Improving Pediatric Trauma Triage Using High Dimensional Data Analysis

使用高维数据分析改进儿科创伤分诊

• 批准号: 8111093
• 负责人: RANDALL S. BURD
• 金额: $ 23.71万
• 依托单位: CHILDREN'S RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8111093
• 关键词: Accident and Emergency department; Accounting; Adolescent; Affect; Age; Anatomic Sites; Caring; Cessation of life; Characteristics; Child; Childhood; Classification; Complex; Data; Data Analyses; Data Set; Databases; Goals; Hospitals; Individual; Infant; Injury; Lead; Methods; Modeling; Morbidity - disease rate; Outcome; Patients; Performance; Physiological; Probability; Regression Analysis; Resources; Risk; Sample Size; Severities; System; Testing; Time; Trauma; Triage; Vehicle crash; base; clinical practice; cost; data acquisition; improved; injured; innovation; mortality; novel; novel strategies; pediatric trauma; preclinical study; predictive modeling; public health relevance; response; response to injury; trauma centers

DESCRIPTION (provided by applicant): Severely injured children achieve the best outcomes when treated at centers that provide specialized pediatric trauma care. Assessing the need for high-level trauma care is a complex classification problem that is affected by a very large number of potentially interacting factors (high-dimensional data), including age, mechanism of injury, known or suspected injuries and the physiological responses to injury. Despite this known complexity, approaches to pediatric trauma triage have been based on expert-derived rules, partly because of challenge of data acquisition in a prehospital setting. New approaches to data acquisition, however, are being rapidly introduced that allow access to increasing amounts of data at the injury scene and during transport, replacing the challenge of data capture with that of managing large numbers of explanatory variables. The overall goal of this project is to develop a triage system that increases the likelihood that injured children are treated at hospitals with the capability of optimizing outcome after injury. The purpose of this proposal is to develop more accurate methods for predicting the outcome and resource needs of injured children based on data available in prehospital and emergency department settings. We hypothesize that the relationship between observable prehospital and early hospital features (patient characteristics, physiologic status, anatomic sites of injury, mechanism of injury and prehospital treatments) and the need for and level of care required for injured children is highly complex, requiring approaches for modeling high-dimensional data to achieve accurate prediction. This hypothesis will be tested in two aims: 1. compare the impact of low- and high-dimensional data on the performance of models predicting time-dependent outcomes and resource utilization after pediatric injury; 2. build high-dimensional multivariate probability models that predict outcomes after pediatric injury using data from individual injury datasets and integrated data from heterogeneous injury datasets. The hypothesis to be tested under Aim 1 is that prediction of time-dependent outcomes and resource utilization after pediatric injury will be improved by modeling high-dimensional data. Aim 1 will be pursued using data obtained from two national trauma databases to develop and compare models based on low- and high-dimensional data. This aim will require extending our innovative approach to high-dimensional regression analysis to handle time- dependent response variables and competing risks. The hypothesis to be tested under Aim 2 is that prediction of outcomes after pediatric injury will be improved using integrated data obtained from heterogeneous injury datasets. Aim 2 will be pursued using a motor vehicle crash dataset and a trauma database to develop multivariate probability models based on data from each dataset and integrated data from both datasets. This aim will require developing novel approaches for building Bayesian graphical models from distributed high- dimensional data. This proposal will bridge gaps in our understanding of the impact of domain complexity on the accuracy of prediction in prehospital and emergency department settings. PUBLIC HEALTH RELEVANCE: Severely injured children achieve the best outcomes when treated at hospitals that provide specialized pediatric trauma care. Determining the need for high-level pediatric trauma care is a complex classification problem that is influenced by a very large number of potentially interacting factors, including age, mechanism of injury, known or suspected injuries and the physiological responses to injury. In this proposal, novel statistical approaches that account for this complexity will be developed for more accurately predicting the need for high-level pediatric trauma care among injured children.

描述（由申请人提供）：在提供专门的儿科创伤护理的中心治疗时，严重受伤的儿童获得了最佳结果。评估对高级创伤护理的需求是一个复杂的分类问题，受到大量潜在相互作用因素（高维数据）的影响，包括年龄，损伤机制，已知或怀疑的伤害以及对损伤的生理反应。尽管有这种已知的复杂性，但小儿创伤分类的方法仍基于专家衍生的规则，部分原因是在院前环境中挑战数据获取的挑战。但是，正在迅速引入数据采集的新方法，以允许在伤害现场和运输过程中访问增加数据量的数据，从而用管理大量解释变量的挑战来代替数据捕获的挑战。该项目的总体目标是开发一个分类系统，以增加受伤儿童在医院接受治疗的可能性，并有能力优化受伤后的结果。该提案的目的是根据院前和急诊室环境中的数据开发更准确的方法来预测受伤儿童的结果和资源需求。我们假设可观察到的院前和早期医院特征（患者特征，生理状态，解剖学位置的损伤，损伤机制和医学前治疗机制）与受伤儿童所需的护理水平和水平是高度复杂的，需要对高维数据进行建模以实现准确的预测。该假设将以两个目的进行检验：1。比较低维数据对预测小儿损伤后时间依赖性结果和资源利用的模型性能的影响； 2。建立高维的多元概率模型，该模型使用来自单个损伤数据集中的数据和来自异质损伤数据集的集成数据来预测小儿损伤后的结局。在目标1下进行检验的假设是，通过建模高维数据来改善小儿损伤后时间依赖性结果和资源利用的预测。将使用从两个国家创伤数据库获得的数据来追求目标1，以开发和比较基于低维数据和高维数据的模型。这个目标将需要扩展我们的创新方法来进行高维回归分析，以处理时间依赖的响应变量和竞争风险。在AIM 2下进行检验的假设是，使用从异质损伤数据集获得的综合数据可以改善小儿损伤后结果的预测。 AIM 2将使用汽车撞车数据集和创伤数据库来追求AIM 2，以根据每个数据集中的数据以及两个数据集的集成数据开发多元概率模型。这个目标将需要开发新的方法来从分布式高维数据中构建贝叶斯图形模型。该提案将弥合我们对域复杂性对院前和急诊部设置预测准确性的影响的理解的差距。 公共卫生相关性：严重受伤的儿童在提供专门的儿科创伤护理的医院接受治疗时取得了最佳成果。确定对高级小儿创伤护理的需求是一个复杂的分类问题，受到大量潜在相互作用因素的影响，包括年龄，损伤机制，已知或怀疑的损伤以及对损伤的生理反应。在此提案中，将开发出解决这种复杂性的新型统计方法，以更准确地预测受伤儿童中高级小儿创伤护理的需求。