Workload effects on response time to life-threatening arrhythmias

描述（由申请人提供）：美国医院每年进行37万至75万例心肺复苏。大约80%的患者不能存活到出院。然而，对许多人来说，无脉性室性心动过速或心室颤动（VT/VF）是首先监测到的心律失常，可以通过及时除颤成功治疗。美国心脏协会建议在心脏骤停两分钟内进行除颤治疗。然而，对于30%的患者，除颤延迟超过两分钟，使他们存活到出院的机会减少了一半。在对有危险的病人进行住院治疗时，没有什么比有效地发现潜在的致命性心律失常更重要的目标了，但目前还没有可靠的证据来指导如何做到这一点。为了增加及时发现心脏事件的可能性，现在越来越多的高危患者由心脏遥测技术人员远程监测。然而，关于单个技术人员可以安全有效地监测患者的适当数量的决定似乎主要基于技术能力，而不是基于我们对人类信息处理局限性的理解。模拟提供了在一定时间内观察和测量对危及生命的心肺事件的反应的机会，其准确度是通过评估对真实事件的反应不可实现的。我们建议在远程监控的背景下使用模拟来评估人系统的性能。我们的目的是确定增加监测的患者数量对心肺事件反应时间的影响。为了实现这一目标，我们将首先设计和测试心脏遥测的高保真模拟，作为测量患者负荷对技术人员性能影响的方法。我们将设计一个真实的模拟，复制心脏遥测技术人员的工作，使用真实患者数据和模拟患者经历VT/VF事件的组合。我们将在不同的病人负荷下对实验程序进行试点测试。其次，我们将进行一项随机对照试验，以确定监测患者数量增加对危及生命的心律失常反应时间的影响。我们将比较五种患者情况下的响应时间。通过统计分析和患者数量与反应时间的图表，我们将确定性能下降的比率。本研究的预期结果是科学建立的技术人员与患者的比例，将平衡及时检测心律失常的重要性与成本和效率要求。所获得的知识将有助于在现实世界的心脏遥测中研究这一问题，并最终有助于制定远程监测的循证标准。这些标准的应用有望提高院内心脏骤停后的生存率。

DESCRIPTION (provided by applicant): Between 370,000 and 750,000 cardiopulmonary resuscitations are attempted each year in US hospitals. Approximately 80% of these patients do not survive to discharge. However, for many, pulseless ventricular tachycardia or ventricular fibrillation (VT/VF) is the first monitored arrhythmia, which may be treated successfully with prompt defibrillation. The American Heart Association recommends defibrillation therapy within two minutes of cardiac arrest onset. Yet, for 30% of patients, defibrillation is delayed more than two minutes, reducing their chance of survival to hospital discharge by half. There are few more important objectives in hospitalizing a patient at risk than being able to effectively detect potentially fatal arrhythmias, yet there is little reliable evidence to guide how this should be done. To increase the potential for timely detection of cardiac events, more and more at-risk patients are now monitored remotely by cardiac telemetry technicians. However, decisions regarding the appropriate number of patients that a single technician may safely and effectively monitor appear to be primarily based on technological capabilities and not on our understanding of human information processing limitations. Simulation provides an opportunity to observe and measure responses to life-threatening cardio-respiratory events in a time frame and with a degree of accuracy not feasible through assessment of response to true events. We propose to use simulation to assess human- system performance in the context of remote monitoring. Our objective is to determine the impact of increasing the number of patients monitored on response time to cardio-respiratory events. To achieve this objective, we will first design and test high-fidelity simulation of cardiac telemetry as a method for measuring patient load effects on technician performance. We will design a realistic simulation that replicates the work of cardiac telemetry technicians using a combination of real patient data and a simulated patient experiencing a VT/VF event. We will pilot test the experimental procedure with different patient loads. Second, we will carry out a randomized controlled trial to determine the impact of increasing number of patients monitored on response time to life-threatening arrhythmias. We will compare response times across five number-of-patient conditions. Through statistical analyses and plots of number of patients vs. response times, we will identify ratios at which performance declines. The expected outcome of this study is a scientifically established technician-to-patient ratio that will balance the critical importance of timely arrhythmia detectio with cost and efficiency requirements. The knowledge to be gained will inform efforts to study this problem in real-world cardiac telemetry and, ultimately, help to develop evidence-based standards for remote monitoring. The application of such standards is expected to improve survival after in-hospital cardiac arrest. PUBLIC HEALTH RELEVANCE: To increase the potential for timely detection and treatment of in-hospital cardiac events, more and more at-risk patients are now monitored remotely by cardiac telemetry technicians. However, decisions regarding the appropriate number of patients that a single technician may safely and effectively monitor are largely based on technological capabilities and not on our understanding of human information processing limitations. We propose to use high-fidelity simulation of cardiac telemetry to determine the impact of increasing the number of patients monitored on response time to cardio-respiratory events. The knowledge to be gained will inform efforts to study this problem in real-world cardiac telemetry and, ultimately, help to develop evidence-based standards for remote monitoring.