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).

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).

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).

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