Subhi J Al'Aref1, Gurpreet Singh2, Jeong W Choi3, Zhuoran Xu3, Gabriel Maliakal4, Alexander R van Rosendael3, Benjamin C Lee3, Zahra Fatima3, Daniele Andreini5, Jeroen J Bax6, Filippo Cademartiri7, Kavitha Chinnaiyan8, Benjamin J W Chow9, Edoardo Conte5, Ricardo C Cury10, Gudruf Feuchtner11, Martin Hadamitzky12, Yong-Jin Kim13, Sang-Eun Lee14, Jonathon A Leipsic15, Erica Maffei16, Hugo Marques17, Fabian Plank18, Gianluca Pontone5, Gilbert L Raff8, Todd C Villines19, Harald G Weirich18, Iksung Cho20, Ibrahim Danad21, Donghee Han22, Ran Heo23, Ji Hyun Lee24, Asim Rizvi25, Wijnand J Stuijfzand3, Heidi Gransar26, Yao Lu3, Ji Min Sung22, Hyung-Bok Park22, Daniel S Berman27, Matthew J Budoff28, Habib Samady29, Peter H Stone30, Renu Virmani31, Jagat Narula32, Hyuk-Jae Chang33, Fay Y Lin3, Lohendran Baskaran34, Leslee J Shaw3, James K Min4. 1. Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas. Electronic address: SJAlaref@UAMS.edu. 2. GlaxoSmithKline, Brentford, United Kingdom. 3. Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. 4. Cleerly Health, New York, New York. 5. Centro Cardiologico Monzino, Institute for Research Hospitalization, and Health Care, Milan, Italy. 6. Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands. 7. Cardiovascular Imaging Center, Institute of Diagnostic and Nuclear Development, Institute for Research Hospitalization, and Health Care, Naples, Italy. 8. Department of Cardiology, William Beaumont Hospital, Royal Oak, Michigan. 9. Department of Medicine and Radiology, University of Ottawa, Ottawa, Canada. 10. Department of Radiology, Miami Cardiac and Vascular Institute, Miami, Florida. 11. Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. 12. Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. 13. Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea. 14. Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea; Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University Health System, Yonsei University College of Medicine, South Korea. 15. Department of Medicine and Radiology, University of British Columbia, Vancouver, Canada. 16. Department of Radiology, ASUR Marche Area Vasta 1, Urbino, Italy. 17. Cardiovascular Imaging Unit, Unit of Cardiovascular Imaging, Hospital da Luz, Lisbon, Portugal. 18. Department of Radiology, Innsbruck Medical University, Innsbruck, Austria. 19. Division of Cardiovascular Medicine, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia. 20. Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea; Department of Cardiology, Chung-Ang University Hospital, Seoul, South Korea. 21. Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands. 22. Integrative Cardiovascular Imaging Research Center, Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea. 23. Division of Cardiology, Department of Internal Medicine, Hanyang University Medical Center, Seoul, Korea. 24. Integrative Cardiovascular Imaging Research Center, Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea; Department of Cardiology, Myongji Hospital, Goyang, South Korea. 25. Department of Radiology, Mayo Clinic, Rochester, Minnesota. 26. Department of Imaging, Cedars Sinai Medical Center, Los Angeles, California. 27. Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, California. 28. Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, California. 29. Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia. 30. Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts. 31. CVPath Institute, Gaithersburg, Maryland. 32. Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Zena and Michael A. Wiener Cardiovascular Institute, Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York. 33. Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea. 34. Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York; Department of Cardiovascular Medicine, National Heart Centre, Singapore.
Abstract
OBJECTIVES: This study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics. BACKGROUND: Coronary computed tomography angiography (CTA) has been validated for patient-level prediction of ACS. However, the applicability of coronary CTA to CL assessment is not known. METHODS: Utilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested case-control study of 468 patients with baseline coronary CTA, the study included ACS patients with invasive coronary angiography-adjudicated CLs that could be aligned to CL precursors on baseline coronary CTA. Separate blinded core laboratories adjudicated CLs and performed atherosclerotic plaque evaluation. Thereafter, the study used a boosted ensemble algorithm (XGBoost) to develop a predictive model of CLs. Data were randomly split into a training set (80%) and a test set (20%). The area under the receiver-operating characteristic curve of this model was compared with that of diameter stenosis (model 1), high-risk plaque features (model 2), and lesion-level features of CL precursors from the ICONIC study (model 3). Thereafter, the machine learning (ML) model was applied to 234 non-ACS patients with 864 lesions to determine model performance for CL exclusion. RESULTS: CL precursors were identified by both coronary angiography and baseline coronary CTA in 124 of 234 (53.0%) patients, with a total of 582 lesions (containing 124 CLs) included in the analysis. The ML model demonstrated significantly higher area under the receiver-operating characteristic curve for discriminating CL precursors (0.774; 95% confidence interval [CI]: 0.758 to 0.790) compared with model 1 (0.599; 95% CI: 0.599 to 0.599; p < 0.01), model 2 (0.532; 95% CI: 0.501 to 0.563; p < 0.01), and model 3 (0.672; 95% CI: 0.662 to 0.682; p < 0.01). When applied to the non-ACS cohort, the ML model had a specificity of 89.3% for excluding CLs. CONCLUSIONS: In a high-risk cohort, a boosted ensemble algorithm can be used to predict CL from non-CL precursors on coronary CTA.
OBJECTIVES: This study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics. BACKGROUND: Coronary computed tomography angiography (CTA) has been validated for patient-level prediction of ACS. However, the applicability of coronary CTA to CL assessment is not known. METHODS: Utilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested case-control study of 468 patients with baseline coronary CTA, the study included ACS patients with invasive coronary angiography-adjudicated CLs that could be aligned to CL precursors on baseline coronary CTA. Separate blinded core laboratories adjudicated CLs and performed atherosclerotic plaque evaluation. Thereafter, the study used a boosted ensemble algorithm (XGBoost) to develop a predictive model of CLs. Data were randomly split into a training set (80%) and a test set (20%). The area under the receiver-operating characteristic curve of this model was compared with that of diameter stenosis (model 1), high-risk plaque features (model 2), and lesion-level features of CL precursors from the ICONIC study (model 3). Thereafter, the machine learning (ML) model was applied to 234 non-ACS patients with 864 lesions to determine model performance for CL exclusion. RESULTS: CL precursors were identified by both coronary angiography and baseline coronary CTA in 124 of 234 (53.0%) patients, with a total of 582 lesions (containing 124 CLs) included in the analysis. The ML model demonstrated significantly higher area under the receiver-operating characteristic curve for discriminating CL precursors (0.774; 95% confidence interval [CI]: 0.758 to 0.790) compared with model 1 (0.599; 95% CI: 0.599 to 0.599; p < 0.01), model 2 (0.532; 95% CI: 0.501 to 0.563; p < 0.01), and model 3 (0.672; 95% CI: 0.662 to 0.682; p < 0.01). When applied to the non-ACS cohort, the ML model had a specificity of 89.3% for excluding CLs. CONCLUSIONS: In a high-risk cohort, a boosted ensemble algorithm can be used to predict CL from non-CL precursors on coronary CTA.
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