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Predicting experimental success: A retrospective case-control study using the rat intraluminal thread model of stroke.
Disease Models & Mechanisms ( IF 4.0 ) Pub Date : 2020-10-22 , DOI: 10.1242/dmm.044651
Lisa Liebenstund 1 , Mark Coburn 1 , Christina Fitzner 1, 2 , Antje Willuweit 3 , Karl-Josef Langen 3, 4 , Jingjin Liu 1 , Michael Veldeman 5 , Anke Höllig 6
Affiliation  

The poor translational success rate of preclinical stroke research may partly be due to inaccurate modelling of the disease. We provide data on transient middle cerebral artery occlusion (tMCAO) experiments including detailed intraoperative monitoring to elaborate predictors indicating experimental success (ischemia without occurrence of confounding pathologies).The tMCAO monitoring data (bilateral cerebral blood flow - CBF, heart rate - HR, mean arterial pressure - MAP) of 16 animals with an "ideal" outcome (MCA-ischemia) and 48 animals with additional or other pathologies (subdural hematoma or subarachnoid hemorrhage) were checked for their prognostic performance (receiver operating characteristic curve and area under the curve - AUC).Animals showing a decrease in the contralateral CBF at the time of MCA occlusion suffered from unintended pathologies. Implementation of baseline MAP in addition to baseline HR (AUC 0.83, 95% CI 0.68 to 0.97) increased prognostic relevance (AUC 0.89, 95% CI 0.79 to 0.98). Prediction performance improved when two additional predictors referring differences in left and right CBF were considered (AUC 1.00, 95% CI 1.0 to 1.0).Our data underline the importance of periinterventional monitoring to verify a successful experimental performance in order to ensure a disease model as homogeneous as possible.

中文翻译:


预测实验成功:使用大鼠中风腔内线模型进行的回顾性病例对照研究。



临床前卒中研究的转化成功率较差,部分原因可能是对该疾病的建模不准确。我们提供短暂性大脑中动脉闭塞 (tMCAO) 实验的数据,包括详细的术中监测,以详细说明实验成功的预测因素(缺血而不发生混杂病理)。tMCAO 监测数据(双侧脑血流量 - CBF、心率 - HR、平均检查 16 只具有“理想”结果(MCA 缺血)的动物和 48 只患有额外或其他病变(硬膜下血肿或蛛网膜下腔出血)的动物的动脉压 - MAP),检查其预后表现(接受者工作特征曲线和曲线下面积) - AUC)。在 MCA 闭塞时表现出对侧 CBF 减少的动物患有意外的病变。除基线 HR(AUC 0.83,95% CI 0.68 至 0.97)外,实施基线 MAP 可增加预后相关性(AUC 0.89,95% CI 0.79 至 0.98)。当考虑左右 CBF 差异的两个附加预测因子时,预测性能得到改善(AUC 1.00,95% CI 1.0 至 1.0)。我们的数据强调了围介入监测的重要性,以验证成功的实验性能,以确保疾病模型符合尽可能同质。
更新日期:2020-10-27
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