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首页> 外文期刊>The European Journal of Neuroscience >Individualized quantification of the benefit from reperfusion therapy using stroke predictive models
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Individualized quantification of the benefit from reperfusion therapy using stroke predictive models

机译：使用中风预测模型的补充治疗的个性化定量益处

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Purpose Recent imaging developments have shown the potential of voxel-based models in assessing infarct growth after stroke. Many models have been proposed but their relevance in predicting the benefit of a reperfusion therapy remains unclear. We searched for a predictive model whose volumetric predictions would identify stroke patients who are to benefit from tissue plasminogen activator (t-PA)-induced reperfusion. Material and Methods Forty-five cases were used to study retrospectively stroke progression from admission to end of follow-up. Predictive approaches based on various statistical models, predictive variables and spatial filtering methods were compared. The optimal approach was chosen according to the area under the precision-recall curve (AUPRC). The final lesion volume was then predicted assuming that the patient would or would not reperfuse. Patients, with an acute lesion of 6 ml and >25% of the acute lesion, were classified as responders. Results The optimal model was a logistic regression using the voxel distance to the acute lesion, the volume of the acute lesion and Gaussian-filtered MRI contrast parameters as predictive variables. The predictions gave a median AUPRC of 0.655, a median AUC of 0.976 and a median volumetric error of 8.29 ml. Nineteen patients matched the responder profile. A non-significant trend of improved reduction in NIHSS score (-42.8%, p = .09) and in lesion volume (-78.1%, p = 0.21) following reperfusion was observed for responder patients. Conclusion Despite limited volumetric accuracy, predictive stroke models can be used to quantify the benefit of reperfusion therapies.
机译：目的最近的成像开发表明了基于体素的模型评估中风后梗塞生长的潜力。已经提出了许多模型，但它们在预测再灌注治疗的益处时的相关性仍不清楚。我们搜索了一种预测模型，其体积预测将识别从组织纤溶酶原激活剂（T-PA）诱导的再灌注中受益的中风患者。材料和方法45例案件用于研究回顾性中风进入进入随访结束。比较了基于各种统计模型，预测变量和空间滤波方法的预测方法。根据精密召回曲线（AUPRC）下的区域选择最佳方法。然后假设患者将不会再使用，因此预测最终病变量。患者，急性病变为6毫升和> 25％的急性病变，被归类为响应者。结果最佳模型是使用逆素距离的逻辑回归到急性病变，急性病变的体积和高斯过滤的MRI对比参数作为预测变量。预测给出了0.655的中值AUPRC，0.976的中值AUC和8.29毫升的中值体积误差。 19名患者匹配了响应者的概况。对于响应者患者，观察到再灌注后，NIHSS得分降低（-42.8％，p = .09）和病变体积（-78.1％，p = 0.21）的非显着趋势。结论尽管有限的体积精度，可用于量化的预测卒中模型来量化再灌注疗法的益处。

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