What does this research mean for the field?

AI-QCT ISCHEMIA demonstrates superior diagnostic accuracy for detecting myocardial ischemia compared to FFR-CT and MPI, with AUCs of 0.82 in patients with diabetes and 0.87 in those without diabetes. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.SUPPORTS_CONSENSUS.

What question did this study set out to answer?

This research evaluates how well a novel AI algorithm predicts coronary ischemia using different patient health conditions.

February 8, 2026

Diagnostic performance of a novel AI algorithm for determining coronary ischemia (AI-QCT ISCHEMIA) according to patient comorbidities

Key Result

AI-QCT ISCHEMIA showed higher diagnostic accuracy (AUC 0.82-0.88) than FFR-CT and MPI in detecting myocardial ischemia, regardless of diabetes, hypertension, or dyslipidemia.

Key Points

This research evaluates how well a novel AI algorithm predicts coronary ischemia using different patient health conditions.
Performed a post-hoc pooled analysis of CREDENCE and PACIFIC-1 studies.
Included symptomatic patients undergoing various heart imaging tests.
Compared the diagnostic accuracy of the AI algorithm to existing methods at the vessel level.
AI-QCT ISCHEMIA had an AUC of 0.82 in patients with diabetes, higher than FFR-CT (0.75) and MPI (0.64).
In patients without diabetes, the AUC was 0.87 for AI-QCT ISCHEMIA.
For patients with hypertension, AI-QCT ISCHEMIA achieved an AUC of 0.87.
In those without hypertension, it showed an AUC of 0.85.
Overall, AI-QCT ISCHEMIA demonstrated superior diagnostic accuracy to MPI and FFR-CT, regardless of comorbid conditions.

Structured PICO

Does AI-QCT ISCHEMIA improve diagnostic accuracy for predicting coronary ischemia compared to MPI and FFR-CT in symptomatic patients with suspected CAD across comorbidity subgroups?

Population

513 symptomatic patients with suspected coronary artery disease from a pooled analysis of the CREDENCE (n=305, mean age 64.4, 69% men) and PACIFIC-1 (n=208, mean age 58.1, 64% men) studies.

Intervention

AI-QCT ISCHEMIA (a novel AI algorithm applied to coronary computed tomography angiography)

Comparator

Myocardial perfusion imaging (MPI) and FFR-CT (with invasive fractional flow reserve [FFR] as the reference standard)

Outcome

Diagnostic accuracy (vessel-level AUC) in predicting myocardial ischemia assessed using invasive FFR across subgroups (diabetes, hypertension, dyslipidemia)surrogate

AI-QCT ISCHEMIA provides superior diagnostic accuracy for detecting coronary ischemia compared to FFR-CT and MPI, maintaining its performance regardless of patient comorbidities like diabetes, hypertension, or dyslipidemia.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Abstract Background This study evaluated the diagnostic accuracy of a novel AI algorithm (AI-QCT ISCHEMIA) for the prediction of coronary ischemia as assessed using fractional flow reserve (FFR) according to the presence of diabetes, hypertension, and dyslipidemia. Methods This is a post-hoc pooled analysis of CREDENCE (validation cohort; 305 patients, mean age 64.4±9.8 years, 69% men, 868 vessels) and PACIFIC-1 (208 patients, mean age 58.1±8.7 years; 64% men, 612 vessels) studies. Symptomatic patients with suspected coronary artery disease underwent coronary computed tomography angiography (CCTA), myocardial perfusion imaging (MPI), FFR-CT, and invasive FFR. The diagnostic accuracy of AI-QCT ISCHEMIA in predicting myocardial ischemia assessed using invasive FFR was compared to MPI and FFR-CT across different subgroups using vessel-level AUC analysis. Results AI-QCT ISCHEMIA had an AUC of 0.82, compared to 0.75 for FFR-CT and 0.64 for MPI in patients with diabetes, while in those without diabetes the AUCs were 0.87, 0.78, and 0.67. In patients with hypertension, the AUCs were 0.87, 0.77, and 0.67, while in those without hypertension, the AUCs were 0.85, 0.78, and 0.65. In patients with dyslipidemia, the AUCs were 0.84, 0.78, and 0.63, while in patients without dyslipidemia, the AUCs were 0.88, 0.76, and 0.69. Conclusions AI-QCT ISCHEMIA demonstrated high diagnostic accuracy to detect myocardial ischemia regardless of diabetes, hypertension and dyslipidemia status, with consistent superiority to MPI and FFR-CT.

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