A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA


Subhi J. Al'Aref, University of Arkansas for Medical Sciences
Gurpreet Singh, GlaxoSmithKline plc.
Jeong W. Choi, New York Presbyterian Hospital
Zhuoran Xu, New York Presbyterian Hospital
Gabriel Maliakal, Cleerly Inc
Alexander R. van Rosendael, New York Presbyterian Hospital
Benjamin C. Lee, New York Presbyterian Hospital
Zahra Fatima, New York Presbyterian Hospital
Daniele Andreini, IRCCS Centro Cardiologico Monzino
Jeroen J. Bax, Leiden University Medical Center - LUMC
Filippo Cademartiri, Institute for Hospitalization and Care Scientific
Kavitha Chinnaiyan, William Beaumont Hospital
Benjamin J.W. Chow, University of Ottawa, Canada
Edoardo Conte, IRCCS Centro Cardiologico Monzino
Ricardo C. Cury, Baptist Cardiac and Vascular Institute
Gudruf Feuchtner, Medizinische Universitat Innsbruck
Martin Hadamitzky, Deutsches Herzzentrum München
Yong Jin Kim, Seoul National University Hospital
Sang Eun Lee, Yonsei University College of Medicine
Jonathon A. Leipsic, The University of British Columbia
Erica Maffei
Hugo Marques, Hospital da Luz
Fabian Plank, Medizinische Universitat Innsbruck
Gianluca Pontone, IRCCS Centro Cardiologico Monzino
Gilbert L. Raff, William Beaumont Hospital
Todd C. Villines, University of Virginia Health System
Harald G. Weirich, Medizinische Universitat Innsbruck
Iksung Cho, Yonsei University College of Medicine
Ibrahim Danad, Amsterdam UMC - Vrije Universiteit Amsterdam
Donghee Han, Severance Hospital
Ran Heo, Hanyang University Medical Center
Ji Hyun Lee, Severance Hospital
Asim Rizvi, Mayo Clinic

Document Type


Publication Date


Publication Title

JACC: Cardiovascular Imaging


© 2020 American College of Cardiology Foundation 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.





First Page


Last Page






This document is currently not available here.