Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia
- PMID: 38483420
- DOI: 10.1016/j.jcmg.2024.01.007
Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia
Abstract
Background: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs.
Objectives: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE).
Methods: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1.
Results: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with aHR: 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE.
Conclusions: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.
Keywords: artificial intelligence; atherosclerosis; coronary computed tomography angiography; coronary ischemia; stress testing.
Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.
Conflict of interest statement
Funding Support and Author Disclosures This project has been supported by the Foundation “De Drie Lichten” in the Netherlands. Dr Nurmohamed has received grants from the Dutch Heart Foundation (Dekker 03-007-2023-0068) and grants from the European Atherosclerosis Society (2023); and is co-founder of Lipid Tools. Mr Wang, Dr Chan, Ms Crabtree, Ms Aquino, Dr Min, and Dr Earls are employees of Cleerly Inc. Dr Choi has received grant support from GW Heart and Vascular Institute; equity in Cleerly, Inc; and has received consulting fees from Siemens Healthineers. Dr Knaapen has received research grants from HeartFlow, Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
