Purpose

This study aims to analyze the multimodal score of the regional pial collateral in predicting hemorrhagic transformation (HT) after mechanical thrombectomy in acute ischemic stroke (AIS).

Methods

On the basis of different brain regions and multiphase computed tomography angiography (mCTA), we evaluated the pial arterial filling status in extent, delay, and contrast washout. The prediction models of HT and symptomatic intracerebral hemorrhage (sICH) were established using mCTA (model-H1 and model-S1), CT perfusion (CTP, model-H2 and model-S2), and comprehensive parameters (model-H3 and model-S3). The receiver operating characteristic curve was used to analyze the prediction performance of each model.

Results

Among the 102 patients with AIS who received thrombectomy, 36 (35.3%) developed HT, and 15 (14.7%) of whom had sICH. In model-H1 and model-S1, washout independently influenced HT (OR, 95%CI 0.398, 0.249–0.634) and sICH (OR, 95%CI 0.552, 0.342–0.892). In model-H2, the relative surface permeability independently influenced HT (OR, 95%CI 1.217, 1.082–1.370). Model-H3 and model-S3 improved the prediction performance (areas under the curve: HT, 0.957; sICH, 0.938). The correlation coefficients between relative cerebral blood volume and the three modes of pial arterial filling status were higher than those of other CTP parameters. The 90-day modified Rankin scale score in the sICH group was significantly increased (P < 0.05).

Conclusion

The multimodal regional pial collateral score has good value in the risk assessment of HT and sICH in patients with AIS after mechanical thrombectomy. The combination of multiple parameters can improve diagnostic performance.

中文翻译：

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预测急性缺血性卒中取栓后出血性转化：区域软脑膜侧支的多模式评分

目的

本研究旨在分析区域软膜侧支的多模态评分在预测急性缺血性卒中 (AIS) 机械取栓术后出血性转化 (HT) 中的作用。

方法

在不同脑区和多期计算机断层血管造影（mCTA）的基础上，我们评估了软脑膜动脉充盈状态的范围、延迟和对比剂冲洗。利用mCTA（模型-H1和模型-S1）、CT灌注（CTP、模型-H2和模型-S2）和综合参数（模型-H3和模型-）建立了HT和症状性脑出血（sICH）的预测模型。 S3)。接收者操作特征曲线用于分析每个模型的预测性能。

结果

在接受血栓切除术的 102 例 AIS 患者中，36 例（35.3%）发生 HT，其中 15 例（14.7%）发生 sICH。在模型-H1 和模型-S1 中，洗脱独立影响 HT (OR, 95%CI 0.398, 0.249–0.634) 和 sICH (OR, 95%CI 0.552, 0.342–0.892)。在模型 H2 中，相对表面渗透率独立影响 HT（OR，95%CI 1.217, 1.082–1.370）。Model-H3 和 Model-S3 提高了预测性能（曲线下面积：HT，0.957；sICH，0.938）。相对脑血容量与软脑膜动脉充盈状态三种模式的相关系数均高于其他CTP参数。sICH组90天改良Rankin量表评分显着升高（P  <0.05）。

结论

多模式区域软脑膜侧支评分在机械取栓后AIS患者HT和sICH风险评估中具有良好价值。多个参数的组合可以提高诊断性能。