Department of Cardiac Sciences, Division of Cardiac Surgery, Kelowna General Hospital, Kelowna, British Columbia, Canada.
Department of Emergency Medicine, University of British Columbia, New Westminster, British Columbia, Canada.
Resuscitation. 2018 Jun;127:51-57. doi: 10.1016/j.resuscitation.2018.03.028. Epub 2018 Mar 23.
Extra-corporeal life support (ECLS) is a life-saving intervention for patients with hypothermia induced cardiac arrest or severe cardiovascular instability. However, its application is highly variable due to a paucity of data in the literature to guide practice. Current guidelines and recommendations are based on expert opinion, single case reports, and small case series. Combining all of the published data in a patient-level analysis can provide a robust assessment of the influence of patient characteristics on survival with ECLS.
To develop a prediction model of survival with good neurologic outcome for accidental hypothermia treated with ECLS.
Electronic searches of PubMed, EMBASE, CINAHL were conducted with a hand search of reference lists and major surgical and critical care conference abstracts. Studies had to report the use of ECLS configured with a circuit, blood pump and oxygenator with an integrated heat exchanger. Randomized and observational studies were eligible for inclusion. Non-human, non-English and review manuscripts were deemed ineligible. Study authors were requested to submit patient level data when aggregate or incomplete individual patient data was provided in a study. Survival with good neurologic outcome was categorized for patients to last follow-up based on the reported scores on the Cerebral Performance Category (1 or 2), Glasgow Outcome Scale (4 or 5) and Pediatric Overall Performance Category (1 or 2). A one-stage, individual patient data meta-analysis was performed with a mixed-effects multi-level logistic regression model reporting odds ratio (OR) with a 95% confidence interval (CI).
Data from 44 observational studies and 40 case reports (n = 658) were combined and analyzed to identify independent predictors of survival with good neurologic outcome. The survival rate with good neurologic outcome of the entire cohort was 40.3% (265 of 658). ECLS rewarming rate (OR: 0.93; 95% CI: 0.88, 0.98; p = .007), female gender (OR: 2.78; 95% CI: 1.69, 4.58; p < 0.001), asphyxiation (OR: 0.19; 95% CI: 0.11, 0.35; p < 0.001) and serum potassium (OR: 0.62; 95% CI: 0.53, 0.73; p < 0.001) were associated with survival with a good neurologic outcome. The logistic regression model demonstrated excellent discrimination (c-statistic: 0.849; 95% CI: 0.823, 0.875).
The use of extracorporeal life support in the treatment of hypothermic cardiac arrest provides a favourable chance of survival with good neurologic outcome. When used in a weighted scoring system, asphyxiation, serum potassium and gender can help clinicians prognosticate the benefit of resuscitating hypothermic patients with ECLS.
体外生命支持 (ECLS) 是治疗低温诱导心搏骤停或严重心血管不稳定患者的救生干预措施。然而，由于文献中缺乏数据来指导实践，其应用存在很大的差异。目前的指南和建议基于专家意见、单一病例报告和小病例系列。对所有已发表的数据进行患者水平分析，可以对患者特征对 ECLS 生存的影响进行稳健评估。
为接受 ECLS 治疗的意外低温患者制定具有良好神经结局的生存预测模型。
对 PubMed、EMBASE 和 CINAHL 进行电子检索，并对主要外科和重症监护会议摘要进行手工检索。研究必须报告使用配置有回路、血液泵和带有集成热交换器的氧气机的 ECLS。随机和观察性研究有资格纳入。非人类、非英语和综述手稿被认为不符合条件。当在研究中提供聚合或不完整的个体患者数据时，研究作者被要求提交患者水平数据。根据报告的大脑绩效类别（1 或 2）、格拉斯哥结局量表（4 或 5）和儿科整体绩效类别（1 或 2）的评分，对最后随访时具有良好神经结局的患者进行生存情况分类。使用混合效应多级逻辑回归模型进行了一阶、个体患者数据荟萃分析，报告优势比（OR）和 95%置信区间（CI）。
对来自 44 项观察性研究和 40 份病例报告（n=658）的数据进行了组合和分析，以确定具有良好神经结局的生存的独立预测因素。整个队列的良好神经结局生存率为 40.3%（658 例中有 265 例）。ECLS 复温率（OR：0.93；95%CI：0.88，0.98；p=0.007）、女性（OR：2.78；95%CI：1.69，4.58；p<0.001）、窒息（OR：0.19；95%CI：0.11，0.35；p<0.001）和血清钾（OR：0.62；95%CI：0.53，0.73；p<0.001）与良好神经结局的生存相关。逻辑回归模型显示出优异的区分度（c 统计量：0.849；95%CI：0.823，0.875）。
在低温性心搏骤停的治疗中使用体外生命支持提供了具有良好神经结局的生存的有利机会。当在加权评分系统中使用时，窒息、血清钾和性别可以帮助临床医生预测 ECLS 复苏低温患者的获益。
