Bivard Andrew, Levi Christopher, Lin Longting, Cheng Xin, Aviv Richard, Spratt Neil J, Kleinig Tim, Butcher Kenneth, Chen Chushuang, Dong Qiang, Parsons Mark
Melbourne Brain Centre, Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia.
Hunter Medical Research Institute, University of Newcastle, Newcastle, NSW, Australia.
Front Neurol. 2021 Sep 9;12:736768. doi: 10.3389/fneur.2021.736768. eCollection 2021.
In the present study we sought to measure the relative statistical value of various multimodal CT protocols at identifying treatment responsiveness in patients being considered for thrombolysis. We used a prospectively collected cohort of acute ischemic stroke patients being assessed for IV-alteplase, who had CT-perfusion (CTP) and CT-angiography (CTA) before a treatment decision. Linear regression and receiver operator characteristic curve analysis were performed to measure the prognostic value of models incorporating each imaging modality. One thousand five hundred and sixty-two sub-4.5 h ischemic stroke patients were included in this study. A model including clinical variables, alteplase treatment, and NCCT ASPECTS was weak ( 0.067, < 0.001, AUC 0.605) at predicting 90 day mRS. A second model, including dynamic CTA variables (collateral grade, occlusion severity) showed better predictive accuracy for patient outcome ( 0.381, < 0.001, AUC 0.781). A third model incorporating CTP variables showed very high predictive accuracy ( 0.488, < 0.001, AUC 0.899). Combining all three imaging modalities variables also showed good predictive accuracy for outcome but did not improve on the CTP model ( 0.439, < 0.001, AUC 0.825). CT perfusion predicts patient outcomes from alteplase therapy more accurately than models incorporating NCCT and/or CT angiography. This data has implications for artificial intelligence or machine learning models.
在本研究中，我们试图测量各种多模态CT方案在识别拟进行溶栓治疗患者的治疗反应性方面的相对统计值。我们使用了一个前瞻性收集的急性缺血性中风患者队列，这些患者正在接受静脉注射阿替普酶评估，在做出治疗决定前进行了CT灌注（CTP）和CT血管造影（CTA）检查。进行线性回归和受试者操作特征曲线分析，以测量纳入每种成像模态的模型的预后价值。本研究纳入了1562例发病4.5小时以内的缺血性中风患者。一个包含临床变量、阿替普酶治疗和非增强CT（NCCT）的脑缺血早期CT评分（ASPECTS）的模型在预测90天改良Rankin量表（mRS）时表现较弱（β = 0.067，P < 0.001，曲线下面积[AUC] = 0.605）。第二个模型，包括动态CTA变量（侧支循环分级、闭塞严重程度），对患者预后显示出更好的预测准确性（β = 0.381，P < 0.001，AUC = 0.781）。第三个纳入CTP变量的模型显示出非常高的预测准确性（β = 0.488，P < 0.001，AUC = 0.899）。将所有三种成像模态变量结合起来对预后也显示出良好的预测准确性，但并未优于CTP模型（β = 0.439，P < 0.001，AUC = 0.825）。CT灌注比包含NCCT和/或CT血管造影的模型更准确地预测阿替普酶治疗的患者预后。该数据对人工智能或机器学习模型具有启示意义。
