Improving Heart Failure Risk Stratification in the ED

改善急诊室的心力衰竭风险分层

基本信息

批准号：
7842246
负责人：
ALAN B STORROW
金额：
$ 26.14万
依托单位：
VANDERBILT UNIVERSITY
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：
2009-07-15 至 2011-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7842246
关键词：
Accident and Emergency department Acute Admission activity Affect Aftercare Algorithms Beds Behavior Biometry Cardiology Caring Cessation of life Collaborations Complex Critical Care Data Data Set Diagnosis Effectiveness Emergency Care Emergency Medicine Emergency Situation Environment Evaluation Event Guidelines Healthcare Healthcare Systems Heart failure Hospitalization Hospitals Hour Inpatients Lead Measures Methodology Modeling Monitor Observational Study Outcome Outpatients Patient Care Patient Discharge Patients Physicians Population Primary Health Care Probability Provider Research Personnel Research Training Resources Risk Risk Factors Selection Bias Source Statistical Methods Stratification Symptoms Testing Translating Treatment outcome Visit Work acute coronary syndrome base clinical application clinical practice clinically relevant cost database design design efficacy testing high risk improved innovation mathematical model novel patient population primary outcome programs prospective safety testing secondary outcome socioeconomics tool

项目摘要

DESCRIPTION (provided by applicant): A critical challenge facing emergency department (ED) physicians is how best to manage patients presenting with symptoms of heart failure. Currently, most patients being evaluated for heart failure are admitted to the hospital, yet not all of these patients warrant such intensive treatment, and up to 50% of these admissions could be avoided. Improving the ability of the emergency physician to effectively and safely manage low-risk patients is essential to avoid unnecessary hospitalizations. We propose developing a decision tool derived from prospectively gathered ED data that will predict risk for inpatient or outpatient death and serious in-hospital or out-of-hospital complications. Further, the proposed project will validate the usefulness and generalizability of this decision tool in three different ED environments across racially and socioeconomically diverse patient populations. To develop our decision tool, over 100 variables routinely available to the emergency physician within the first two hours of ED presentation will be considered for inclusion in a statistical risk model. Unlike exisitng models using inpatient data, these measures are representative of actual clinical practice and routinely used to decide a patient's disposition. We will collect standardized data during a patient's evaluation for heart failure. Relying on chart review or large dataset analyses can lead to missing and inconsistent data. We will include all patients evaluated for heart failure regardless of final diagnosis, thus avoiding selection bias inherent in models based on patients with a definitive diagnosis. A fundamental innovation we propose is a tool using 5-day outcomes for primary analyses, and 30-day outcomes for secondary analyses. This overcomes the limitation of 30-day outcome models that are highly dependent on unpredictable, post-visit patient and provider behavior. Another novel aspect of the proposed project is the combining of expertise in emergency medicine, cardiology, and biostatistics to accurately assign post-treatment outcomes to acute presentations. Results will be translated into an algorithm that will be disseminated worldwide. This is the first step toward achieving our broad objective of appropriate allocation of hospital resources to reduce costs of heart failure care. In collaboration with outcomes and effectiveness researchers, we plan to conduct further studies to test the efficacy of our risk model.

描述（由申请人提供）：急诊科 (ED) 医生面临的一个关键挑战是如何最好地管理出现心力衰竭症状的患者。目前，大多数接受心力衰竭评估的患者都会入院，但并非所有这些患者都需要如此强化治疗，并且高达 50% 的入院患者是可以避免的。提高急诊医生有效、安全地管理低风险患者的能力对于避免不必要的住院治疗至关重要。我们建议开发一种基于前瞻性收集的急诊数据的决策工具，该工具将预测住院或门诊死亡以及严重的院内或院外并发症的风险。此外，拟议的项目将验证该决策工具在跨种族和社会经济多样化患者群体的三种不同 ED 环境中的有用性和普遍性。为了开发我们的决策工具，急诊医生在急诊科就诊的前两个小时内常规可用的 100 多个变量将被考虑纳入统计风险模型。与使用住院患者数据的现有模型不同，这些测量代表了实际的临床实践，并且通常用于决定患者的处置。我们将在患者评估心力衰竭期间收集标准化数据。依赖图表审查或大型数据集分析可能会导致数据丢失和不一致。无论最终诊断如何，我们都将纳入所有接受心力衰竭评估的患者，从而避免基于明确诊断的患者的模型中固有的选择偏差。我们提出的一项根本性创新是使用 5 天结果进行主要分析，使用 30 天结果进行二次分析的工具。这克服了 30 天结果模型的局限性，该模型高度依赖于不可预测的就诊后患者和提供者的行为。该项目的另一个新颖之处是结合了急诊医学、心脏病学和生物统计学的专业知识，以准确地将治疗后结果分配给急性表现。结果将被转化为将在全球范围内传播的算法。这是实现我们适当分配医院资源以降低心力衰竭护理成本的广泛目标的第一步。我们计划与结果和有效性研究人员合作进行进一步的研究，以测试我们的风险模型的有效性。