Benutzer: Gast  Login
Titel:

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

Dokumenttyp:
Article; Journal Article; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural
Autor(en):
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 J W; 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,...     »
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.
Zeitschriftentitel:
JACC Cardiovasc Imaging
Jahr:
2020
Band / Volume:
13
Heft / Issue:
10
Seitenangaben Beitrag:
2162-2173
Volltext / DOI:
doi:10.1016/j.jcmg.2020.03.025
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/32682719
Print-ISSN:
1936-878X
TUM Einrichtung:
Institut für Radiologie und Nuklearmedizin
 BibTeX