A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA.
JACC Cardiovasc Imaging
; 13(10): 2162-2173, 2020 10.
Article
em En
| MEDLINE
| ID: mdl-32682719
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença da Artéria Coronariana
/
Placa Aterosclerótica
Tipo de estudo:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
JACC Cardiovasc Imaging
Assunto da revista:
ANGIOLOGIA
/
CARDIOLOGIA
/
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2020
Tipo de documento:
Article