Strategies to Predict and Prevent In-Hospital Cardiac Arrest

描述（由申请人提供）：医院内心脏骤停（IHCA）是一个重大的公共卫生问题，每年约有37万至75万患者受到影响，生存率通常低于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。旨在应对早期临床恶化患者的快速反应系统（RRSs）在预防IHCA和死亡方面令人惊讶地表现不佳，导致一些决策者和研究人员认为，未能识别早期临床恶化的迹象，也未能作为可能的病因寻求帮助。一种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转移的可能性准确地对患者进行分层，从而使干预措施能够针对高危患者。一些基于生理的评分系统已经被提出，这些系统为异常的生命体征分配分值，但它们的一般预测能力和繁琐的性质限制了它们的采用。我们已经开发了一个简单的，单一的问题，关于患者稳定性的临床判断定量量表，预测未来24小时内IHCA或ICU转移。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，试图找到最敏感和最具体的临床恶化预测器。然后，我们将使用三种方法中最好的一种，或者如果更好的话，使用一种组合方法，以识别高风险的非icu住院患者，并针对他们进行RRS干预，而无需识别病情恶化的患者并寻求帮助，从而允许对高风险患者的RRS进行有针对性的评估。相关性（见使用说明）：如果能够识别临床警告信号并迅速采取行动，医院中的一些心脏骤停是可以预防的。由于不可能一直监测每一位住院患者，因此确定哪些患者处于高风险的策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)