Department of Intensive Care Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.
Department of Anesthesiology and Intensive Care Medicine, Catholic University of The Sacred Heart, Rome, Italy.
Intensive Care Med. 2020 May;46(5):963-972. doi: 10.1007/s00134-019-05921-6. Epub 2020 Feb 3.
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.
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).
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 μg/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.
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.
心脏骤停（CA）后复苏指南推荐使用多模态算法进行预后预测。我们旨在评估应用该算法后不确定预后的发生率，并为该人群的预后提供策略。
我们检查了昏迷性 CA 患者的前瞻性队列（n=485），其中应用了 ERC/ESICM 算法。在预后不确定的患者中，使用标准化脑电图分类（良性、恶性、高度恶性）和血清神经元特异性烯醇化酶（NSE）进行预后评估。3 个月时的神经恢复分为良好（Cerebral Performance Categories [CPC] 1-2）和不良（CPC 3-5）。
使用 ERC/ESICM 算法，155 例（32%）患者预后不良；所有患者在 3 个月时死亡。在其余 330 例（68%）预后不确定的患者中，大多数（212/330；64%）恢复良好。在该患者亚组中，第 3 天无高度恶性脑电图的患者，其良好恢复的灵敏度为 99.5%[97.4-99.9]%，优于 NSE<33μg/L（单独使用时为 84.9%[79.3-89.4]%；与 EEG 联合使用时为 84.4%[78.8-89.0]%，均 p<0.001）。高度恶性脑电图的特异性相同（99.5%[97.4-99.9]%），但对不良恢复的灵敏度高于 NSE。对预后预测因子的判别能力进一步分析显示，NSE 优于 EEG 的价值有限。
在大多数昏迷性 CA 患者中，应用 ERC/ESICM 预后算法后，其预后仍然不确定。标准化脑电图背景分析能够准确预测良好和不良恢复，从而大大减少了该患者人群中昏迷预后的不确定性。
