McLaughlin M S, Hinckley P J, Treiman S M, Kim S-E, Stoddard G J, Parker D L, Treiman G S, McNally J S
From the Department of Radiology (M.S.M., P.J.H., S.M.T., S.-E.K., D.L.P., G.S.T., J.S.M.), Utah Center for Advanced Imaging Research.
Department of Orthopedics (G.J.S.), Study Design and Biostatistics Center.
AJNR Am J Neuroradiol. 2015 Dec;36(12):2360-6. doi: 10.3174/ajnr.A4454. Epub 2015 Sep 3.
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.
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.
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).
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.
磁共振成像（MR成像）通过使用磁化准备快速梯度回波序列，能够高精度地检测斑块内出血。然而，并非所有患者都能轻易进行MR成像检查，许多患者会接受CT血管造影（CTA）检查。我们的目标是确定斑块内出血的重要临床和管腔成像预测指标，以此作为其存在的指标及发病机制的线索。
在这项回顾性横断面研究中，对接受MR成像/MRA卒中检查的患者进行颈动脉斑块内出血成像。我们分析了726个颈动脉斑块，排除了非颈动脉卒中来源的血管（n = 420）、闭塞血管（n = 7）或近乎闭塞的血管（n = 3）。斑块内出血的潜在颈动脉成像预测指标包括直径百分比和毫米狭窄程度、斑块厚度、溃疡形成以及管腔内血栓。记录临床预测指标，并拟合多变量逻辑回归模型。采用向后剔除法确定阈值为双侧P < 0.10时斑块内出血的重要预测指标。还进行了受试者操作特征分析。
颈动脉斑块内出血的预测指标包括斑块厚度（比值比[OR] = 2.20，P < 0.001）、毫米狭窄程度（OR = 0.46，P < 0.001）、溃疡形成（OR = 4.25，P = 0.020）、年龄（OR = 1.11，P = 0.001）和男性（OR = 3.23，P = 0.077）。最终模型的判别价值极佳（曲线下面积 = 0.9,32）。这显著高于仅使用斑块厚度（曲线下面积 = 0.881）、毫米狭窄程度（曲线下面积 = 0.830）或溃疡形成（曲线下面积 = 0.715，P < 0.001）的模型。
对颈动脉斑块内出血的最佳判别需要有关斑块厚度、毫米狭窄程度、溃疡形成、年龄和男性性别的信息。这些因素对斑块内出血具有较高的判别能力，并可能为斑块内出血的发病机制提供线索。当MR成像检查禁忌时，该模型可用于预测斑块内出血的存在情况。
