A Multiple Regression Model of Combined Duplex Criteria for Detecting Threshold Carotid Stenosis and Predicting the Exact Degree of Carotid Stenosis

Abstract

Combined duplex criteria are commonly used in vascular laboratories for evaluating carotid stenosis. However, most of these combinations are empirical, and systemic validation is lacking. This study was completed using a multiple regression method to evaluate the accuracy of different combined duplex criteria for detecting threshold carotid stenosis and predicting the exact degree of carotid stenosis on angiography. Two hundred sixty-six sets of unilateral carotid duplex and angiographic data were randomly divided into 2 sets: a derivation set and a validation set. The derivation set was used to develop a multiple logistic regression model for detecting 70% threshold carotid stenosis. Age, sex, systolic blood pressure, diastolic blood pressure, Doppler peak systolic velocity (PSV), Doppler and diastolic velocity (EDV), the systolic carotid ratio (SCR), and ophthalmic artery flow direction were tested as independent variables. A multiple linear regression model was also developed for predicting the exact degree of carotid stenosis on angiogram. The validation set was then used to evaluate the accuracy of these models. According to the logistic regression strategy, the best multiple logistic regression model was as follows: probability of threshold carotid stenosis = exp(2.6 PSV - 6.2)/[1 + exp(2.6 PSV - 6.2)]. The best linear regression model was as follows: degree of carotid stenosis = 20.2 PSV - 7.4 EDV + 0.4 SCR + 8.5. Both models proved to be valid following an evaluation of the validation set. This study illustrates that Doppler parameters may be of use in predicting the exact degree of carotid stenosis and the probability of threshold carotid stenosis. This is important if duplex criteria are going to replace angiography as the only tool for selecting endarterectomy candidates.