Strategies to Predict and Prevent In-Hospital Cardiac Arrest

预测和预防院内心脏骤停的策略

基本信息

批准号：
7923859
负责人：
Dana Peres Edelson
金额：
$ 12.96万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7923859
关键词：
Abbreviations Acute Address Admission activity Adoption Age Algorithms American Heart Association Apache Indians Area Area Under Curve Automated External Defibrillator Blood Circulation Bypass Cardiopulmonary Resuscitation Cardiovascular system Cessation of life Chronic Clinical Critical Care Data Deterioration Development Early treatment Emergency Situation Equilibrium Etiology Evaluation Event Failure Floor Goals Health Heart Arrest Hospitals Hour ImProv Inpatients Instruction Intensive Care Intensive Care Units Intervention Joint Commission on Accreditation of Healthcare Organizations Joints Judgment Length of Stay Life Literature Measures Medical Modeling Monitor Nature Neonatal Observational Study Outcome Patients Performance Pharmaceutical Preparations Physicians Physiological Physiology Policy Maker Principal Investigator Probability Public Health Randomized Randomized Controlled Clinical Trials Receiver Operator Characteristics Registries Research Personnel Resources Resuscitation Risk S-nitro-N-acetylpenicillamine SCAP2 gene Sampling Sensitivity and Specificity Sepsis Syndrome Signs and Symptoms Solutions Staging Survival Rate System Testing Therapeutic Intervention Time United States United States National Institutes of Health Ventricular Fibrillation Ventricular Tachycardia Work abstracting arm base cost design help-seeking behavior high risk improved instrument mortality outcome forecast outreach patient safety predictive modeling preference prevent programs prospective response routine care standard care tool

项目摘要

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)

描述（由申请人提供）：院内心脏骤停 (IHCA) 是一个重大的公共卫生问题，每年影响估计 370,000-750,000 名患者，存活率通常低于 20%。据了解，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）旨在对临床恶化早期阶段的患者做出反应，但在预防 IHCA 和死亡方面却令人惊讶地表现不佳，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寻求可能的病因帮助。解决这个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分层，从而针对高风险患者进行干预。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。我们开发了一个关于患者稳定性的简单、单一问题、定量临床判断量表，可预测未来 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，试图找到即将发生的临床恶化的最敏感和最具体的预测因子。然后，我们将使用三者中最好的措施，或者更好的综合措施，以确定高风险的非 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，从而允许对高危患者的 RRS 进行有针对性的评估。相关性（参见说明）：如果能够识别临床警告信号并迅速采取行动，一些在医院发生的心脏骤停是可以预防的。由于始终监测每位住院患者并不现实，因此确定哪些患者处于高风险的策略将允许额外的资源专门针对这些患者。（摘要完）