Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy

摘要

Features of high-risk coronary artery plaques prone to major adverse cardiac events (MACE) were identified by intravascular ultrasound (IVUS) virtual histology (VH). These plaque features are: Thin-cap fibroatheroma (TCFA), plaque burden PB≥70%, or minimal luminal area MLA≤4mm². Identification of arterial locations likely to later develop such high-risk plaques may help prevent MACE. We report a machine learning method for prediction of future high-risk coronary plaque locations and types in patients under statin therapy. Sixty-one patients with stable angina on statin therapy underwent baseline and one-year follow-up VH-IVUS non-culprit vessel examinations followed by quantitative image analysis. For each segmented and registered VH-IVUS frame pair (n=6341), location-specific (≈0.5mm) vascular features and demographic information at baseline were identified. Seven independent support vector machine (SVM) classifiers with seven different feature subsets were trained to predict high-risk plaque types one year later. A leave-one-patient-out cross-validation was used to evaluate the prediction power of different feature subsets. The experimental results showed that our machine learning method predicted future TCFA with correctness of 85.9%, 81.7%, and 77.0% (G-mean) for baseline plaque phenotypes of TCFA, thick-cap fibroatheroma, and non-fibroatheroma, respectively. For predicting PB≥70%, correctness was 80.8% for baseline PB≥70% and 85.6% for 50%≤PB<70%. Accuracy of predicted MLA≤4mm² was 81.6% for baseline MLA≤4mm² and 80.2% for 4mm²<MLA≤6mm². Location-specific prediction of future high-risk coronary artery plaques is feasible through machine learning using focal vascular features and demographic variables. Our approach outperforms previously-reported results and shows the importance of local factors on high-risk coronary artery plaque development.