Search

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 < 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 specif

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 < 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

Background: Visual assessment of diameter-stenosis on Computed Tomography Coronary Angiography (CTCA) lacks specificity to determine functional significance of coronary artery stenosis. Percent-aggregate plaque volume (%APV) and ASLA score, which incorporates Area of Stenosis, Lesion length, and area of myocardium subtended estimated by APPROACH score (Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease) have been described to predict lesion specific ischaemia in focal lesions with intermediate stenosis. Methods and Results: Included were 81 patients (mean age 64.7 +/- 9 years, 62% male; 94 vessels) who underwent 320- detector-row CTCA, invasive coronary angiography and fractional-flow-reserve (FFR). We examined vessels with wide range of diameter stenosis (mid to severe) and with multiple lesions. Invasive FFR of <=0.8 was considered functionally significant. The first 54 patients (62 vessels) formed the derivation cohort. ASLA score was the best predictor of FFR <= 0.8 (AUC 0.83, p < 0.001) compared to %APV (0.72), CT >50% (0.76), APPROACH score (0.79), area-stenosis (0.73), diameter-stenosis (0.74), minimum-luminal-diameter (0.74), minimal-luminal-area (0.72), and lesion-length (0.67). ASLA score and not %APV, provided incremental predictive value when added to CT > 50 [(NRI 0.71, p = 0.005) vs. (NRI 0.01, p = 0.96)]. In the validation cohort of 27 patients (32 vessels), the ASLA score (AUC 0.85) was again a better predictor of FFR <= 0.8 compared to %APV (0.71), CT > 50% (0.66) and other CT indices. The AUC of ASLA score was superior to CTCA>50% (p = 0.001). Conclusion(s): ASLA score is a novel predictor of functional significance of coronary stenosis and adds incremental predictive value to CT > 50 but %APV did not.Copyright © 2018 Elsevier B.V.