Improving Pediatric Trauma Triage Using High Dimensional Data Analysis

描述(由申请人提供)：严重受伤的儿童在提供专门的儿科创伤护理的中心接受治疗时，效果最好。评估对高级别创伤护理的需求是一个复杂的分类问题，它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、已知或可疑损伤以及对损伤的生理反应。尽管有这种已知的复杂性，儿科创伤分诊的方法一直基于专家派生的规则，部分原因是在院前环境中获取数据的挑战。然而，新的数据采集方法正在迅速推出，允许在受伤现场和运输过程中获取越来越多的数据，用管理大量解释变量的挑战取代了数据采集的挑战。该项目的总体目标是开发一种分类系统，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局。这项提议的目的是根据院前和急诊科提供的数据，开发更准确的方法来预测受伤儿童的结局和资源需求。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、损伤解剖部位、损伤机制和院前治疗)与受伤儿童的需要和所需护理水平之间的关系非常复杂，需要建立高维数据模型的方法来实现准确的预测。这一假设将在两个目标下进行检验：1.比较低维和高维数据对预测儿科损伤后随时间变化的结果和资源利用的模型的影响；2.使用来自单个损伤数据集的数据和来自不同类型损伤数据集的综合数据，构建预测儿科损伤后结果的高维多变量概率模型。在目标1下要检验的假设是，通过对高维数据进行建模，将改进对儿科损伤后随时间变化的结果和资源利用的预测。将利用从两个国家创伤数据库获得的数据来实现目标1，以开发和比较基于低维和高维数据的模型。这一目标将需要将我们的创新方法扩展到高维回归分析，以处理依赖时间的响应变量和竞争风险。在目标2下要检验的假设是，使用从不同类型的损伤数据集获得的综合数据，将改进对儿科损伤后结果的预测。将利用机动车碰撞数据集和创伤数据库来实现目标2，以每个数据集的数据和这两个数据集的综合数据为基础建立多变量概率模型。这一目标将需要开发新的方法来从分布式高维数据建立贝叶斯图形模型。这项建议将弥补我们在理解领域复杂性对院前和急诊科设置中预测准确性的影响方面的差距。 公共卫生相关性：严重受伤的儿童在提供儿科创伤专门护理的医院接受治疗时，可获得最佳结果。确定是否需要高水平的儿科创伤护理是一个复杂的分类问题，它受到大量潜在相互作用因素的影响，包括年龄、损伤机制、已知或可疑损伤以及对损伤的生理反应。在这项提案中，将开发考虑这种复杂性的新的统计方法，以更准确地预测受伤儿童对高水平儿科创伤护理的需求。

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.