PDF(Pubmed)
Abstract：
UNASSIGNED: A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal CO2, which allows for research on how patients demonstrate different responses to CPR. The aim of this review is to give an overview of methodological challenges and opportunities in using defibrillator data for research.
UNASSIGNED: The successful collection of defibrillator files has several challenges. There is no scientific standard on how to store such data, which have resulted in several proprietary industrial solutions. The data needs to be exported to a software environment where signal filtering and classifications of ECG rhythms can be performed. This may be automated using different algorithms and artificial intelligence (AI). The patient can be classified being in ventricular fibrillation or -tachycardia, asystole, pulseless electrical activity or having obtained return of spontaneous circulation. How this dynamic response is time-dependent and related to covariates can be handled in several ways. These include Aalen\'s linear model, Weibull regression and joint models.
UNASSIGNED: The vast amount of signal data from defibrillator represents promising opportunities for the use of AI and statistical analysis to assess patient response to CPR. This may provide an epidemiologic basis to improve resuscitation guidelines and give more individualized care. We suggest that an international working party is initiated to facilitate a discussion on how open formats for defibrillator data can be accomplished, that obligates industrial partners to further develop their current technological solutions.
UNASSIGNED: The successful collection of defibrillator files has several challenges. There is no scientific standard on how to store such data, which have resulted in several proprietary industrial solutions. The data needs to be exported to a software environment where signal filtering and classifications of ECG rhythms can be performed. This may be automated using different algorithms and artificial intelligence (AI). The patient can be classified being in ventricular fibrillation or -tachycardia, asystole, pulseless electrical activity or having obtained return of spontaneous circulation. How this dynamic response is time-dependent and related to covariates can be handled in several ways. These include Aalen\'s linear model, Weibull regression and joint models.
UNASSIGNED: The vast amount of signal data from defibrillator represents promising opportunities for the use of AI and statistical analysis to assess patient response to CPR. This may provide an epidemiologic basis to improve resuscitation guidelines and give more individualized care. We suggest that an international working party is initiated to facilitate a discussion on how open formats for defibrillator data can be accomplished, that obligates industrial partners to further develop their current technological solutions.
摘要：
■应将除颤器连接到所有接受心肺复苏（CPR）的患者，以允许早期除颤。除颤器将收集信号数据，例如心电图（ECG），胸阻抗和呼气末CO2，这允许研究患者如何表现出对CPR的不同反应。这篇综述的目的是概述使用除颤器数据进行研究的方法学挑战和机遇。
■成功收集除颤器文件有几个挑战。如何存储这些数据没有科学标准，这导致了几个专有的工业解决方案。需要将数据导出到软件环境中，在该环境中可以执行信号过滤和ECG节律分类。这可以使用不同的算法和人工智能(AI)来自动化。患者可以被分类为心室纤颤或心动过速，心搏停止,无脉电活动或已恢复自发循环。这种动态响应是如何依赖于时间并且与协变量相关的,可以通过几种方式来处理。这些包括Aalen的线性模型，威布尔回归和联合模型。
■来自除颤器的大量信号数据代表了使用AI和统计分析来评估患者对CPR的反应的有希望的机会。这可以提供流行病学基础，以改善复苏指南并提供更个性化的护理。我们建议成立一个国际工作组，以促进关于如何实现除颤器数据的开放格式的讨论，这迫使工业合作伙伴进一步开发他们目前的技术解决方案。
■成功收集除颤器文件有几个挑战。如何存储这些数据没有科学标准，这导致了几个专有的工业解决方案。需要将数据导出到软件环境中，在该环境中可以执行信号过滤和ECG节律分类。这可以使用不同的算法和人工智能(AI)来自动化。患者可以被分类为心室纤颤或心动过速，心搏停止,无脉电活动或已恢复自发循环。这种动态响应是如何依赖于时间并且与协变量相关的,可以通过几种方式来处理。这些包括Aalen的线性模型，威布尔回归和联合模型。
■来自除颤器的大量信号数据代表了使用AI和统计分析来评估患者对CPR的反应的有希望的机会。这可以提供流行病学基础，以改善复苏指南并提供更个性化的护理。我们建议成立一个国际工作组，以促进关于如何实现除颤器数据的开放格式的讨论，这迫使工业合作伙伴进一步开发他们目前的技术解决方案。
