A risk score for carotid plaque as an assessment risk of cardiovascular risk among patients with hypertension

An accurate and explainable ensemble learning method for carotid plaque prediction in an asymptomatic population

BACKGROUND AND OBJECTIVE

The identification of carotid plaque, one of the most crucial tasks in stroke screening, is of great significance in the assessment of subclinical atherosclerosis and preventing the onset of stroke. However, traditional ultrasound examination is not prevalent or cost-effective for asymptomatic people, particularly low-income individuals in rural areas. Thus, it is necessary to develop an accurate and explainable model for early identification of the risk of plaque prevalence that can help in the primary prevention of stroke.

METHODS

We developed an ensemble learning method to predict the occurrence of carotid plaques. A dataset comprising 1440 subjects (50% with plaques and 50% without plaques) and ten-fold cross-validation were utilized to evaluate the model performance. Four machine learning methods (extreme gradient boosting (XGBoost), gradient boosting decision tree, random forest, and support vector machine) were evaluated. Subsequently, the interpretability of the XGBoost model, which provided the best performance, was analyzed from three aspects: feature importance, feature effect on prediction model, and feature effect on prediction decision for a specific subject.

RESULTS

The XGBoost algorithm provided the best performance (sensitivity: 0.8678, specificity: 0.8592, accuracy: 0.8632, F1 score: 0.8621, area under the curve: 0.8635) in carotid plaque prediction and also had excellent performance under missing data circumstances. Further, interpretability analysis showed that the decisions of the XGBoost model were highly congruent with clinical knowledge.

CONCLUSION

The model results are superior to those of state-of-the-art methods. Thus, it is a promising carotid plaque prediction tool that could be used in the primary prevention of stroke.

Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease

Carotid artery plaque is a measure of atherosclerosis and is associated with future risk of atherosclerotic cardiovascular disease (ASCVD), which encompasses coronary, cerebrovascular, and peripheral arterial diseases. With advanced imaging techniques, computerized tomography (CT) and magnetic resonance imaging (MRI) have shown their potential superiority to routine ultrasound to detect features of carotid plaque vulnerability, such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC), and calcification. The correlation between imaging features and histological changes of carotid plaques has been investigated. Imaging of carotid features has been used to predict the risk of cardiovascular events. Other techniques such as nuclear imaging and intra-vascular ultrasound (IVUS) have also been proposed to better understand the vulnerable carotid plaque features. In this article, we review the studies of imaging specific carotid plaque components and their correlation with risk scores.