Risk prediction of recurrent ischemic stroke based on Carotid Plaque-RADS: construction and validation of a nomogram
Miao Qiao1, Ting Zhou1, Rui Wang1
1Department of Ultrasound Imaging, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Insights
A new nomogram model incorporating Carotid Plaque-RADS effectively predicts recurrent ischemic stroke (RIS). This tool aids clinicians in assessing RIS risk, improving patient outcomes for ischemic stroke (IS) patients.
Area of Science:
- Neurology
- Cardiovascular Medicine
- Radiology
Background:
- Ischemic stroke (IS) has a high recurrence rate and severe consequences.
- The Carotid Plaque Reporting and Data System (Carotid Plaque-RADS) is a new tool for assessing cerebrovascular risk.
- Further validation is needed for its predictive power in recurrent ischemic stroke (RIS).
Purpose of the Study:
- To develop a nomogram model for predicting the likelihood of recurrent ischemic stroke (RIS).
- To integrate Carotid Plaque-RADS as a key component in the RIS prediction model.
- To evaluate the predictive performance of the developed nomogram.
Main Methods:
- Retrospective review of 286 acute ischemic stroke (IS) patients.
- Classification of carotid plaque data using Carotid Plaque-RADS.
- Multivariate logistic regression to identify independent risk factors for RIS.
- Development and validation of a nomogram model for RIS risk prediction.
Main Results:
- Significant differences in LDL, hypertension, atrial fibrillation, severe carotid stenosis, and Carotid Plaque-RADS categories between IS and RIS groups.
- LDL, hypertension, atrial fibrillation, severe carotid stenosis, and Carotid Plaque-RADS identified as independent risk factors for RIS.
- The nomogram model demonstrated good calibration and predictive performance (AUC 0.79/0.76), outperforming models using only clinical features or Carotid Plaque-RADS alone.
Conclusions:
- The nomogram model incorporating Carotid Plaque-RADS offers a novel and effective tool for clinical risk assessment of recurrent ischemic stroke (RIS).
- The model shows favorable predictive performance, aiding in better clinical decision-making for IS patients.
Background And Purpose:
Ischemic stroke (IS) is characterized by a high recurrence rate and more serious repercussions. Recently, the Carotid Plaque Reporting and Data System (Carotid Plaque-RADS) has been introduced to gauge and forecast the risk of cerebrovascular incidents. More studies are required to confirm its predictive power for recurrent ischemic stroke (RIS). We aimed to create a nomogram model that can evaluate the likelihood of RIS, with Carotid Plaque-RADS serving as a crucial instrument in this model.
Methods:
We carried out a retrospective review of 286 patients diagnosed with acute IS at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine between January 2020 and January 2025. The study population consisted of two groups: the IS group (129 patients) and the RIS group (157 patients), depending on whether they experienced a recurrence of IS. Carotid ultrasound examination and clinical data were gathered and classified according to Carotid Plaque-RADS. Independent risk factors for the RIS were determined using multivariate logistic regression analyses. Subsequently, we developed a nomogram model to forecast RIS risk and evaluated its performance.
Results:
The RIS and IS groups showed significant differences in low-density lipoprotein (LDL), hypertension, atrial fibrillation, severe carotid stenosis, and Carotid Plaque-RADS categories. Multivariate logistic regression analysis identified LDL, hypertension, atrial fibrillation, severe carotid stenosis, and Carotid Plaque-RADS as independent risk factors for RIS. The nomogram model built using these risk factors demonstrated good calibration (H-L goodness-of-fit test P = 0.354). Internal and external validation demonstrated that the calibration curves were consistent with the original curves. The nomogram model combining Carotid Plaque-RADS and clinical features showed area under the curve (AUC) values of 0.79 and 0.76, outperforming models using only clinical features (AUC 0.72 and 0.70) or only Carotid Plaque-RADS (AUC 0.71 and 0.69). The model showed considerable clinical benefit within the 0.2-0.8 threshold range in the decision curve analysis (DCA).
Conclusion:
The nomogram model based on Carotid Plaque-RADS provides a novel and effective tool for clinical risk assessment and demonstrates favorable predictive performance for RIS.
