Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan, ROC.
Ph.D. Program for Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University and National Health Research Institutes, Taipei, Taiwan, ROC.
Sci Rep. 2022 May 4;12(1):7254. doi: 10.1038/s41598-022-11201-z.
Existing prognostic models to predict the neurological recovery in patients with cardiac arrest receiving targeted temperature management (TTM) either exhibit moderate accuracy or are too complicated for clinical application. This necessitates the development of a simple and generalizable prediction model to inform clinical decision-making for patients receiving TTM. The present study explores the predictive validity of the Cardiac Arrest Survival Post-resuscitation In-hospital (CASPRI) score in cardiac arrest patients receiving TTM, regardless of cardiac event location, and uses artificial neural network (ANN) algorithms to boost the prediction performance. This retrospective observational study evaluated the prognostic relevance of the CASPRI score and applied ANN to develop outcome prediction models in a cohort of 570 patients with cardiac arrest and treated with TTM between 2014 and 2019 in a nationwide multicenter registry in Taiwan. In univariate logistic regression analysis, the CASPRI score was significantly associated with neurological outcome, with the area under the receiver operating characteristics curve (AUC) of 0.811. The generated ANN model, based on 10 items of the CASPRI score, achieved a training AUC of 0.976 and validation AUC of 0.921, with the accuracy, precision, sensitivity, and specificity of 89.2%, 91.6%, 87.6%, and 91.2%, respectively, for the validation set. CASPRI score has prognostic relevance in patients who received TTM after cardiac arrest. The generated ANN-boosted, CASPRI-based model exhibited good performance for predicting TTM neurological outcome, thus, we propose its clinical application to improve outcome prediction, facilitate decision-making, and formulate individualized therapeutic plans for patients receiving TTM.
现有的预测模型，用于预测接受目标温度管理（TTM）的心脏骤停患者的神经恢复，要么表现出中等准确性，要么过于复杂而不适合临床应用。因此，需要开发一种简单且可推广的预测模型，为接受 TTM 的患者提供临床决策依据。本研究探讨了心脏骤停复苏后住院期间（CASPRI）评分在接受 TTM 的心脏骤停患者中的预测有效性，无论心脏事件的位置如何，并使用人工神经网络（ANN）算法来提高预测性能。这项回顾性观察性研究评估了 CASPRI 评分的预后相关性，并在台湾一个全国多中心注册中心的一个队列中，应用 ANN 开发了一个预后预测模型，该队列包括 2014 年至 2019 年间接受 TTM 治疗的 570 例心脏骤停患者。在单变量逻辑回归分析中，CASPRI 评分与神经结局显著相关，受试者工作特征曲线下面积（AUC）为 0.811。基于 CASPRI 评分的 10 项指标生成的 ANN 模型，其训练 AUC 为 0.976，验证 AUC 为 0.921，验证集的准确率、精度、敏感度和特异度分别为 89.2%、91.6%、87.6%和 91.2%。CASPRI 评分对接受 TTM 后心脏骤停的患者具有预后相关性。生成的基于 ANN 增强的 CASPRI 模型对预测 TTM 神经结局具有良好的性能，因此，我们建议将其临床应用于改善预后预测、辅助决策制定和为接受 TTM 的患者制定个体化治疗计划。
