Standardized EEG analysis to reduce the uncertainty of outcome prognostication after cardiac arrest
Standardized EEG analysis to reduce the uncertainty of outcome prognostication after cardiac arrest
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DOI：
10.1007/s00134-019-05921-6
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发表时间：
2020-02-03
影响因子：
38.9
通讯作者：
Oddo, Mauro
中科院分区：
文献类型：
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作者：
Bongiovanni, Filippo;Romagnosi, Federico;Oddo, Mauro
目的复苏后指南推荐了一种多模式算法用于心脏骤停（CA）后的结局预测。我们的目的是评估应用该算法后不确定预后的患病率，并提供一种改善该人群预后的策略。方法我们研究了一个前瞻性队列的昏迷CA患者（n = 485）中的ERC/ESICM算法的应用。在预后不确定的患者中，采用标准化EEG分类（良性、恶性、高度恶性）和血清神经元特异性烯醇化酶（NSE）进行诊断研究。3个月时的神经功能恢复被分为良好（脑功能分类[CPC] 1-2）和差（CPC 3-5）。结果使用ERC/ESICM算法，155例（32%）患者预后不良;所有患者均在3个月时死亡。在其余330例（68%）结局不确定的患者中，大多数（212/330; 64%）恢复良好。在该患者亚组中，在第3天没有高度恶性EEG时，良好恢复的敏感性为99.5 [97.4-99.9] %，这比NSE < 33 μ g/L更上级（单独使用时为84.9 [79.3-89.4] %;与EEG联合使用时为84.4 [78.8-89] %，均p < 0.001）。高度恶性EEG的特异性相同（99.5 [97.4-99.9] %），但敏感性高于NSE恢复不良。进一步分析结果预测因素的辨别力，发现NSE相对于EEG的价值有限。结论在大多数昏迷CA患者中，应用ERC/ESICM分类算法后，结果仍不确定。标准化的EEG背景分析能够准确预测良好和不良恢复，从而大大降低了该患者人群中昏迷发生的不确定性。
Purpose Post-resuscitation guidelines recommend a multimodal algorithm for outcome prediction after cardiac arrest (CA). We aimed at evaluating the prevalence of indeterminate prognosis after application of this algorithm and providing a strategy for improving prognostication in this population. Methods We examined a prospective cohort of comatose CA patients (n = 485) in whom the ERC/ESICM algorithm was applied. In patients with an indeterminate outcome, prognostication was investigated using standardized EEG classification (benign, malignant, highly malignant) and serum neuron-specific enolase (NSE). Neurological recovery at 3 months was dichotomized as good (Cerebral Performance Categories [CPC] 1-2) vs. poor (CPC 3-5). Results Using the ERC/ESICM algorithm, 155 (32%) patients were prognosticated with poor outcome; all died at 3 months. Among the remaining 330 (68%) patients with an indeterminate outcome, the majority (212/330; 64%) showed good recovery. In this patient subgroup, absence of a highly malignant EEG by day 3 had 99.5 [97.4-99.9] % sensitivity for good recovery, which was superior to NSE < 33 mu g/L (84.9 [79.3-89.4] % when used alone; 84.4 [78.8-89] % when combined with EEG, both p < 0.001). Highly malignant EEG had equal specificity (99.5 [97.4-99.9] %) but higher sensitivity than NSE for poor recovery. Further analysis of the discriminative power of outcome predictors revealed limited value of NSE over EEG. Conclusions In the majority of comatose CA patients, the outcome remains indeterminate after application of ERC/ESICM prognostication algorithm. Standardized EEG background analysis enables accurate prediction of both good and poor recovery, thereby greatly reducing uncertainty about coma prognostication in this patient population.