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Development and validation of a web-based dynamic nomogram to predict individualized risk of severe carotid artery
Jian Huang1,2,3, Zhuoran Li4, Xiaozhu Liu5
1Scientific Research Department, First People's Hospital of Zigong City, Zigong, China.
Insights
This study identified six risk factors for severe carotid artery stenosis (CAS) in ischemic stroke (IS) patients. A web-based tool was developed to predict individual CAS risk, aiding tailored treatment strategies.
Area of Science:
- Neurology
- Cardiovascular Medicine
- Medical Informatics
Background:
- Severe carotid artery stenosis (CAS) is a significant cause of recurrent ischemic stroke (IS), with diagnosis often delayed in resource-limited settings.
- Early identification of high-risk patients is crucial for timely intervention and improved prognosis.
- Current diagnostic methods may be inaccessible in certain regions, necessitating alternative prediction strategies.
Purpose of the Study:
- To develop and validate a non-invasive dynamic prediction model for identifying high-risk severe CAS in IS patients.
- To identify key clinical and laboratory risk factors associated with severe CAS in the IS population.
- To create a user-friendly web-based tool for individualized risk assessment of severe CAS.
Main Methods:
- Retrospective cohort study of 739 IS patients from July 2017 to March 2021.
- Risk factor identification using Least Absolute Shrinkage and Selection Operator (LASSO) and Multivariate Logistic Regression (MLR).
- Model validation using C-statistic, Area Under the Curve (AUC), Decision Curve Analysis (DCA), Clinical Impact Curve (CIC), and calibration plots. A web-based tool was developed.
Main Results:
- 66.0% of IS patients were diagnosed with severe CAS.
- Six key predictors identified: history of stroke, serum sodium, hsCRP, CRP, basophil percentage, and MCHC.
- The prediction model demonstrated good discrimination (C-statistic/AUC = 0.70) and calibration, with clinical utility confirmed by DCA and CIC.
Conclusions:
- Six key risk factors for severe CAS in IS patients were identified.
- A validated web-based dynamic nomogram was developed for predicting individual severe CAS risk.
- This tool can facilitate personalized, risk-stratified, and timely treatment strategies for IS patients with potential CAS.
Objectives:
Delays in diagnosing severe carotid artery stenosis (CAS) are prevalent, particularly in low-income regions with limited access to imaging examinations. CAS is a major contributor to the recurrence and poor prognosis of ischemic stroke (IS). This retrospective cohort study proposed a non-invasive dynamic prediction model to identify potential high-risk severe carotid artery stenosis in patients with ischemic stroke.
Methods:
From July 2017 to March 2021, 739 patients with ischemic stroke were retrospectively recruited from the Department of Neurology at Liuzhou Traditional Chinese Medical Hospital. Risk factors for severe CAS were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression (MLR) methods. The model was constructed after evaluating multicollinearity. The model's discrimination was assessed using the C-statistic and area under the curve (AUC). Its clinical utility was evaluated through the decision curve analysis (DCA) and the clinical impact curve (CIC). Calibration was examined using a calibration plot. To provide individualized predictions, a web-based tool was developed to estimate the risk of severe CAS.
Results:
Among the patients, 488 of 739 (66.0%) were diagnosed with severe CAS. Six variables were incorporated into the final model: history of stroke, serum sodium, hypersensitive C-reactive protein (hsCRP), C-reactive protein (CRP), basophil percentage, and mean corpuscular hemoglobin concentration (MCHC). Multicollinearity was ruled out through correlation plots, variance inflation factor (VIF) values, and tolerance values. The model demonstrated good discrimination, with a C-statistic/AUC of 0.70 in the test set. The DCA and CIC indicated that clinical decisions based on the model could benefit IS patients. The calibration plot showed strong concordance between predicted and observed probabilities. The web-based prediction model exhibited robust performance in estimating the risk of severe CAS.
Conclusion:
This study identified six key risk factors for severe CAS in IS patients. In addition, we developed a web-based dynamic nomogram to predict the individual risk of severe CAS. This tool can potentially support tailored, risk-based, and time-sensitive treatment strategies.
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