From the Departments of Neurosciences (A.W., R. Lemmens), Cognitive Neurology (P.D.), and Electrical Engineering (D.R.), KU Leuven, University of Leuven; VIB Center for Brain & Disease Research (A.W., R. Lemmens); Department of Neurology (A.W., R. Lemmens), University Hospitals Leuven, Leuven, Belgium; Department of Neurology (B.C., G.T.), University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Stanford Stroke Center (S.C., G.W.A.), Stanford University Medical Center, Palo Alto, CA; Department of Neurology (B.N.), Lund University, Sweden; Guided Development GmbH (R. Laage), Heidelberg, Germany; and Florey Institute of Neuroscience and Mental Health (V.N.T.), Heidelberg, Australia.
Neurology. 2018 May 1;90(18):e1570-e1577. doi: 10.1212/WNL.0000000000005413. Epub 2018 Apr 4.
To develop an automated model based on diffusion-weighted imaging (DWI) to detect patients within 4.5 hours after stroke onset and compare this method to the visual DWI-FLAIR (fluid-attenuated inversion recovery) mismatch.
We performed a subanalysis of the "DWI-FLAIR mismatch for the identification of patients with acute ischemic stroke within 4.5 hours of symptom onset" (PRE-FLAIR) and the "AX200 for ischemic stroke" (AXIS 2) trials. We developed a prediction model with data from the PRE-FLAIR study by backward logistic regression with the 4.5-hour time window as dependent variable and the following explanatory variables: age and median relative DWI (rDWI) signal intensity, interquartile range (IQR) rDWI signal intensity, and volume of the core. We obtained the accuracy of the model to predict the 4.5-hour time window and validated our findings in an independent cohort from the AXIS 2 trial. We compared the receiver operating characteristic curve to the visual DWI-FLAIR mismatch.
In the derivation cohort of 118 patients, we retained the IQR rDWI as explanatory variable. A threshold of 0.39 was most optimal in selecting patients within 4.5 hours after stroke onset resulting in a sensitivity of 76% and specificity of 63%. The accuracy was validated in an independent cohort of 200 patients. The predictive value of the area under the curve of 0.72 (95% confidence interval 0.64-0.80) was similar to the visual DWI-FLAIR mismatch (area under the curve = 0.65; 95% confidence interval 0.58-0.72; for difference = 0.18).
An automated analysis of DWI performs at least as good as the visual DWI-FLAIR mismatch in selecting patients within the 4.5-hour time window.
开发一种基于弥散加权成像（DWI）的自动模型，以检测发病后 4.5 小时内的患者，并将该方法与视觉 DWI-FLAIR（液体衰减反转恢复）不匹配进行比较。
我们对“DWI-FLAIR 不匹配用于识别发病后 4.5 小时内的急性缺血性卒中患者”（PRE-FLAIR）和“AX200 缺血性卒中”（AXIS 2）试验进行了亚组分析。我们通过向后逻辑回归，将 4.5 小时时间窗作为因变量，年龄和中位数相对 DWI（rDWI）信号强度、rDWI 信号强度四分位距（IQR）和核心体积作为解释变量，在 PRE-FLAIR 研究数据中建立预测模型。我们在 AXIS 2 试验的独立队列中验证了模型预测 4.5 小时时间窗的准确性。我们比较了受试者工作特征曲线与视觉 DWI-FLAIR 不匹配。
在 118 例患者的推导队列中，我们保留了 IQR rDWI 作为解释变量。选择发病后 4.5 小时内的患者的最佳阈值为 0.39，其敏感性为 76%，特异性为 63%。在 200 例患者的独立队列中验证了准确性。曲线下面积为 0.72（95%置信区间 0.64-0.80）的预测值与视觉 DWI-FLAIR 不匹配相似（曲线下面积=0.65；95%置信区间 0.58-0.72；差异=0.18）。
与视觉 DWI-FLAIR 不匹配相比，DWI 的自动分析在选择 4.5 小时时间窗内的患者方面至少同样有效。
