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

 Subhi J Al'Aref  ;  Gurpreet Singh  ;  Jeong W Choi  ;  Zhuoran Xu  ;  Gabriel Maliakal  ;  Alexander R van Rosendael  ;  Benjamin C Lee  ;  Zahra Fatima  ;  Daniele Andreini  ;  Jeroen J Bax  ;  Filippo Cademartiri  ;  Kavitha Chinnaiyan  ;  Benjamin J W Chow  ;  Edoardo Conte  ;  Ricardo C Cury  ;  Gudruf Feuchtner  ;  Martin Hadamitzky  ;  Yong-Jin Kim  ;  Sang-Eun Lee  ;  Jonathon A Leipsic  ;  Erica Maffei  ;  Hugo Marques  ;  Fabian Plank  ;  Gianluca Pontone  ;  Gilbert L Raff  ;  Todd C Villines  ;  Harald G Weirich  ;  Iksung Cho  ;  Ibrahim Danad  ;  Donghee Han  ;  Ran Heo  ;  Ji Hyun Lee  ;  Asim Rizvi  ;  Wijnand J Stuijfzand  ;  Heidi Gransar  ;  Yao Lu  ;  Ji Min Sung  ;  Hyung-Bok Park  ;  Daniel S Berman  ;  Matthew J Budoff  ;  Habib Samady  ;  eter H Stone  ;  Renu Virmani  ;  Jagat Narula  ;  Hyuk-Jae Chang  ;  Fay Y Lin  ;  Lohendran Baskaran  ;  Leslee J Shaw  ;  James K Min 
 JACC-CARDIOVASCULAR IMAGING, Vol.13(10) : 2162-2173, 2020-10 
Journal Title
Issue Date
acute coronary syndrome ; coronary computed tomography angiography ; diameter stenosis ; machine learning
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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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Lee, Jee Hyun(이지현)
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Han, Donghee(한동희)
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