A boosted ensemble algorithm for determination of plaque stability in high-risk patients on coronary CTA

Al'Aref, S.J.; Singh, G.; Choi, J.W.; Xu, Z.R.; Maliakal, G.; Rosendael, A.R. van; ... ; Min, J.K. (2020)

Article / Letter to editor

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 casecontrol 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 thismodel 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. (c) 2020 by the American College of Cardiology Foundation.

• acute coronary syndrome
• coronary computed tomography angiography
• diameter stenosis
• machine learning

• All authors: Al'Aref, S.J.; Singh, G.; Choi, J.W.; Xu, Z.R.; Maliakal, G.; Rosendael, A.R. van; Lee, B.C.; Fatima, Z.; Andreini, D.; Bax, J.J.; Cademartiri, F.; Chinnaiyan, K.; Chow, B.J.W.; Conte, E.; Cury, R.C.; Feuchtner, G.; Hadamitzky, M.; Kim, Y.J.; Lee, S.E.; Leipsic, J.A.; Maffei, E.; Marques, H.; Plank, F.; Pontone, G.; Raff, G.L.; Villines, T.C.; Weirich, H.G.; Cho, I.; Danad, I.; Han, D.; Heo, R.; Lee, J.H.; Rizvi, A.; Stuijfzand, W.J.; Gransar, H.; Lu, Y.; Sung, J.M.; Park, H.B.; Berman, D.S.; Budoff, M.J.; Samady, H.; Stone, P.H.; Virmani, R.; Narula, J.; Chang, H.J.; Lin, F.Y.; Baskaran, L.; Shaw, L.J.; Min, J.K.
• Date: 2020-10-01
• Journal: JACC: Cardiovascular Imaging
• Volume: 13
• Issue: 10
• Pages: 2162 - 2173
• DOI: doi:10.1016/j.jcmg.2020.03.025
• Link: https://www.sciencedirect.com/science/article/pii/S1936878X2030423X?via%...