1. A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA
- Author
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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, and Min, James K
- Subjects
Biomedical and Clinical Sciences ,Cardiovascular Medicine and Haematology ,Clinical Sciences ,Cardiovascular ,Clinical Research ,Heart Disease ,Heart Disease - Coronary Heart Disease ,Atherosclerosis ,Detection ,screening and diagnosis ,4.2 Evaluation of markers and technologies ,Algorithms ,Case-Control Studies ,Computed Tomography Angiography ,Coronary Angiography ,Coronary Artery Disease ,Coronary Stenosis ,Humans ,Plaque ,Atherosclerotic ,Predictive Value of Tests ,Severity of Illness Index ,acute coronary syndrome ,coronary computed tomography angiography ,diameter stenosis ,machine learning ,Cardiorespiratory Medicine and Haematology ,Cardiovascular System & Hematology ,Cardiovascular medicine and haematology ,Clinical sciences - 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
- Published
- 2020