Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan; Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, Taiwan; Division of Critical Care Medicine, Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan.
Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan; Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
J Formos Med Assoc. 2022 Feb;121(2):490-499. doi: 10.1016/j.jfma.2021.07.004. Epub 2021 Jul 28.
To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN).
The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome. ANN were applied for developing outcome prediction models. The generalizability of the models was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).
The parameters associated with survival were age, duration of cardiopulmonary resuscitation, history of diabetes mellitus (DM), heart failure, end-stage renal disease (ESRD), systolic blood pressure (BP), diastolic BP, body temperature, motor response after ROSC, emergent coronary angiography or percutaneous coronary intervention (PCI), and the cooling methods. The parameters associated with the favorable neurologic outcomes were age, sex, DM, chronic obstructive pulmonary disease, ESRD, stroke, pre-arrest cerebral-performance category, BP, body temperature, motor response after ROSC, emergent coronary angiography or PCI, and cooling methods. After adequate training, ANN Model 1 to predict survival achieved an AUC of 0.80. Accuracy, sensitivity, and specificity were 75.9%, 71.6%, and 79.3%, respectively. ANN Model 4 to predict the favorable neurologic outcome achieved an AUC of 0.87, with accuracy, sensitivity, and specificity of 86.7%, 77.7%, and 88.0%, respectively.
The ANN-based models achieved good performance to predict the survival and favorable neurologic outcomes after TTM. The models proposed have clinical value to assist in decision-making.
通过人工神经网络（ANN）确定接受目标温度管理（TTM）的患者的预后相关预测因子并建立预测模型。
从 2014 年 1 月至 2019 年 8 月接受 TTM 治疗的心脏骤停并恢复自主循环（ROSC）的 580 例患者中得出本队列。我们评估了与生存和良好神经功能结局相关的参数的预测价值。应用 ANN 建立预后预测模型。通过 5 折交叉验证评估模型的泛化能力。根据准确性、敏感性、特异性和接受者操作特征曲线（ROC）下面积（AUC）评估模型的性能。
与生存相关的参数为年龄、心肺复苏持续时间、糖尿病（DM）史、心力衰竭、终末期肾病（ESRD）、收缩压（BP）、舒张压、体温、ROSC 后的运动反应、紧急冠状动脉造影或经皮冠状动脉介入治疗（PCI）以及冷却方法。与良好神经结局相关的参数为年龄、性别、DM、慢性阻塞性肺疾病、ESRD、中风、发病前脑功能状态、BP、体温、ROSC 后的运动反应、紧急冠状动脉造影或 PCI 以及冷却方法。在经过充分的训练后，ANN 模型 1 预测生存的 AUC 为 0.80。准确性、敏感性和特异性分别为 75.9%、71.6%和 79.3%。ANN 模型 4 预测良好神经结局的 AUC 为 0.87，准确性、敏感性和特异性分别为 86.7%、77.7%和 88.0%。
基于 ANN 的模型在预测 TTM 后的生存和良好神经结局方面表现出良好的性能。所提出的模型具有临床价值，可以辅助决策。
