AJNR. American journal of neuroradiology · 2015
Optimal Prediction of Carotid Intraplaque Hemorrhage Using Clinical and Lumen Imaging Markers.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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McLaughlin M S, Hinckley P J, Treiman S M, Kim S-E, Stoddard G J, Parker D L, Treiman G S, McNally J S
Аннотация
BACKGROUND AND PURPOSE: MR imaging detects intraplaque hemorrhage with high accuracy by using the magnetization-prepared rapid acquisition of gradient echo sequence. Still, MR imaging is not readily available for all patients, and many undergo CTA instead. Our goal was to determine essential clinical and lumen imaging predictors of intraplaque hemorrhage, as indicators of its presence and clues to its pathogenesis.
MATERIALS AND METHODS: In this retrospective cross-sectional study, patients undergoing stroke work-up with MR imaging/MRA underwent carotid intraplaque hemorrhage imaging. We analyzed 726 carotid plaques, excluding vessels with non-carotid stroke sources (n = 420), occlusions (n = 7), or near-occlusions (n = 3). Potential carotid imaging predictors of intraplaque hemorrhage included percentage diameter and millimeter stenosis, plaque thickness, ulceration, and intraluminal thrombus. Clinical predictors were recorded, and a multivariable logistic regression model was fitted. Backward elimination was used to determine essential intraplaque hemorrhage predictors with a thresholded 2-sided P < .10. Receiver operating characteristic analysis was also performed.
RESULTS: Predictors of carotid intraplaque hemorrhage included plaque thickness (OR = 2.20, P < .001), millimeter stenosis (OR = 0.46, P < .001), ulceration (OR = 4.25, P = .020), age (OR = 1.11, P = .001), and male sex (OR = 3.23, P = .077). The final model discriminatory value was excellent (area under the curve = 0.932). This was significantly higher than models using only plaque thickness (area under the curve = 0.881), millimeter stenosis (area under the curve = 0.830), or ulceration (area under the curve= 0.715, P < .001).
CONCLUSIONS: Optimal discrimination of carotid intraplaque hemorrhage requires information on plaque thickness, millimeter stenosis, ulceration, age, and male sex. These factors predict intraplaque hemorrhage with high discriminatory power and may provide clues to the pathogenesis of intraplaque hemorrhage. This model could be used to predict the presence of intraplaque hemorrhage when MR imaging is contraindicated.
MATERIALS AND METHODS: In this retrospective cross-sectional study, patients undergoing stroke work-up with MR imaging/MRA underwent carotid intraplaque hemorrhage imaging. We analyzed 726 carotid plaques, excluding vessels with non-carotid stroke sources (n = 420), occlusions (n = 7), or near-occlusions (n = 3). Potential carotid imaging predictors of intraplaque hemorrhage included percentage diameter and millimeter stenosis, plaque thickness, ulceration, and intraluminal thrombus. Clinical predictors were recorded, and a multivariable logistic regression model was fitted. Backward elimination was used to determine essential intraplaque hemorrhage predictors with a thresholded 2-sided P < .10. Receiver operating characteristic analysis was also performed.
RESULTS: Predictors of carotid intraplaque hemorrhage included plaque thickness (OR = 2.20, P < .001), millimeter stenosis (OR = 0.46, P < .001), ulceration (OR = 4.25, P = .020), age (OR = 1.11, P = .001), and male sex (OR = 3.23, P = .077). The final model discriminatory value was excellent (area under the curve = 0.932). This was significantly higher than models using only plaque thickness (area under the curve = 0.881), millimeter stenosis (area under the curve = 0.830), or ulceration (area under the curve= 0.715, P < .001).
CONCLUSIONS: Optimal discrimination of carotid intraplaque hemorrhage requires information on plaque thickness, millimeter stenosis, ulceration, age, and male sex. These factors predict intraplaque hemorrhage with high discriminatory power and may provide clues to the pathogenesis of intraplaque hemorrhage. This model could be used to predict the presence of intraplaque hemorrhage when MR imaging is contraindicated.