Chiu Chun-Chieh, Chang Yu-Jun, Chiu Chun-Wen, Chen Ying-Chen, Hsieh Yung-Kun, Hsiao Shun-Wen, Yen Hsu-Heng, Siao Fu-Yuan
Department Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, 50006, Taiwan.
Epidemiology and Biostatics Center, Changhua Christian Hospital, Changhua, 50006, Taiwan.
Sci Rep. 2025 Jan 23;15(1):2915. doi: 10.1038/s41598-025-87200-7.
Extracorporeal cardiopulmonary resuscitation (ECPR) improves survival for prolonged cardiac arrest (CA) but carries significant risks and costs due to ECMO. Previous predictive models have been complex, incorporating both clinical data and parameters obtained after CPR or ECMO initiation. This study aims to compare a simpler clinical-only model with a model that includes both clinical and pre-ECMO laboratory parameters, to refine patient selection and improve ECPR outcomes. Medical records between January 2012 and January 2019 in our institution were retrospectively reviewed. Patients who met the following criteria were enrolled in the ECPR program: age 18-75 years, CCPR started with CA in < 5 min, CA was assumed to be of heart origin, and refractory CA. Survivors had similar underlying diseases and younger age without statistical significance (57.0 vs. 61.0 years, p = 0.117). Survivors had significantly higher rates of initial shockable rhythm, pulseless ventricular tachycardia and ventricular fibrillation, shorter low-flow time (CPR-to-ECMO time), lower lactate levels, and higher initial pH. Survival to discharge was higher for emergency department CA than for out-of-hospital and in-hospital CA (63.3% vs. 35.3%, p = 0.007). Two models were used for evaluating survival to discharge and good neurological outcomes. Model 1, short version based on clinical factors, (S1, survival score 1; F1, function score 1) included the patient's characteristics before ECPR, whereas Model 2, full version included clinical factors and laboratory data including lactate and pH levels (S2, survival score 2; F2, function score 2). Both Model 1(S1) and Model 2(S2) showed good predictive ability for survival to discharge with areas under the receiver operating characteristic (AUROCs) of 0.79 and 0.83, respectively. Model 1(F1) and Model 2(F2) revealed prediction power for good neurological outcomes, with AUROCs of 0.80 and 0.79, respectively. The AUROCs of survival score Model 1(S1) and 2(S2) and function score Model 1(F1) and 2(F2) were not significantly different. This study demonstrates that clinical factors alone can effectively predict survival to discharge and favorable neurological outcomes at 6 months. This emphasizes the importance of early prognostic evaluation and supports the use of clinical data as a practical tool for clinicians in decision-making for this difficult situation.
体外心肺复苏（ECPR）可提高长时间心脏骤停（CA）患者的生存率，但由于使用体外膜肺氧合（ECMO），存在重大风险和成本。以往的预测模型较为复杂，纳入了临床数据以及心肺复苏（CPR）或启动ECMO后获得的参数。本研究旨在比较一个更简单的仅基于临床因素的模型与一个包含临床和ECMO前实验室参数的模型，以优化患者选择并改善ECPR的治疗效果。我们对本机构2012年1月至2019年1月期间的病历进行了回顾性研究。符合以下标准的患者纳入ECPR项目：年龄18 - 75岁，心脏骤停发生后5分钟内开始进行持续胸外按压（CCPR），假定心脏骤停源于心脏，且为难治性心脏骤停。幸存者的基础疾病相似，年龄更小，但无统计学意义（57.0岁对61.0岁，p = 0.117）。幸存者初始可电击心律、无脉性室性心动过速和室颤的发生率显著更高，低流量时间（CPR至ECMO时间）更短，乳酸水平更低，初始pH值更高。急诊科心脏骤停患者出院生存率高于院外和院内心脏骤停患者（63.3%对35.3%，p = 0.007）。使用两个模型评估出院生存率和良好的神经功能转归。模型1为基于临床因素的简化版（S1，生存评分1；F1，功能评分1），包括ECPR前患者的特征，而模型2为完整版，包括临床因素和实验室数据，如乳酸水平和pH值（S2，生存评分2；F2，功能评分2）。模型1（S1）和模型2（S2）对出院生存率均显示出良好的预测能力，受试者工作特征曲线下面积（AUROCs）分别为0.79和0.83。模型1（F1）和模型2（F2）对良好的神经功能转归显示出预测能力，AUROCs分别为0.80和0.79。生存评分模型1（S1）和2（S2）以及功能评分模型1（F1）和2（F2）的AUROCs无显著差异。本研究表明，仅临床因素就能有效预测出院生存率和6个月时良好的神经功能转归。这强调了早期预后评估的重要性，并支持临床医生将临床数据作为在这种困难情况下进行决策的实用工具。
