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Prognostic value of a novel artificial intelligence-based coronary CTA-derived ischemia algorithm among patients with

Sarah Bär1, Teemu Maaniitty2, Takeru Nabeta3

  • 1Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland; Department of Cardiology, Bern University Hospital Inselspital, Bern, Switzerland.

Journal of Cardiovascular Computed Tomography
|April 25, 2024
PubMed

Insights

An artificial intelligence algorithm (AI-QCTischemia) can predict cardiac ischemia in patients with obstructive coronary artery disease (CAD). The AI tool showed prognostic value in patients with normal downstream PET perfusion, but not in those with reduced perfusion.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Coronary computed tomography angiography (CTA) is used to assess obstructive coronary artery disease (CAD).
  • Positron emission tomography (PET) perfusion imaging can further evaluate myocardial ischemia in patients with obstructive CAD.
  • A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to predict ischemia directly from coronary CTA.

Purpose of the Study:

  • To evaluate the prognostic value of AI-QCTischemia in patients with obstructive CAD.
  • To compare the prognostic performance of AI-QCTischemia in patients with normal versus abnormal downstream PET perfusion.

Main Methods:

  • A retrospective cohort of patients with obstructive CAD on coronary CTA referred for 15O-H2O-PET perfusion imaging was analyzed.
  • AI-QCTischemia was calculated by blinded analysts.
  • The primary endpoint was a composite of death, myocardial infarction, or unstable angina pectoris, with a median follow-up of 6.2 years.

Main Results:

  • AI-QCTischemia provided conclusive results for 86% of patients.
  • In patients with normal PET perfusion, an abnormal AI-QCTischemia result was associated with a significantly higher risk of the primary endpoint (adjusted HR 2.47).
  • This association was not observed in patients with abnormal PET perfusion (adjusted HR 1.09), with a significant interaction (p=0.039).

Conclusions:

  • AI-QCTischemia demonstrated incremental prognostic value in patients with obstructive CAD and normal downstream PET perfusion.
  • The algorithm did not show significant prognostic value in patients with reduced PET perfusion.
  • AI-QCTischemia may aid in risk stratification for select patients with obstructive CAD.
Abstract

Keywords:
Artificial intelligenceCoronary computed tomography angiographyIschemiaPositron emission tomographyPrognosis

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