Department of Neurology, Taipei Medical University - Shuang Ho Hospital, New Taipei, Taiwan.
Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Sci Rep. 2020 Nov 25;10(1):20501. doi: 10.1038/s41598-020-77546-5.
Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018. The ANN models were generated using eight clinical inputs and two outputs. The generalizability of the model was validated using fivefold cross-validation. The performance of each model was assessed according to the accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). After adequate training, the ANN predictive model AUC for sICH was 0.941, with accuracy, sensitivity, and specificity of 91.0%, 85.7%, and 92.5%, respectively. The predictive model AUC for 3-month mortality was 0.976, with accuracy, sensitivity, and specificity of 95.2%, 94.4%, and 95.5%, respectively. The generated ANN-based models exhibited high predictive performance and reliability for predicting sICH and 3-month mortality after thrombolysis; thus, its clinical application to assist decision-making when administering tPA is envisaged.
尽管静脉注射组织型纤溶酶原激活剂（tPA）有明显的益处，但症状性颅内出血（sICH）仍然是一种常见的并发症，也是治疗急性缺血性脑卒中（AIS）时的主要关注点。本研究探讨了使用基于人工神经网络（ANN）的模型来预测接受 tPA 治疗的 AIS 患者的 sICH 和 3 个月死亡率。我们在 2009 年至 2018 年间的 331 名患者队列中，基于与 sICH 和死亡率相关的治疗前参数的预测价值评估，开发了 ANN 模型。ANN 模型使用 8 个临床输入和 2 个输出生成。使用五重交叉验证验证了模型的泛化能力。根据准确性、精密度、敏感性、特异性和接收器操作特征曲线（AUC）下的面积评估每个模型的性能。经过充分的训练，sICH 的 ANN 预测模型 AUC 为 0.941，准确性、敏感性和特异性分别为 91.0%、85.7%和 92.5%。3 个月死亡率的预测模型 AUC 为 0.976，准确性、敏感性和特异性分别为 95.2%、94.4%和 95.5%。生成的基于 ANN 的模型对预测溶栓后 sICH 和 3 个月死亡率具有较高的预测性能和可靠性；因此，预计其在协助 tPA 给药决策时的临床应用。
