Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial

Yumin Kim, The George Washington University School of Medicine, Washington, District of Columbia, USA.
Andrew D. Choi, The George Washington University School of Medicine, Washington, District of Columbia, USA.
Anha Telluri, The George Washington University School of Medicine, Washington, District of Columbia, USA.
Isabella Lipkin, The George Washington University School of Medicine, Washington, District of Columbia, USA.
Andrew J. Bradley, The George Washington University School of Medicine, Washington, District of Columbia, USA.
Alfateh Sidahmed, The George Washington University School of Medicine, Washington, District of Columbia, USA.
Rebecca Jonas, Jefferson Medical Institute, Philadelphia, Pennsylvania, USA.
Daniele Andreini, Centro Cardiologico Monzino IRCCS, Milan, Italy.
Ravi Bathina, CARE Hospital and FACTS Foundation, Hyderabad, India.
Andrea Baggiano, Centro Cardiologico Monzino IRCCS, Milan, Italy.
Rodrigo Cerci, Quanta Diagnostico Nuclear, Curitiba, Brazil.
Eui-Young Choi, Gangnam Severance Hospital, Seoul, South Korea.
Jung-Hyun Choi, Pusan National University Hospital, Busan, South Korea.
So-Yeon Choi, Ajou University Hospital, Gyeonggi-do, South Korea.
Namsik Chung, Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea.
Jason Cole, Cardiology Associates of Mobile, Mobile, Alabama, USA.
Joon-Hyung Doh, Inje University, Ilsan Paik Hospital, Gyeonggi-do, South Korea.
Sang-Jin Ha, Gangneung Asan Hospital, Gangwon-do, South Korea.
Ae-Young Her, Kangwon National University Hospital, Gangwon-do, South Korea.
Cezary Kepka, National Institute of Cardiology, Warsaw, Poland.
Jang-Young Kim, Wonju Severance Hospital, Gangwon-do, South Korea.
Jin Won Kim, Korea University Guro Hospital, Seoul, South Korea.
Sang-Wook Kim, Chung-Ang University Hospital, Seoul, South Korea.
Woong Kim, Yeungnam University Hospital, Daegu, South Korea.
Gianluca Pontone, Centro Cardiologico Monzino IRCCS, Milan, Italy.
Todd C. Villines, University of Virginia Medical Center, Charlottesville, Virginia, USA.
Iksung Cho, Chung-Ang University Hospital, Seoul, South Korea.
Ibrahim Danad, VU Medical Center, Amsterdam, the Netherlands.
Ran Heo, Hanyang University, Hanyang University Medical Center, Seoul, South Korea.
Sang-Eun Lee, Myongji Hospital, Seonam University College of Medicine, Gyeonggi-do, South Korea.
Ji Hyun Lee, Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea.
Hyung-Bok Park, Myongji Hospital, Seonam University College of Medicine, Gyeonggi-do, South Korea.

Document Type

Journal Article

Publication Date

2-27-2023

Journal

Clinical cardiology

DOI

10.1002/clc.23995

Keywords

CCTA; artificial Intelligence; atherosclerosis; coronary artery disease; coronary computed tomography; fractional flow reserve; quantitative coronary angiography

Abstract

AIMS: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). METHODS: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. RESULTS: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. CONCLUSIONS: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.

Department

Medicine

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