Multimodal approach for neurologic prognostication of out-of-hospital cardiac arrest patients undergoing targeted temperature management
Multimodal approach for neurologic prognostication of out-of-hospital cardiac arrest patients undergoing targeted temperature management
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DOI：
10.1016/j.resuscitation.2018.11.007
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发表时间：
2019-01-01
期刊：
影响因子：
6.5
通讯作者：
Chung, Sung Phil
中科院分区：
文献类型：
--
作者：
Kim, Ji Hoon;Kim, Min Joung;Chung, Sung Phil
目的：自从引入目标温度管理（TTM）以来，心脏骤停后患者预后测试的准确性和时间已经发生了变化。虽然以前的研究已经证明了多模式方法在评估TTM患者预后方面的有效性，但很少有研究调查了顺序结合不同预后模式的优化策略。本研究确定了一个最佳的顺序组合的预后模式，以预测不良的神经功能的结果，患者接受TTM。方法：我们进行了一项回顾性分析，使用TTM管理登记数据。所有患者在固定的时间进行了相同的预后测试序列。序列包括脑计算机断层扫描（CT）、血清神经元特异性烯醇化酶（NSE）、电生理检查、神经系统检查和弥散加权成像。我们使用分层分类和回归树分析来寻找最佳的预后模型。主要措施是心脏骤停后一个月的神经功能预后不良。结果：共纳入192例患者，其中103例（53.6%）神经功能预后不良。最终模型包括脑CT、血清NSE、脑电图、体感诱发电位和瞳孔对光反射。我们的模型预测结果不佳，假阳性率为0%。此外，我们的模型具有0.911的受试者工作特征曲线下面积值（95%可信区间，0.872-0.950），显著高于单独的每种预后模式。我们的逐步模型显示了良好的预后能力，可以预测心脏骤停后一个月的不良预后，并可用于最大限度地减少假悲观预测的风险，接受TTM的患者。
Aim: Since the introduction of targeted temperature management (TTM), the accuracy and timing of prognostic tests for post-cardiac arrest patients have changed. Although previous studies have demonstrated the effectiveness of a multimodal approach in assessing the prognosis of TTM patients, few studies have investigated an optimised strategy that sequentially combines different prognostic modalities. This study identified an optimal sequential combination of prognostic modalities to predict poor neurologic outcomes in patients undergoing TTM.Methods: We performed a retrospective analysis using TTM management registry data. All patients underwent an identical sequence of prognostic tests at fixed timings. The sequence included brain computed tomography (CT), serum neuron-specific enolase (NSE), electrophysiological examination, neurologic examination, and diffusion-weighted imaging. We used hierarchical classification and regression tree analysis to find the optimal prognostic model. The primary measure was a poor neurologic outcome at one month after cardiac arrest.Results: A total of 192 patients were included and 103 patients (53.6%) had poor neurologic outcomes. The final model consisted of brain CT, serum NSE, electroencephalogram, somatosensory-evoked potentials, and pupil light reflex. Our model predicted poor outcomes with a 0% false positive rate. Moreover, our model had an area under the receiver operating characteristic curve value of 0.911 (95% confidence interval, 0.872-0.950), which was significantly higher than that of each prognostic modality alone.Conclusions: Our stepwise model showed excellent prognostic ability to predict poor outcomes at one month after cardiac arrest and may be used to minimise the risk of false pessimistic predictions in patients undergoing TTM.