Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach

Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression

Coronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the model training was also applied. When the models were trained/validated on ROI defined by the difference between follow-up and baseline plaque burden, the average accuracy and Mathews correlation coefficient were 0.70 and 0.41 respectively. Using a ROI partition criterion based only on the baseline's plaque burden resulted in averages of 0.60 accuracy and 0.23 Mathews correlation coefficient. An XGBoost model was capable of predicting plaque progression/regression changes in coronary vascular segments of patients treated with rosuvastatin therapy in 13 months. The proposed method, first of its kind, successfully managed to address the problem of stratification of patients at risk of coronary plaque progression, using IVUS images and standard patient clinical data.

Angiography-derived radial wall strain predicts coronary lesion progression in non-culprit intermediate stenosis

BACKGROUND

Intermediate coronary lesions (ICLs) are highly prevalent but ported mixed prognosis. Radial strain has been associated with plaque vulnerability, yet its role in predicting lesion progression is largely unknown. The purpose of this study was to determine the predictive value of angiography-derived radial wall strain (RWS) for progression of untreated non-culprit ICLs.

METHODS

Post-hoc analysis was conducted in a study cohort including 603 consecutive patients with 808 ICLs identified at index procedure with angiographic follow-up of up to two years. RWS analysis was performed on selected angiographic frames with minimal foreshortening and vessel overlap. Lesion progression was defined as ≥ 20% increase in percent diameter stenosis.

RESULTS

Lesion progression occurred in 49 ICLs (6.1%) with a median follow-up period of 16.8 months. Maximal RWS (RWS_max), frequently located at the proximal and throat plaque regions, distinguished progressive ICLs from silent ones. The largest area under the curve value of 0.75 (95% CI: 0.67-0.82, P < 0.001) was reached at the optimal RWS_max cutoff value of > 12.6%. According to this threshold, 178 ICLs were classified as having a high strain pattern. Exposure to a high strain amplitude with RWS_max > 12.6% was independently associated with an increased risk of lesion progression (adjusted HR = 6.82, 95% CI: 3.67-12.66, P < 0.001).

CONCLUSIONS

Assessment of RWS from coronary angiography is feasible and provides independent prognostic value in patients with untreated ICLs.