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业儿科创伤护理的中心接受治疗时效果最好。评估高水平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、已知或疑似损伤以及对损伤的生理反应。尽管这种已知的复杂性，儿科创伤分诊的方法是基于专家衍生的规则，部分原因是院前设置的数据采集的挑战。然而，正在迅速引入新的数据采集方法，这些方法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诊系统，提高受伤儿童在医院接受治疗的可能性，并具有优化受伤后结果的能力。本建议的目的是根据院前和急诊科设置的现有数据，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，可观察到的院前和院前早期特征（患者特征、生理状态、损伤解剖部位、损伤机制和院前治疗）与受伤儿童的护理需求和护理水平之间的关系非常复杂，需要采用高维数据建模的方法来实现准确预测。这一假设将在两个方面得到验证：1。比较低维和高维数据对预测儿童损伤后时间依赖性结局和资源利用的模型性能的影响；2. 建立高维多变量概率模型，利用来自个体损伤数据集的数据和来自异构损伤数据集的综合数据来预测儿童损伤后的结果。Aim 1需要验证的假设是，通过对高维数据进行建模，可以提高对儿童损伤后时间依赖性结局和资源利用的预测。目标1将使用从两个国家创伤数据库获得的数据来开发和比较基于低维和高维数据的模型。这一目标需要将我们的创新方法扩展到高维回归分析，以处理随时间变化的响应变量和竞争风险。Aim 2中需要验证的假设是，使用来自异质损伤数据集的综合数据可以改善对儿童损伤后预后的预测。目标2将使用机动车碰撞数据集和创伤数据库来开发基于每个数据集的数据和来自两个数据集的综合数据的多元概率模型。这个目标将需要开发新的方法来从分布的高维数据建立贝叶斯图形模型。该建议将弥合我们对院前和急诊科设置中预测准确性的领域复杂性影响的理解差距。

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.