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Published on: November 22, 2024
[Building of a Clinical Prediction Model for Hemodynamic Depression after Carotid Artery Stenting]
Wei-Dong Fan1, Kun Liu1, Tong Qiao2
1Department of Vascular Surgery,the Affiliated Suqian First People's Hospital of Nanjing Medical University,Suqian,Jiangsu 223800,China.
Insights
This study identified key risk factors for hemodynamic depression (HD) after carotid artery stenting (CAS). A predictive model incorporating diabetes, smoking, plaque characteristics, and lesion location effectively forecasts HD occurrence.
Area of Science:
- Vascular Surgery
- Cardiology
- Medical Prediction Modeling
Context:
- Carotid artery stenting (CAS) is a common procedure to treat carotid artery stenosis.
- Hemodynamic depression (HD) is a potential complication following CAS.
- Identifying predictors of HD is crucial for patient management.
Purpose:
- To analyze risk factors associated with hemodynamic depression (HD) after carotid artery stenting (CAS).
- To develop and validate a clinical prediction model for HD post-CAS.
Summary:
- A study of 116 patients undergoing CAS identified lower rates of diabetes and smoking, and higher rates of hypertension, bilateral CAS, calcified plaque, eccentric plaque, and proximity of stenosis to the carotid bifurcation as predictors of HD.
- A multivariate logistic regression model incorporating these factors demonstrated an Area Under the Curve (AUC) of 0.807, indicating good predictive performance for HD after CAS.
Impact:
- The developed clinical prediction model can aid clinicians in identifying patients at higher risk of HD after CAS.
- This facilitates personalized risk assessment and potentially tailored peri-procedural management strategies.
- Understanding these predictors can improve patient outcomes and procedural safety in CAS.
Abstract:
Objective To analyze the risk factors and build a clinical prediction model for hemodynamic depression (HD) after carotid artery stenting (CAS). Methods A total of 116 patients who received CAS in the Department of Vascular Surgery,Drum Tower Clinical College of Nanjing Medical University and the Department of Vascular Surgery,the Affiliated Suqian First People's Hospital of Nanjing Medical University from January 1,2016 to January 1,2022 were included in this study.The patients were assigned into a HD group and a non-HD group.The clinical baseline data and vascular disease characteristics of each group were collected,and multivariate Logistic regression was employed to identify the independent predictors of HD after CAS and build a clinical prediction model.The receiver operating characteristic (ROC) curve was drawn,and the area under the ROC curve (AUC) was calculated to evaluate the predictive performance of the model. Results The HD group had lower proportions of diabetes (P=0.014) and smoking (P=0.037) and higher proportions of hypertension (P=0.031),bilateral CAS (P=0.018),calcified plaque (P=0.001),eccentric plaque (P=0.003),and the distance<1 cm from the minimum lumen level to the carotid bifurcation (P=0.009) than the non-HD group.The age,sex,coronary heart disease,symptomatic carotid artery stenosis,degree of stenosis,and length of lesions had no statistically significant differences between the HD group and the non-HD group (all P>0.05).Based on the above predictive factors,a clinical prediction model was established,which showed the AUC of 0.807 and the 95% CI of 0.730-0.885 (P<0.001).The model demonstrated the sensitivity of 62.7% and the specificity of 87.7% when the best cut-off value of the model score reached 12.5 points. Conclusions Diabetes,smoking,calcified plaque,eccentric plaque,and the distance<1 cm from the minimum lumen level to the carotid bifurcation are independent predictors of HD after CAS.The clinical prediction model built based on the above factors has good performance in predicting the occurrence of HD after CAS.
