Resuscitation最新文献

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Corrigendum to “The “invisible ceiling” of bystander CPR in three Asian countries: Descriptive study of national OHCA registry” [Resuscitation 206 (2025) 110445]

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-03-04 DOI: 10.1016/j.resuscitation.2025.110555

Yohei Okada , Ki Jeong Hong , Shir Lynn Lim , Dehan Hong , Yih Yng Ng , Benjamin S.H. Leong , Kyoung Jun Song , Jeong Ho Park , Young Sun Ro , Tetsuhisa Kitamura , Chika Nishiyama , Tasuku Matsuyama , Takeyuki Kiguchi , Norihiro Nishioka , Taku Iwami , Sang Do Shin , Marcus Eng Hock Ong , Fahad Javaid Siddiqui

引用次数: 0

Amiodarone vs lidocaine in adult out-of-hospital cardiac arrest, is there a clear winner?

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-03-01 DOI: 10.1016/j.resuscitation.2025.110547

Petter Overton-Harris, Joshua R. Lupton

引用次数: 0

LIFEFORCE: An essential European Union funded project training schoolchildren in CPR using a targeted BLS algorithm

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-03-01 DOI: 10.1016/j.resuscitation.2025.110553

N. Rott, LIFEFORCE Collaborators

引用次数: 0

Semi-autonomous drone delivering automated external defibrillators for real out-of-hospital cardiac arrest: A Danish feasibility study

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-03-01 DOI: 10.1016/j.resuscitation.2025.110544

Louise Kollander Jakobsen , Jannie Kristine Bang Gram , Anne Juul Grabmayr , Anders Højen , Carolina Malta Hansen , Martin Rostgaard-Knudsen , Andreas Claesson , Fredrik Folke

Aim

To assess the feasibility and safety of drone-delivered automated external defibrillators (AEDs) in real out-of-hospital cardiac arrests (OHCAs) in Denmark, addressing the critical need for timely defibrillation in OHCAs.

Methods

In this prospective clinical study in Aalborg, Denmark, an AED-carrying drone was dispatched for suspected OHCAs, from June 2022 to April 2023. The drone was stationed in an urban area (maximum flight-radius 6 km, covering 110,000 inhabitants) within designated airspace not requiring preflight approval from air-traffic control. Upon OHCA-suspicion, the emergency medical dispatcher activated the drone, which autonomously took off and flew beyond-visual-line-of-sight to the OHCA-location. On-site, a remote drone pilot (stationed cross-border) winched down the AED near the patient’s location. Flights were restricted to dry weather, mean windspeeds < 8 m/s, and 8 am to 10 pm.

Results

Of 76 suspected OHCAs, 27 occurred during non-operating hours (nighttime). Of the remaining 49 OHCAs, 16 (33%) were eligible for drone take-off, all of which resulted in successful AED-delivery, without any adverse events. Weather caused 14 cancellations (29%), technical issues (dispatch centre, drone, or hangar problems) 13 (27%), and closed airspace 6 (12%). The median drone response time from activation to AED-delivery was 04:47 min (IQR 03:45–05:27), and the corresponding ambulance response time was 03:25 min (IQR 02:43–04:14). No drone-delivered AEDs were attached.

Conclusion

This study demonstrates the safety and feasibility of drone-delivered AEDs to real OHCAs. Improved time to AED delivery was limited due to swift ambulance service, highlighting the importance of strategic AED drone placement.

目的：评估在丹麦实际发生的院外心脏骤停（OHCAs）中使用无人机投放自动体外除颤器（AED）的可行性和安全性，满足院外心脏骤停患者对及时除颤的迫切需求：在丹麦奥尔堡进行的这项前瞻性临床研究中，从 2022 年 6 月到 2023 年 4 月，丹麦为疑似院外心脏骤停患者派出了一架携带自动体外除颤器（AED）的无人机。无人机驻扎在城市地区（最大飞行半径 6 公里，覆盖 11 万居民）的指定空域内，无需空中交通管制部门的飞行前批准。紧急医疗调度员在收到 OHCA 的怀疑后启动了无人机，无人机自动起飞，在视线范围之外飞往 OHCA 所在地。在现场，一名远程无人机驾驶员（跨境驻扎）将自动体外除颤器吊到病人所在位置附近。飞行仅限于干燥天气，平均风速结果：在 76 例疑似心脏骤停患者中，有 27 例发生在非工作时间（夜间）。在剩余的 49 例心脏骤停患者中，有 16 例（33%）符合无人机起飞条件，全部成功投放了自动体外除颤器，未发生任何不良事件。天气原因导致取消 14 次（29%），技术问题（调度中心、无人机或机库问题）13 次（27%），封闭空域 6 次（12%）。无人机从启动到投放自动体外除颤器的响应时间中位数为 04:47 分钟（IQR 03:45 - 05:27），相应的救护车响应时间为 03:25 分钟（IQR 02:43 - 04:14）。未安装无人机投放的自动体外除颤器：这项研究证明了无人机投放自动体外除颤器对实际 OHCAs 的安全性和可行性。由于救护服务迅速，AED 的投放时间有限，这凸显了战略性 AED 无人机投放的重要性。

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引用次数: 0

Use of machine learning models to identify National Institutes of Health-funded cardiac arrest research

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-03-01 DOI: 10.1016/j.resuscitation.2025.110545

Ryan A. Coute , Kameshwari Soundararajan , Michael C. Kurz , Ryan L. Melvin , Ryan C. Godwin

Objective

To compare the performance of three artificial intelligence (AI) classification strategies against manually classified National Institutes of Health (NIH) cardiac arrest (CA) grants, with the goal of developing a publicly available tool to track CA research funding in the United States.

Methods

Three AI strategies—traditional machine learning (ML), large language model (LLM) zero-shot learning, and LLM few-shot learning—were compared to manually categorized CA grant abstracts from NIH RePORTER (2007–2021). Traditional ML used a regularized logistic regression model trained on embedding vectors generated by OpenAI’s text-embedding-3-small model. Zero-shot learning, using GPT-4o-mini, classified grants based on task descriptions without labeled examples. Few-shot learning included six example grants. Models were evaluated on a balanced 20% holdout test set using accuracy, precision (positive predictive value), recall (sensitivity), and F1 score (harmonic mean of precision and recall).

Results

Out of 1,505 grants categorized, 378 (25%) were identified as CA research, yielding 302 grants in the holdout test set, 76 of which were CA research. The few-shot approach performed best, achieving the highest accuracy (0.90) and the best balance of precision and recall (F1 score 0.82). In contrast, traditional ML had the lowest accuracy (0.87) and the highest precision (0.89) but suffered from poor recall, with approximately 2.5 times more false negatives than either generative approach. The zero-shot approach outperformed traditional ML in accuracy (0.88) and recall (0.86) but had lower precision (0.72).

Conclusion

AI can rapidly identify CA grants with excellent accuracy and very good precision and recall, making it a promising tool for tracking research funding.

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引用次数: 0

Reply to “Revisiting priorities in resuscitative hysterotomy”

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-03-01 DOI: 10.1016/j.resuscitation.2025.110548

Caroline Leech, Tim Nutbeam, Keith Couper, Joyce Yeung

引用次数: 0

Reply to letter “Learn to drive, learn CPR: Advancing road safety and life-saving skills across Europe”

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-02-26 DOI: 10.1016/j.resuscitation.2025.110565

Alexander Fuchs, Robert Greif

引用次数: 0

Shot in the Thigh or Stab in the Dark? Challenging the Evidence for IM Adrenaline in OHCA. 一针见血还是暗箭伤人？质疑在 OHCA 中使用即时肾上腺素的证据。

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-02-26 DOI: 10.1016/j.resuscitation.2025.110562

引用次数: 0

Evaluating Artificial Intelligence Competency in Education: Performance evaluation of ChatGPT in the Neonatal resuscitation program exam.

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-02-25 DOI: 10.1016/j.resuscitation.2025.110563

Mehmet Semih Demirtas, Kamil Kokulu, Gaffari Tunc

引用次数: 0

Drone delivery of automated external defibrillators for out-of-hospital cardiac arrest: It’s all about location

IF 6.5 1区医学 Q1 CRITICAL CARE MEDICINE

Resuscitation

Pub Date : 2025-02-25 DOI: 10.1016/j.resuscitation.2025.110561

Sheldon Cheskes

引用次数: 0

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