Strategies to Predict and Prevent In-Hospital Cardiac Arrest

预测和预防院内心脏骤停的策略

• 批准号: 8103985
• 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
• 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/8103985
• 关键词: Abbreviations; Acute; Address; Admission activity; Adoption; Age; Algorithms; American Heart Association; Area; Area Under Curve; Automated External Defibrillator; Blood Circulation; Bypass; Cardiac Death; 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

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）是一个重要的公共卫生问题，每年约有370，000 - 750，000例患者受到影响，存活率通常低于20%。据了解，这些患者中有一半以上在逮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），旨在响应患者在临床恶化的早期阶段，已经令人惊讶的是，在预防IHCA和死亡方面，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帮助作为可能的病因。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，该工具可用于根据即将发生的IHCA或ICU转移的可能性对患者进行准确分层，从而允许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，其将点值分配给异常生命体征，但是其平庸的预测能力和繁琐的性质限制了其采用。我们已经开发了一个简单的，单一的问题，定量规模的临床判断有关病人的稳定性，预测IHCA或ICU转移在未来24小时内。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，以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素。然后，我们将使用三者中最好的，或更好的组合措施，以识别高风险的非ICU住院患者，并针对他们进行RRS干预，绕过识别恶化患者和寻求帮助的需要，从而允许对高风险患者的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，医院中的一些心脏骤停可能是可以预防的。由于在任何时候都监测每一个住院病人是不切实际的，确定哪些病人处于高风险的战略将允许专门针对这些病人的额外资源。 (End摘要）