학술논문
A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA
Document Type
article
Author
Al'Aref, Subhi J; Singh, Gurpreet; Choi, Jeong W; Xu, Zhuoran; Maliakal, Gabriel; van Rosendael, Alexander R; Lee, Benjamin C; Fatima, Zahra; Andreini, Daniele; Bax, Jeroen J; Cademartiri, Filippo; Chinnaiyan, Kavitha; Chow, Benjamin JW; Conte, Edoardo; Cury, Ricardo C; Feuchtner, Gudruf; Hadamitzky, Martin; Kim, Yong-Jin; Lee, Sang-Eun; Leipsic, Jonathon A; Maffei, Erica; Marques, Hugo; Plank, Fabian; Pontone, Gianluca; Raff, Gilbert L; Villines, Todd C; Weirich, Harald G; Cho, Iksung; Danad, Ibrahim; Han, Donghee; Heo, Ran; Lee, Ji Hyun; Rizvi, Asim; Stuijfzand, Wijnand J; Gransar, Heidi; Lu, Yao; Sung, Ji Min; Park, Hyung-Bok; Berman, Daniel S; Budoff, Matthew J; Samady, Habib; Stone, Peter H; Virmani, Renu; Narula, Jagat; Chang, Hyuk-Jae; Lin, Fay Y; Baskaran, Lohendran; Shaw, Leslee J; Min, James K
Source
JACC Cardiovascular Imaging. 13(10)
Subject
Language
Abstract
ObjectivesThis study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics.BackgroundCoronary 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.MethodsUtilizing 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.ResultsCL 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