Evaluating Hospital Outcomes for Injured Patients

描述（由申请人提供）：国家依赖其创伤中心来处理日常紧急情况和潜在灾害。本提案的目的是通过改进用于评估创伤中心绩效的数据库、统计方法和报告系统，为确保美国创伤护理的质量提供信息和技术。美国外科医师学会（ACS）创建了一个国家创伤数据库（NTDB），现在整合了500多家医院注册的100多万名患者的详细数据。这为本项目和其他创伤结果研究提供了一个新的、独特的数据来源。然而，由于NTDB是一个偏向于较大医院和病情较重的患者的便利样本，因此本项目也将使用来自AHRQ全国住院患者样本（NIS）的当代基于人口的数据。入院后的住院死亡率预计在两个数据库的不同机构之间有系统的差异，即使在对损伤类型和严重程度、合并症和年龄进行调整后也是如此。将寻求患者协变量的最佳组合来解释这一主要结果。将评估剩余变异性，以确定与改善生存率相关的医院特征和具有特殊结果的个别机构。其他结果（住院时间、出院到长期护理）也将被评估。多层（分层）模型将用于统计分析。这些模型已经更广泛地应用于医疗保健研究，现在是解释集群数据结构（例如，医院内的患者）的标准方法。它们减少了因样本量小而产生的虚假离群值识别，同时提供了对患者和医院特征的独立影响的更有效的估计。使用新的数据库、变量和统计模型的估计和汇总统计将与传统方法的结果进行比较。该项目旨在建立一个可信且可验证的数据管理结构和分析方法，可用于ACS、AHRQ和其他机构对创伤护理的持续评估。

DESCRIPTION (Provided by Applicant): The nation depends upon its trauma centers for everyday emergencies as well as potential disasters. The objective of this proposal is to provide information and techniques for ensuring the quality of trauma care in the United States by improving the databases, statistical methods, and reporting systems used to assess trauma center performance. The American College of Surgeons (ACS) has created a National Trauma Data Bank (NTDB), now combining detailed data from more than a million patients in more than 500 hospital registries. This provides a new and unique data source for this project and other trauma outcomes research. However, because the NTDB is a convenience sample biased toward larger hospitals and sicker patients, contemporary population- based data from the AHRQ's Nationwide Inpatient Sample (NIS) will also be utilized for this project. Hospital mortality after admission is expected to vary systematically among institutions in either database, even after adjustments for injury type and severity, comorbidity, and age. Optimal combinations of patient covariates will be sought to explain this primary outcome. The residual variability will be evaluated in order to identify hospital characteristics associated with improved survival and individual institutions with exceptional results. Other outcomes (length of stay, discharge to long-term care) will also be evaluated. Multilevel (hierarchical) models will be used for statistical analysis. These models have become more widely applied to health care research and are now a standard way to account for clustered data structures (e.g., patients within hospitals). They reduce spurious outlier identification resulting from small sample sizes, while at the same time providing more valid estimates of the independent effects of patient and hospital characteristics. Estimations and summary statistics using the new databases, variables, and statistical models will be compared with results from traditional methods. This project is designed to produce a credible and verifiable data management structure and analytic methodology that can be used for the continuing evaluation of trauma care by the ACS, AHRQ, and others.