Strategies to Predict and Prevent In-Hospital Cardiac Arrest

描述(由申请人提供)：住院心脏骤停(IHCA)是一个重要的公共卫生问题，估计每年有37万至75万名患者受到影响，存活率一般低于20%。已知这些患者中有一半以上在被捕前的几个小时内表现出临床恶化的迹象。快速反应系统(RRS)旨在对临床恶化的早期阶段的患者做出反应，但在预防IHCA和死亡方面却出人意料地平淡无奇，导致一些政策制定者和研究人员建议未能识别早期临床恶化的迹象或呼吁帮助作为可能的病因。这一问题的一个可能的解决方案是开发一种风险预测工具，该工具可用于根据患者即将进行IHCA或ICU转移的可能性对患者进行准确的分层，从而使干预措施能够针对高危患者。已经提出了几种基于生理学的评分系统，为异常生命体征分配积分值，但其平庸的预测能力和繁琐的性质限制了它们的采用。我们已经开发了一种简单的、单一问题的、关于患者稳定性的临床判断量化量表，它可以预测未来24小时内IHCA或ICU的转院情况。我们建议在更大的患者样本中验证该工具，并将其与两种基于生理学的预测算法进行比较，试图找到对即将到来的临床恶化最敏感和最特异的预测因子。然后，我们将使用三种方法中的最好一种，或者如果更好的话，综合使用这三种方法，以便识别高危非ICU住院患者，并针对他们进行RRS干预，从而绕过识别病情恶化的患者和寻求帮助的需要，从而对高危患者的RRS进行有针对性的评估。相关性(见说明书)：如果能够识别临床警告信号并迅速采取行动，医院的一些心脏骤停可能是可以预防的。由于始终监测每一位住院患者并不现实，因此确定哪些患者处于高危状态的战略将使额外的资源能够专门针对这些患者。(摘要结束)

DESCRIPTION (provided by applicant): In-hospital cardiac arrest (IHCA) is a significant public health concern, afflicting an estimated 370,000- 750,000 patients annually, with survival rates generally below 20%. Over half of these patients are known to display signs of clinical deterioration in the hours leading up to the arrest. Rapid Response Systems (RRSs), designed to respond to patients in the early stages of clinical deterioration, have been surprisingly underwhelming with regards to preventing IHCA and death, leading some policy makers and researchers to suggest failures to identify the signs of early clinical deterioration or to call for help as possible etiologies. One possible solution to this problem is the development of a risk prediction tool that could be used to accurately stratify patients based on their likelihood of impending IHCA or ICU transfer, allowing interventions to be targeted at high risk patients. Several physiology-based scoring systems, which assign point values to abnormal vital signs, have been proposed but their mediocre predictive ability and cumbersome nature have limited their adoption. We have developed a simple, single question, quantitative scale of clinical judgment regarding patient stability that predicts IHCA or ICU transfer within the next 24 hours. We propose to validate that tool in a larger sample of patients and compare it to two physiology-based prediction algorithms, in an attempt to find the most sensitive and specific predictor of impending clinical deterioration. We will then use the best of the three, or a combined measure if better, in order to identify high-risk non-ICU inpatients and target them for a RRS intervention that bypasses the need to identify deteriorating patients and call for help, thereby allowing a targeted assessment of the RRS in high risk patients. RELEVANCE (See instructions): Some cardiac arrests in the hospital may be preventable if the clinical warning signs can be identified and acted upon quickly. Since it is not practical to monitor every hospitalized patient at all times, strategies to determine which patients are at high risk would allow additional resources to be targeted specifically at those patients. (End of Abstract)