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Published on: May 14, 2013
30-Day Risk Score for Mortality and Stroke in Patients with Carotid Artery Stenosis Using Artificial Intelligence
Rohini J Patel1, Daniel Willie-Permor1, Austin Fan1
1Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA.
Insights
Combining clinical factors and plaque morphology improves prediction of stroke and death in patients with carotid artery stenosis. This integrated approach offers a more accurate risk assessment than traditional methods alone.
Area of Science:
- Cardiovascular Imaging
- Neurology
- Medical Informatics
Background:
- Carotid artery stenosis intervention decisions rely on stenosis percentage and symptoms.
- Plaque morphology's role in stroke prediction is under-assessed.
- A need exists for predictive models incorporating plaque morphology for stroke and death risk.
Purpose of the Study:
- To develop a predictive model and risk score for 30-day stroke and death.
- To evaluate the additive value of carotid plaque morphology in risk prediction.
- To integrate clinical and imaging data for enhanced risk stratification.
Main Methods:
- Analysis of computed tomographic angiography head/neck data from 2010-2021.
- Three-dimensional plaque imaging using image recognition software.
- Stepwise backward regression, AUC, and AIC for model selection and assessment.
- Risk score modeled after the Framingham Study.
Main Results:
- Three models were developed: clinical variables only (AUC 0.737), plaque morphology only (AUC 0.644), and combined variables (AUC 0.759).
- The combined model (Model C) demonstrated superior predictive performance with the highest AUC and lowest AIC.
- Key predictors in the combined model included age, sex, matrix volume, history of TIA/stroke, BMI, PVAT, lipid-rich necrotic core, COPD, and hyperlipidemia.
Conclusions:
- Integrating clinical factors with plaque morphology significantly enhances the prediction of mortality and stroke risk in carotid artery stenosis.
- A risk score incorporating these factors can identify high-risk patients, with 3 points indicating a 20% stroke/death risk.
- Further prospective studies are recommended to validate these predictive findings.
Background:
The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology.
Methods:
Patients with a computed tomographic angiography head/neck between 2010 and 2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each computed tomography was used to create a three-dimensional image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with area under the receiver operating characteristic curves (AUCs). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke.
Results:
We created 3 models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, peripheral arterial disease, hyperlipidemia, body mass index (BMI), chronic obstructive pulmonary disease (COPD), and history of transient ischemia attack (TIA)/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue (PVAT) volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, PVAT, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC.
Conclusions:
Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model has a 20% chance of stroke/death. Further prospective studies are needed to validate our findings.
