自动 DWI 分析可识别出在 4.5 小时溶栓时间窗内的患者。
Automated DWI analysis can identify patients within the thrombolysis time window of 4.5 hours.
机构信息
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.
出版信息
Neurology. 2018 May 1;90(18):e1570-e1577. doi: 10.1212/WNL.0000000000005413. Epub 2018 Apr 4.
OBJECTIVE
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.
METHODS
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.
RESULTS
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).
CONCLUSIONS
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.
目的
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。
方法
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。
结果
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）。
结论
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。
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