Sex Differences in Detection of Acute Coronary Syndrome Culprit Lesion Using Conventional and AI-Enhanced Coronary CT Angiography.
Authors
Affiliations (48)
Affiliations (48)
- Department of Internal Medicine, Division of Cardiology, Seoul National University Hospital, Seoul, South Korea.
- Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
- Division of Cardiology, Department of Medicine, Gangneung Asan Hospital, South Korea.
- 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 Medical School, 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.
- Division of University Cardiology, IRCCS Ospedale Galeazzi Sant'Ambrogio, Department of Clinical and Biomedical Sciences, University of 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.
- Fiona Stanley Hospital, Curtin University, Harry Perkins Institute of Medical Research, Perth, Australia.
- 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.
- Department of Cardiology, Lausanne University Hospital and University of Lausanne, Switzerland.
- Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Centro Integral de Enfermedades Cardiovasculares (CIEC), Hospital Universitario HM Montepríncipe, Madrid, Spain.
- Division of Cardiology, Loyola University Chicago, Chicago, Illinois, USA.
- Division of Cardiology, Edward Hines Jr VA Hospital, Hines, Illinois, USA.
- Department of Cardiology, Washington University School of Medicine, St. Louis, MO, 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, Institute of Science Tokyo, Tokyo, Japan.
- Division of Cardiovascular Medicine, Tsuchiura Kyodo General Hospital, Ibaraki, Japan.
- Department of Cardiology, Osaka Medical and Pharmaceutical University, Takatsuki, Japan.
- Department of Computational Engineering and Sciences, University of Texas, Austin, USA.
- 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, MA, USA.
- Heart & Vascular Institute, McGovern Medical School, University of Texas Health Sciences Center, Houston, TX, USA.
Abstract
Acute coronary syndrome (ACS) exhibits marked sex-related differences in clinical presentation and underlying pathophysiology. However, whether these differences translate into variations in culprit lesion prediction remains unclear. We investigated the sex-specific performance of conventional coronary computed tomography angiography (CCTA) and artificial intelligence-enabled quantitative coronary plaque and hemodynamic assessment (AI-QCPHA) for ACS culprit detection. In this sub-study of the EMERALD II trial (NCT03591328), patients with ACS who underwent CCTA 1 month to 3 years prior to the event were analyzed. Culprit and non-culprit lesions were defined by invasive coronary angiography at the time of ACS. Diagnostic performance of obstructive stenosis (CAD-RADS ≥3) and high-risk plaque (HRP) criteria was compared between women and men. Information gain-based feature selection identified sex-specific AI-QCPHA best predictors across five feature clusters, and their incremental predictive value was evaluated using receiver operating characteristics reported as area under the curve (AUC). A total of 351 patients (90 women, 261 men) with 2,451 lesions were included. Conventional CCTA performed similarly across sexes for obstructive stenosis, whereas HRP differed by sex. AI-QCPHA identified the same best features for ACS culprit prediction in both sexes, and integration of these features improved discriminatory performance beyond conventional CCTA (women AUC 0.84 vs. 0.78, p = 0.002; men 0.82 vs. 0.75, p < 0.001). Among individual predictors, delta FFRCT yielded the highest discriminatory performance in both women and men. AI-QCPHA enhances culprit lesion prediction in obstructive ACS beyond conventional CCTA in both sexes, supporting its potential role for improved culprit-lesion discrimination.