Resuscitation最新文献

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Beyond survival: Cost-effectiveness of eCPR for refractory cardiac arrest 超越生存：eCPR治疗难治性心脏骤停的成本效益

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-20 DOI: 10.1016/j.resuscitation.2025.110930

Sasa Rajsic, Gabriel Putzer, Regina Unterpertinger, Robert Breitkopf

引用次数: 0

Experience with physiological based cord clamping is associated with increased effect size: a post-hoc analysis of the ABC3 trial 基于生理的脐带夹紧经验与增加的效应大小相关：ABC3试验的事后分析

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-17 DOI: 10.1016/j.resuscitation.2025.110921

Ronny Knol, Marijn J. Vermeulen, Willem P. de Boode, G. Jeroen Hutten, Sandra A. Prins, Estelle E.M. Mulder, Christian V. Hulzebos, Sam J. van Sambeeck, Mayke E. van der Putten, Inge A. Zonnenberg, Sten P. Willemsen, Thomas van den Akker, Philip L.J. DeKoninck, Stuart B. Hooper, Arjan B. te Pas, Emma Brouwer, Anton H. van Kaam, Enrico Lopriore, Irwin K.M. Reiss, Esther J. d’Haens, Helene A. Bouma, Hendrik J. Niemarkt, Tinta Lebon, Debbie H. Nuytemans, Graeme R. Polglase, Sylke J. Steggerda

引用次数: 0

Beyond ventricular fibrillation waveform analysis: a new era of personalised defibrillation 超越心室颤动波形分析：个体化除颤的新时代

IF 4.6 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-17 DOI: 10.1016/j.resuscitation.2025.110929

Francesca Fumagalli , Giuseppe Ristagno

引用次数: 0

Reply to: When ground truth is not static: rethinking AI performance in occlusive MI detection from post-MI ECGs. 回复：当地面真相不是静态的：从心肌梗死后心电图重新思考人工智能在闭塞性心肌梗死检测中的表现。

IF 4.6 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-16 DOI: 10.1016/j.resuscitation.2025.110928

Claudio Silwanis, Max Groche, Clemens Steinwender, Thomas Lambert

引用次数: 0

When Ground Truth Is Not Static: Rethinking AI Performance in Occlusive MI Detection from Post-MI ECGs 当基础事实不是静态的：从心肌梗死后心电图重新思考人工智能在闭塞性心肌梗死检测中的表现

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-13 DOI: 10.1016/j.resuscitation.2025.110924

Lingyi Meng, Mengda Yu

引用次数: 0

Hypoxemia in hypothermic cardiac arrest: why one number should not decide a life “低氧血症在低温心脏骤停：为什么一个数字不应该决定一个人的生命”

IF 4.6 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-13 DOI: 10.1016/j.resuscitation.2025.110923

Joseph Varon

引用次数: 0

Age and sex differences in the sensitivity of ICD-10 diagnostic codes for identifying patients with out-of-hospital cardiac arrest in the British Columbia cardiac arrest registry: a retrospective cohort study 在不列颠哥伦比亚省心脏骤停登记处，ICD-10诊断代码识别院外心脏骤停患者敏感性的年龄和性别差异：一项回顾性队列研究

IF 4.6 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-11 DOI: 10.1016/j.resuscitation.2025.110922

Mika’il Visanji , Omar Dewidar , Theresa Aves , Katherine S. Allan , Meijiao Guan , Brian Grunau , Christopher B. Fordyce , Jim Christenson , Jennie Helmer , Jacob Hutton , Paul Dorian , Steve Lin

Background

Cardiac arrest researchers frequently use administrative health databases to study out-of-hospital cardiac arrest (OHCA) incidence, but their sensitivity to identify OHCA is not well known.

Methods

We included Emergency Medical Services (EMS)-treated cases from the BC Cardiac Arrest Registry that survived to hospital admission between 2012 and 2016, and linked to their admission records in the hospital admissions database (Discharge Abstract Database). We calculated sensitivity as the proportion of cases with OHCA-related International Classification of Disease 10 (ICD-10) codes that were listed as a preadmission comorbidity or the primary reason for hospital stay (coded as arising preadmission). In cases without OHCA-related codes, we identified other recorded ICD-10 codes and diagnosis types.

Results

Of 6009 eligible OHCAs during the study period, 2032 (33.8 %) patients who survived to hospital admission were included. OHCA-related ICD-10 codes were recorded in 1357/2032 (66.8 %; 95 % CI 64.7, 68.8) of cases. Sensitivity did not differ across sex and age groups. Among cases without OHCA-related codes, acute myocardial infarction was the most frequently assigned pre-admission diagnosis (225/675; 33.3 %), coded as most responsible for hospital admission, and coronary artery disease-related ICD-10 codes were the most common pre-admission diagnoses (176/675, 26.1 %).

Conclusion

One third of patients in our patient population suffering from OHCA and admitted to hospital were not coded as having a cardiac arrest. Using administrative databases to identify OHCA patients results in underestimation of cardiac arrest incidence, highlighting the need for ongoing dedicated OHCA registries.

心脏骤停研究人员经常使用行政卫生数据库来研究院外心脏骤停（OHCA）的发生率，但其识别OHCA的敏感性尚不清楚。方法我们纳入了2012年至2016年期间在BC省心脏骤停登记处接受紧急医疗服务（EMS）治疗的住院病例，并将其入院记录与医院入院数据库（出院摘要数据库）相关联。我们将敏感性计算为与ohca相关的国际疾病分类10 （ICD-10）代码被列为入院前共病或住院的主要原因（编码为入院前发生）的病例的比例。在没有ohca相关代码的病例中，我们确定了其他记录的ICD-10代码和诊断类型。结果在研究期间的6009例符合条件的ohca中，2032例（33.8%）患者存活至住院。1357/2032例（66.8%;95% CI 64.7, 68.8）记录了与ohca相关的ICD-10代码。敏感性在性别和年龄组之间没有差异。在没有ohca相关代码的病例中，急性心肌梗死是最常见的入院前诊断（225/675,33.3%），被编码为最负责的入院原因，冠状动脉疾病相关的ICD-10代码是最常见的入院前诊断（176/675,26.1%）。结论本组患者中有三分之一的OHCA患者入院时未被编码为心脏骤停。使用行政数据库来识别OHCA患者会导致心脏骤停发生率的低估，这突出了持续的OHCA专门登记的必要性。

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引用次数: 0

Large-scale automated phenotyping of cardiac arrest and withdrawal of life-sustaining therapy using electronic health record data 使用电子健康记录数据对心脏骤停和停止生命维持治疗进行大规模自动分型。

IF 4.6 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-08 DOI: 10.1016/j.resuscitation.2025.110919

Catherine Clive , Arjun Singh , Bram Overmeer , Spencer Boris , Lydia Peterson , Jaden Searle , Greg Hooke , Niels Turley , Marta Fernandes , Aditya Gupta , Manohar Ghanta , Valdery Moura Junior , S. Mukeriji , Sahar Zafar , Edilberto Amorim , M. Brandon Westover , Haoqi Sun

Aims of the study

Anoxic brain injury following cardiac arrest is a leading cause of death in the United States. Withdrawal of life-sustaining therapy (WLST) is a common end-of-life decision in these patients, but its contributing factors and outcomes remain poorly understood. We developed machine learning models to enable large-scale, automated phenotyping to identify patients who died following WLST.

Methods

We used structured and unstructured EHR (Electronic Health Record) data from two major hospitals to train models that identify (1) patients with cardiac arrest and coma, and (2) patients who died after WLST. Performance was evaluated using the area under the receiver operating characteristic (AUROC) and precision-recall (AUPRC) curves, as well as other precision metrics.

Results

On holdout (internal) testing the models achieved AUROC/AUPRC values of 0.984/0.968 (cardiac arrest) and 0.992/0.991 (WLST). Cross-hospital evaluation showed strong performance for the cardiac arrest phenotype but variable generalizability for the WLST phenotype, with sensitivity depending on the training site. Population-level error rates were low (<0.5 %) for the cardiac arrest phenotype; estimates for WLST varied by hospital.

Conclusion

These models establish a reproducible framework for automated cohort identification. Nearly half of comatose post-arrest patients died following WLST, with 42 % of these deaths occurring within 72 h, highlighting the impact of early prognostication decisions. The models enable rapid cohort identification for research on neuroprognostication, including how WLST decisions may perpetuate self-fulfilling prophecies. Broader validation across health systems and larger cohorts will improve generalizability and inform evidence-based end-of-life decision-making.

Institutional review board approval: Mass General Brigham IRB BIDMC: 2022P000481; MGB: 2013P001024.

All procedures complied with institutional and national ethical standards; informed consent was waived for use of de-identified data.

研究目的：心脏骤停后的缺氧脑损伤是美国死亡的主要原因。在这些患者中，停止维持生命治疗（WLST）是一种常见的临终决定，但其影响因素和结果仍然知之甚少。我们开发了机器学习模型，以实现大规模、自动化的表型分析，以识别WLST后死亡的患者。方法：我们使用来自两家大医院的结构化和非结构化EHR（电子健康记录）数据来训练模型，以识别(1)心脏骤停和昏迷患者，以及(2)WLST后死亡患者。使用接收器工作特性（AUROC）和精确召回率（AUPRC）曲线下的面积以及其他精度指标来评估性能。结果：经内部检验，模型的AUROC/AUPRC值分别为0.984/0.968（心脏骤停）和0.992/0.991 （WLST）。跨医院评估显示，心脏骤停表型具有很强的表现，但WLST表型具有可变的普遍性，其敏感性取决于训练地点。结论：这些模型为自动队列识别建立了一个可重复的框架。近一半的骤停后昏迷患者在WLST后死亡，其中42%的死亡发生在72小时内，突出了早期预后决定的影响。这些模型能够快速识别神经预测研究的队列，包括WLST决策如何使自我实现的预言永存。在卫生系统和更大的队列中进行更广泛的验证将提高普遍性，并为基于证据的临终决策提供信息。机构审查委员会批准：Mass General Brigham IRB BIDMC: 2022P000481；MGB: 2013 p001024。所有程序均符合机构和国家道德标准；对于使用去识别数据，我们放弃了知情同意。

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Expanding qEEG feature analysis: A step towards better prognostication 扩展qEEG特征分析：迈向更好预测的一步。

IF 4.6 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-08 DOI: 10.1016/j.resuscitation.2025.110916

Munirah Alyaseen, Barnaby R. Scholefield

引用次数: 0

Head CT after pediatric in-hospital cardiac arrest: strong signals, careful conclusions 儿科院内心脏骤停后的头部CT：强烈信号，谨慎结论

IF 4.6 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-12-06 DOI: 10.1016/j.resuscitation.2025.110920

Stephan Katzenschlager , Jimena del Castillo

引用次数: 0

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