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