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-QCT_ISCHEMIA) 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 (FFR_CT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCT_ISCHEMIA 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-QCT_ISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFR_CT, 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-QCT_ISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFR_CT, 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-QCT_ISCHEMIA test was associated with an HR of 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.