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）是一个重大的公共卫生问题，每年遭受估计370,000-750,000名患者的折磨，生存率通常低于20％。众所周知，这些患者中有一半在被捕之前显示出临床恶化的迹象。快速反应系统（RRSS）旨在在临床恶化的早期阶段对患者做出反应，在防止IHCA和死亡方面令人惊讶的是，令人惊讶的是，导致一些政策制定者和研究人员提出未能确定早期临床下降或寻求帮助的迹象。解决此问题的一种可能解决方案是开发风险预测工具，该工具可根据患者即将来临的IHCA或ICU转移的可能性来准确地对其进行分层，从而可以针对高风险患者的干预措施。已经提出了几种基于生理的评分系统，它们为异常生命体征分配了点值，但是它们的中等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，定量的临床判断量表，这些临床判断，以预测未来24小时内IHCA或ICU转移。我们建议在较大的患者样本中验证该工具，并将其与两种基于生理的预测算法进行比较，以尝试找到即将临床的临床恶化的最敏感和最特定的预测指标。然后，我们将使用这三个中的最好的方法，或者如果更好的话，以确定高风险的非ICU住院患者并将其定位为RRS干预措施，以绕过需要识别恶劣患者并寻求帮助的需求，从而允许对高风险患者的RRS进行针对性评估。相关性（请参阅说明）：如果可以迅速识别并采取临床警告标志，则可以预防医院中的一些心脏骤停。由于始终监测每个住院的患者是不切实际的，因此确定哪些患者处于高风险的策略将允许专门针对这些患者的其他资源。 （抽象的结尾）