Key result
Novel clinical risk score identifies significant carotid stenosis with ~0.75 C-statistic, outperforming prior models.
Why the study?
Increasing evidence that aggressive medical therapy and lifestyle changes reduce stroke risk in carotid stenosis has renewed interest in screening, but tools to identify high-risk individuals are needed.
Can a predictive scoring system based on demographics and clinical risk factors accurately identify individuals at high risk for carotid stenosis?
Cross-Sectional (n=2,885,257)
Can a predictive scoring system based on demographics and clinical risk factors accurately identify individuals at high risk for carotid stenosis?
Effect estimate: C statistic 0.753
A novel predictive scoring system using common demographic and clinical risk factors can effectively identify individuals at high risk for >50% carotid stenosis (C-statistic 0.753) to guide targeted, cost-effective screening.
May aid targeted CS screening decisions via the score; extends prior models but requires prospective validation before practice change.
OBJECTIVE: To develop a model for the identification of individuals at risk for carotid stenosis (CS) that could be useful in a clinical setting when trying to decide whether screening is worthwhile. BACKGROUND: Evidence that aggressive medical therapy and life style changes reduce the risk of stroke in individuals with CS is increasing and has led to a renewed interest in screening for CS. METHODS: Data on demographics and risk factors were obtained from 2,885,257 individuals who had carotid Duplex scans by Life Line Screening between 2003 and 2008. Multivariable logistic regression analysis was used to identify independent risk factors for CS (>50% stenosis). A scoring system was developed where risk factors were assigned a weighted score. Predictive ability was assessed by calculating C statistics and r2. RESULTS: In the screened cohort, 71,004 patients (2.4%) had CS. Independent risk factors include advanced age, smoking, peripheral arterial disease, high blood pressure, coronary artery disease, diabetes, cholesterol, and abdominal aortic aneurysm. African Americans, Asians, and Hispanics had reduced risk than whites. Exercise and consumption of fruit, vegetables, and nuts had a modest protective effect. A predictive scoring system was created that identifies individuals with CS more efficiently (C statistic = 0.753) than previously published models. CONCLUSIONS: We provide a model that enables identification of individuals who have a high probability of having CS. This model can be helpful in designing targeted screening programs that are cost-effective.
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Greco et al. (2013) conducted a cross-sectional in Carotid stenosis (n=2,885,257). Predictive scoring system vs. Previously published models was evaluated on Predictive ability for carotid stenosis (>50% stenosis) (C statistic 0.753). A predictive scoring system based on demographic and clinical risk factors identified individuals with >50% carotid stenosis with a C statistic of 0.753, outperforming prior models.
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