Department of Neuroscience, Metro Health, University of Michigan, Wyoming, MI, USA.
Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
Clin Neuroradiol. 2021 Dec;31(4):1111-1119. doi: 10.1007/s00062-020-00985-0. Epub 2020 Dec 23.
Despite advancement in mechanical thrombectomy (MT) techniques, 10-30% of MT for large vessel occlusions (LVO) are unsuccessful. Current prediction models fail to address the association between patient-specific factors and reperfusion. We aimed to evaluate objective, easily reproducible, admission clinical and radiological biomarkers that predict unsuccessful MT.
We analyzed consecutive anterior LVO MT patients at two comprehensive stroke centers. The primary outcome was unsuccessful reperfusion defined by a modified thrombolysis in cerebral infarction (mTICI) score of 0-2a. We quantitatively assessed the hyperdense vessel sign by measuring Hounsfield units (HU) on admission computed tomography (CT). Receiver operating characteristic (ROC) curves were plotted to estimate the predictive value of quantitative hyperdense middle cerebral artery (MCA) measurements (delta and ratio) and of the final model for mTICI scores. We performed multivariable logistic regression to analyze associations with outcomes.
Out of 348 patients 87 had unsuccessful MT. Smoking, difficult arch, vessel tortuosity, vessel calcification, diminutive vessels, truncal M1 occlusion, delta HU and HU ratio were significantly associated with unsuccessful MT in the univariate analysis. When we fitted two separate multivariate models including all significant variables and a HU measurement; delta HU <6 (odds ratio, OR = 2.07, 95% confidence intervals, CI 1.09-3.92) and HU ratio ≤1.1 (OR = 2.003, 95% CI 1.05-3.81) were independently associated with failed MT after adjustment for smoking, diminutive vessels, vessel tortuosity, and difficult arch. The area under the curve AUC<9 of the final model was 0.717.
Novel radiological biomarkers on CT, CT angiography (CTA) and digital subtraction angiography (DSA) may help identify patients refractory to standard MT and prepare interventionalists for using additional alternative methods. Quantitative assessment of HU (delta and ratio) may be important in developing objective prediction tools for unsuccessful MT.
尽管机械取栓(MT)技术有所进步,但 10-30%的大血管闭塞(LVO)MT 治疗仍不成功。目前的预测模型未能解决患者特定因素与再灌注之间的关系。我们旨在评估客观、易于复制的入院临床和影像学生物标志物,以预测 MT 治疗的失败。
我们分析了两个综合卒中中心连续的前循环 LVO MT 患者。主要结局为改良脑梗死溶栓(mTICI)评分 0-2a 的再灌注失败。我们通过测量入院计算机断层扫描(CT)的亨氏单位(HU)来定量评估高密度血管征。绘制受试者工作特征(ROC)曲线,以评估定量测量高密度大脑中动脉(MCA)的预测值(差值和比值)以及最终模型对 mTICI 评分的预测价值。我们进行了多变量逻辑回归分析,以分析与结果的相关性。
在 348 例患者中,87 例 MT 治疗失败。在单变量分析中,吸烟、弓部困难、血管迂曲、血管钙化、血管细小、M1 干闭塞、HU 差值和 HU 比值与 MT 治疗失败显著相关。当我们分别拟合包括所有显著变量和 HU 测量值的两个多变量模型时;HU 差值<6(优势比,OR=2.07,95%置信区间,CI 1.09-3.92)和 HU 比值≤1.1(OR=2.003,95% CI 1.05-3.81)在调整吸烟、血管细小、血管迂曲和弓部困难后,与 MT 治疗失败独立相关。最终模型的曲线下面积 AUC<9 为 0.717。
CT、CT 血管造影(CTA)和数字减影血管造影(DSA)上的新影像学生物标志物可能有助于识别对标准 MT 治疗有抗性的患者,并为介入医生准备使用额外的替代方法。HU(差值和比值)的定量评估可能对开发 MT 治疗失败的客观预测工具很重要。