6.

Authors

Teemu Maaniitty^{1

2}, Sarah Bär^{1

3}, Takeru Nabeta⁴, Jeroen J Bax^{4

5

6}, Antti Saraste^{1

5

6}, Juhani Knuuti^{1

2

6}

Affiliations

¹ Turku PET Centre Turku University Hospital and University of Turku Turku Finland.
² Department of Clinical Physiology, Nuclear Medicine and PET Turku University Hospital Turku Finland.
³ Department of Cardiology Bern University Hospital Inselspital Bern Switzerland.
⁴ Department of Cardiology Leiden University Medical Center Leiden the Netherlands.
⁵ Heart Center Turku University Hospital and University of Turku Turku Finland.
⁶ Faculty of Medicine University of Turku Turku Finland.

PMID: 41195775
DOI: 10.1161/JAHA.124.040726

Abstract

Background: Artificial intelligence-guided quantitative computed tomography ischemia (AI-QCT_ischemia) is a novel machine-learning method for predicting myocardial ischemia from coronary computed tomography angiography (CCTA). This observational cohort study aimed to compare the long-term prognostic value of AI-QCT_ischemia with hybrid CCTA/positron emission tomography (PET) myocardial perfusion imaging in suspected coronary artery disease (CAD).

Methods: Symptomatic patients with suspected CAD underwent CCTA with selective downstream PET to detect ischemic CAD. Blinded reanalysis of CCTA images was done using the AI-QCT_ischemia algorithm, providing a binary result (normal versus abnormal).

Results: In the full analysis set (n=2271), hybrid CCTA/PET imaging was successful in 94% of the patients and AI-QCT_ischemia evaluation was feasible in 83%, resulting in a per-protocol set of 1772 patients (19% with ischemic CAD on hybrid CCTA/PET and 25% with abnormal AI-QCT_ischemia). There was moderate-to-substantial agreement between the methods (Cohen's κ=0.61). During a median follow-up of 7.0 years, 177 (10%) patients experienced the composite end point of all-cause death, myocardial infarction, or unstable angina. Ischemic CAD on hybrid CCTA/PET was predictive of the composite end point (hazard ratio [HR], 2.35 [95% CI, 1.62-3.40]; P<0.001), after adjustment for clinical variables and early (6-month) myocardial revascularization. Similarly, an abnormal (ischemic) AI-QCT_ischemia result was independently predictive of adverse outcomes (adjusted HR, 1.98 [95% CI, 1.39-2.80]; P<0.001). The adjusted models, including either hybrid CCTA/PET or AI-QCT_ischemia, demonstrated similar discriminative ability (C-index 0.734 versus 0.729; P=0.53).

Conclusions: The AI-QCT_ischemia algorithm demonstrated long-term prognostic value comparable to hybrid CCTA/PET perfusion imaging in suspected CAD.

Keywords: artificial intelligence; coronary computed tomography angiography; myocardial perfusion imaging; positron emission tomography; prognosis.

Publication types

Comparative Study
Observational Study

MeSH terms

Aged
Algorithms*
Computed Tomography Angiography* / methods
Coronary Angiography* / methods
Coronary Artery Disease* / diagnostic imaging
Coronary Artery Disease* / mortality
Female
Humans
Machine Learning
Male
Middle Aged
Myocardial Ischemia* / diagnostic imaging
Myocardial Perfusion Imaging* / methods
Positron-Emission Tomography*
Predictive Value of Tests
Prognosis