Carotid Plaque-RADS Score Combined with Pericarotid Fat Density-An Incremental Prediction Model for Stroke Recurrence
Jinhua Qian1, Qinjie Chi2, Li Zhu3
1Department of Radiology, Affiliated Hospital 2 of Nantong University, Nantong 226600, China; Department of Intervention, Affiliated Hospital 2 of Nantong University, Nantong 226600, China.
Insights
A new risk index combining carotid plaque imaging (RADS) and fat density (PFD) accurately predicts stroke recurrence. This comprehensive risk index (CRI) improves risk stratification, especially for patients with mild-to-moderate stenosis.
Area of Science:
- Neuroimaging and Cerebrovascular Disease
- Cardiovascular Research
- Radiology and Medical Imaging
Background:
- Accurate prediction of stroke recurrence is crucial for effective patient management.
- Current methods for risk stratification may not fully capture the complexity of carotid plaque vulnerability.
- Carotid plaque reporting and data system (RADS) and pericarotid fat density (PFD) are emerging imaging biomarkers.
Purpose of the Study:
- To evaluate the prognostic value of combined carotid plaque-RADS score and PFD for predicting stroke recurrence.
- To explore the utility of a novel comprehensive risk index (CRI) in stroke risk stratification.
- To assess the predictive performance of the combined model compared to stenosis degree alone.
Main Methods:
- Development of a binary comprehensive risk index (CRI) integrating carotid plaque-RADS and PFD.
- Utilized Kaplan-Meier survival analysis, multivariate logistic regression, ROC, and DCA to assess predictive value.
- Assessed CRI's performance in predicting stroke recurrence over stenosis degree.
Main Results:
- Recurrent stroke occurred in 23.3% of patients over a mean follow-up of 17.24 months.
- The CRI significantly improved stroke recurrence risk stratification, particularly in patients with mild-to-moderate stenosis.
- Independent predictors included plaque-RADS ≥ 3, CRI, affected-side PFD, and bilateral PFD difference; the combined model achieved an AUC of 0.892.
Conclusions:
- Integrating carotid plaque-RADS and PFD via the CRI significantly enhances stroke recurrence risk prediction accuracy.
- The combined assessment model offers valuable insights for personalized stroke prevention and treatment strategies.
- This approach is particularly beneficial for refining risk assessment in patients with mild-to-moderate carotid stenosis.
Rationale And Objectives:
This study aimed to evaluate the prognostic value of combined carotid plaque reporting and data system (RADS) score and pericarotid fat density (PFD) for predicting stroke recurrence risk, and to explore its utility in stroke risk stratification.
Methods:
We developed a novel binary comprehensive risk index (CRI) that integrates the carotid plaque-RADS and PFD: low CRI (RADS <3 and PFD ≤ -74 HU) and high CRI (RADS ≥3 or PFD > -74 HU). Net reclassification improvement, Kaplan-Meier survival analysis, multivariate logistic regression, receiver operating characteristic curves (ROC), and decision curve analysis (DCA) were used to assess the predictive value of CRI over stenosis degree.
Results:
During a mean follow-up period of 17.24±11.93 months, 64 of 272 patients (23.3%) experienced recurrent stroke. CRI significantly improved stroke recurrence risk stratification in mild-to-moderate stenosis patients. Kaplan-Meier survival analysis revealed significant differences in stroke recurrence rates across varying plaque-RADS and CRI (P < 0.0001). Independent predictors of stroke recurrence included plaque-RADS ≥ 3 (OR=2.68, 95% CI: 1.03-6.96), CRI (OR=8.25, 95% CI: 2.23-30.44), affected-side PFD (OR=0.97, 95% CI: 0.94-0.99), and bilateral PFD difference (OR=1.09, 95% CI: 1.05-1.13). The combined model incorporating stenosis degree, plaque-RADS, affected-side PFD, bilateral PFD difference, and CRI demonstrated superior prediction performance, achieving an area under the ROC curve of 0.892.
Conclusion:
Integrating carotid plaque-RADS and PFD significantly enhances the accuracy of stroke recurrence risk prediction, especially in patients with mild-to-moderate stenosis. This combined assessment model provides valuable insights for personalized prevention and treatment strategies for stroke recurrence.
