한빛사논문
Bon-Kwon Koo MD, PhD a,∗, Seokhun Yang MD a,∗, Jae Wook Jung MD a, Jinlong Zhang MD, PhD b, Keehwan Lee MD a, Doyeon Hwang MD a, Kyu-Sun Lee MD, PhD c, Joon-Hyung Doh MD, PhD d, Chang-Wook Nam MD, PhD e, Tae Hyun Kim MD f, Eun-Seok Shin MD, PhD g, Eun Ju Chun MD, PhD h, Su-Yeon Choi MD, PhD i, Hyun Kuk Kim MD, PhD j, Young Joon Hong MD, PhD k, Hun-Jun Park MD, PhD l, Song-Yi Kim MD m, Mirza Husic MD, PhD n, Jess Lambrechtsen MD, PhD n, Jesper M. Jensen MD, PhD o, Bjarne L. Nørgaard MD, PhD o, Daniele Andreini MD, PhD p,q, Pal Maurovich-Horvat MD, PhD r, Bela Merkely MD, PhD s, Martin Penicka MD, PhD t, Bernard de Bruyne MD, PhD t, Abdul Ihdayhid MD, PhD u, Brian Ko MD, PhD u, Georgios Tzimas MD v, Jonathon Leipsic MD, PhD v, Javier Sanz MD w, Mark G. Rabbat MD x, Farhan Katchi MD y, Moneal Shah MD z, Nobuhiro Tanaka MD, PhD aa, Ryo Nakazato MD, PhD bb, Taku Asano MD, PhD bb, Mitsuyasu Terashima MD, PhD cc, Hiroaki Takashima MD, PhD dd, Tetsuya Amano MD, PhD dd, Yoshihiro Sobue MD, PhD ee, Hitoshi Matsuo MD, PhD ee, Hiromasa Otake MD, PhD ff, Takashi Kubo MD, PhD aa, Masahiro Takahata MD, PhD gg, Takashi Akasaka MD, PhD gg, Teruhito Kido MD, PhD hh, Teruhito Mochizuki MD, PhD hh, Hiroyoshi Yokoi MD, PhD ii, Taichi Okonogi MD jj, Tomohiro Kawasaki MD, PhD jj, Koichi Nakao MD, PhD kk, Tomohiro Sakamoto MD, PhD kk, Taishi Yonetsu MD, PhD ll, Tsunekazu Kakuta MD, PhD mm, Yohei Yamauchi MD, PhD nn, Jeroen J. Bax MD, PhD oo, Leslee J. Shaw PhD w, Peter H. Stone MD pp, Jagat Narula MD, PhD qq
aDepartment of Internal Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul National University of College of Medicine, Seoul, South Korea
bDepartment of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
cDepartment of Cardiology, Eulji University Medical Center, Daejeon, South Korea
dDepartment of Medicine, Inje University Ilsan Paik Hospital, Goyang, South Korea
eDepartment of Medicine, Keimyung University Dongsan Medical Center, Daegu, South Korea
fDepartment of Cardiology, Ulsan Medical Center, Ulsan, South Korea
gDepartment of Cardiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea
hDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, South Korea
iDepartment of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea
jDepartment of Internal Medicine and Cardiovascular Center, Chosun University Hospital, University of Chosun College of Medicine, Gwangju, South Korea
kDepartment of Cardiology, Chonnam National University Hospital, Gwangju, South Korea
lDivision of Cardiology, Department of Internal Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, South Korea
mDivision of Cardiology, Department of Internal Medicine, Jeju National University Hospital, Jeju, South Korea
nDepartment of Cardiology, Odense University Hospital, Svendborg, Denmark
oDepartment of Cardiology, Aarhus University Hospital, Aarhus, Denmark
pCentro Cardiologico Manzano, Istituti di Ricovero e Cura a Carattere Scientifico Milan, Italy
qDepartment of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
rDepartment of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
sThe Heart and Vascular Center, Semmelweis University, Budapest, Hungary
tCardiovascular Center Aalst, Onze Lieve Vrouwziekenhuis-Clinic, Aalst, Belgium
uMonash Cardiovascular Research Centre, Monash University and Monash Heart, Monash Health, Clayton, Victoria, Australia
vDepartment of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada
wCardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
xDivision of Cardiology, Loyola University Chicago, Chicago, Illinois, USA
yDepartment of Cardiology, Washington University School of Medicine in St. Louis, Missouri, USA
zDepartment of Cardiology, Allegheny General Hospital, Pittsburgh, Pennsylvania, USA
aaDepartment of Cardiology, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan
bbCardiovascular Center, St Luke’s International Hospital, Tokyo, Japan
ccDepartment of Cardiovascular Medicine, Toyohashi Heart Center, Aichi, Japan
ddDepartment of Cardiology, Aichi Medical University, Nagakute, Japan
eeDepartment of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan
ffDivision of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan
ggDepartment of Cardiovascular Medicine, Wakayama Medical University, Wakayama, Japan
hhDepartment of Radiology, Ehime University Graduate School of Medicine, Ehime, Japan
iiCardiovascular Center, Fukuoka Sanno Hospital, Fukuoka, Japan
jjCardiovascular Center, Shin-Koga Hospital, Kurume, Japan
kkDivision of Cardiology, Saiseikai Kumamoto Hospital Cardiovascular Center, Kumamoto, Japan
llDepartment of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan
mmDivision of Cardiovascular Medicine, Tsuchiura Kyodo General Hospital, Ibaraki, Japan
nnDepartment of Cardiology, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
ooDepartment of Cardiology, Heart Lung Centre, Leiden University Medical Centre, Leiden, the Netherlands
ppDivision of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
qqMcGovern Medical School, University of Texas Health Sciences Center, Houston, Texas, USA
Address for correspondence: Dr Bon-Kwon Koo
∗Drs Koo and Yang contributed equally to this work.
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).
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