JACC Cardiovasc Imaging. 2024;17(9):1062-1076.
Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.
제1저자 구본권, 양석훈 · 교신저자 구본권
소속 (43)
- Department of Internal Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul National University of College of Medicine, Seoul, South Korea. Electronic address: bkkoo@snu.ac.kr.
- Department of Internal Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul National University of College of Medicine, Seoul, South Korea.
- Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
- Department of Cardiology, Eulji University Medical Center, Daejeon, South Korea.
- Department of Medicine, Inje University Ilsan Paik Hospital, Goyang, South Korea.
- Department of Medicine, Keimyung University Dongsan Medical Center, Daegu, South Korea.
- Department of Cardiology, Ulsan Medical Center, Ulsan, South Korea.
- Department of Cardiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea.
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, South Korea.
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea.
- Department of Internal Medicine and Cardiovascular Center, Chosun University Hospital, University of Chosun College of Medicine, Gwangju, South Korea.
- Department of Cardiology, Chonnam National University Hospital, Gwangju, South Korea.
- Division of Cardiology, Department of Internal Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea.
- Division of Cardiology, Department of Internal Medicine, Jeju National University Hospital, Jeju, South Korea.
- Department of Cardiology, Odense University Hospital, Svendborg, Denmark.
- Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
- Centro Cardiologico Manzano, Istituti di Ricovero e Cura a Carattere Scientifico, Milan, Italy; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy.
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary.
- The Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
- Cardiovascular Center Aalst, Onze Lieve Vrouwziekenhuis-Clinic, Aalst, Belgium.
- Monash Cardiovascular Research Centre, Monash University and Monash Heart, Monash Health, Clayton, Victoria, Australia.
- Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada.
- Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
- Division of Cardiology, Loyola University Chicago, Chicago, Illinois, USA.
- Department of Cardiology, Washington University School of Medicine in St. Louis, Missouri, USA.
- Department of Cardiology, Allegheny General Hospital, Pittsburgh, Pennsylvania, USA.
- Department of Cardiology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan.
- Cardiovascular Center, St Luke's International Hospital, Tokyo, Japan.
- Department of Cardiovascular Medicine, Toyohashi Heart Center, Aichi, Japan.
- Department of Cardiology, Aichi Medical University, Nagakute, Japan.
- Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan.
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan.
- Department of Cardiovascular Medicine, Wakayama Medical University, Wakayama, Japan.
- Department of Radiology, Ehime University Graduate School of Medicine, Ehime, Japan.
- Cardiovascular Center, Fukuoka Sanno Hospital, Fukuoka, Japan.
- Cardiovascular Center, Shin-Koga Hospital, Kurume, Japan.
- Division of Cardiology, Saiseikai Kumamoto Hospital Cardiovascular Center, Kumamoto, Japan.
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan.
- Division of Cardiovascular Medicine, Tsuchiura Kyodo General Hospital, Ibaraki, Japan.
- Department of Cardiology, Osaka Medical and Pharmaceutical University, Takatsuki, Japan.
- Department of Cardiology, Heart Lung Centre, Leiden University Medical Centre, Leiden, the Netherlands.
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- McGovern Medical School, University of Texas Health Sciences Center, Houston, Texas, USA.
Abstract
BACKGROUND A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization.
OBJECTIVES This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA).
METHODS Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort.
RESULTS Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA.
CONCLUSIONS AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).
Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Keywords
acute coronary syndromeartificial intelligencecoronary CT angiographyhemodynamicsplaque characteristics
Conflict of interest statement
Funding Support and Author Disclosures This study received funding from HeartFlow Inc. The company performed the computational fluid dynamics, and artificial intelligence quantitative coronary plaque analysis but was not involved in the study design, collection, analysis, and interpretation of data; the writing of this paper; or the decision to submit it for publication. Dr Koo has received institutional research grants from Abbott, Philips, and HeartFlow Inc. Dr Nam has received institutional research grants from Abbott and Genoss. Dr Merkely has received direct personal payments for speaker fees or studies from Abbott, AstraZeneca, Biotronik, Boehringer Ingelheim, CSL Behring, Daiichi Sankyo, DUKE Clinical Institute, Medtronic, and Novartis and institutional grants from Abbott, AstraZeneca, Biotronik, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, CSL Behring, Daiichi Sankyo, DUKE Clinical Institute, Eli Lilly, Medtronic, Novartis, Terumo, and VIFOR Pharma. Dr de Bruyne has received institutional unrestricted research grants from Abbott, Boston Scientific, and Biotronik; has received consulting fees from Abbott, Opsens, and Boston Scientific; and is a shareholder for Siemens, GE, Bayer, Philips, HeartFlow Inc, Edwards Life Sciences, Sanofi, and Omega Pharma. Dr Ko has equity in Artrya and has received honoraria from Medtronic, Abbott Vascular, and Canon Medical. Dr Leipsic is a consultant for and holds stock options in HeartFlow Inc and modest personal core lab fees from Arineta. Dr Shah has received honoraria as a speaker for HeartFlow Inc. Dr Bax has received unrestricted research grants from Abbott and Edwards Lifesciences to the Department of Cardiology, Leiden University Medical Center, the Netherlands. Dr Shaw has received honoraria from HeartFlow Inc and Elucid Imaging. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
그 밖의 정보
- 출판 형태
- Journal Article, Observational Study, Validation Study
- 저널
- JACC. Cardiovascular imaging · eISSN 1876-7591
- 전자 출판
- 2024-05-15
- MeSH
- Aged · Female · Humans · Male · Middle Aged · Acute Coronary Syndrome* / physiopathology / diagnostic imaging · Artificial Intelligence* · Computed Tomography Angiography* · Coronary Angiography* · Coronary Artery Disease* / physiopathology / diagnostic imaging · Coronary Circulation · Coronary Vessels / physiopathology / diagnostic imaging · Hemodynamics · Multidetector Computed Tomography · Plaque, Atherosclerotic* · Predictive Value of Tests · Prognosis · Radiographic Image Interpretation, Computer-Assisted · Reproducibility of Results · Risk Assessment · Risk Factors · Rupture, Spontaneous · Severity of Illness Index · Time Factors
- 데이터
- ClinicalTrials.gov: NCT03591328