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... Show moreOBJECTIVES 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. Show less
Bakhshi, H.; Meyghani, Z.; Kishi, S.; Magalhaes, T.A.; Vavere, A.; Kitslaar, P.H.; ... ; Arbab-Zadeh, A. 2019
OBJECTIVES This study sought to investigate the performance of various cardiac computed tomography (CT)-derived atherosclerotic plaque metrics for predicting provocable myocardial ischemia... Show moreOBJECTIVES This study sought to investigate the performance of various cardiac computed tomography (CT)-derived atherosclerotic plaque metrics for predicting provocable myocardial ischemia.BACKGROUND The association of coronary arterial diameter stenosis with myocardial ischemia is only modest, but cardiac CT provides several other, readily available atherosclerosis metrics, which may have incremental value.METHODS The study analyzed 873 nonstented coronary arteries and their myocardial perfusion territories in 356 patients (mean 62 years of age) enrolled in the CORE320 (Coronary Artery Evaluation using 320-row Multidetector Computed Tomography Angiography and Myocardial Perfusion) study. Myocardial perfusion defects in static CT perfusion imaging were graded at rest and after adenosine in 13 myocardial segments using a 4-point scale. The summed difference score was calculated by subtracting the summed rest score from the summed stress score. Reversible ischemia was defined as summed difference score >= 1. In a sensitivity analysis, results were also provided using single-photon emission computed tomography (SPECT) as the reference standard. Vessel based predictor variables included maximum percent diameter stenosis, lesion length, coronary calcium score, maximum cross-sectional calcium arc, percent atheroma volume (PAV), low-attenuation atheroma volume, positive (external) vascular remodeling, and subjective impression of "vulnerable plaque." The study used logistic regression models to assess the association of plaque metrics with myocardial ischemia.RESULTS In univariate analysis, all plaque metrics were associated with reversible ischemia. In the adjusted logistic model, only maximum percent diameter stenosis (1.26; 95% confidence interval: 1.15 to 1.38) remained an independent predictor. With SPECT as outcome variable, PAV and "vulnerable" plaque remained predictive after adjustment. In vessels with intermediate stenosis (40% to 70%), no single metric had clinically meaningful incremental value.CONCLUSIONS Various plaque metrics obtained by cardiac CT predict provocable myocardial ischemia by CT perfusion imaging through their association with maximum percent stenosis, while none had significant incremental value. With SPECT as reference standard, PAV and "vulnerable plaque" remained predictors of ischemia after adjustment but the predictive value added to stenosis assessment alone was small. (C) 2019 by the American College of Cardiology Foundation. Show less
