Identifying Electronic Phenotypes associated with Patient Health Outcomes of Interhospital Transfer Patients

项目总结/摘要 在美国，每年约有160万患者接受医院间转移（IHT）。这些 大约550，000次通过空气进行的IHT（即，直升机或喷气式飞机），或大约每60秒一次， 估计每年要花费60亿美元。与普遍的看法相反，接受IHT的患者 更糟糕的结果，其中包括两倍的住院时间，两倍的费用，和更高的死亡率比非 转移病人。虽然更差的结果不一定是由于疾病的严重程度更高，但因素 导致不利结果的因素尚未确定。对于大约30%的患者来说， 立即危及生命的情况，如创伤或心脏病发作，立即通过空气进行IHT是有益的;但对于 另外70%的患者没有遇到危及生命的情况，其益处不太清楚。 目前关于谁应该被转移以及如何转移（空中还是地面）的决策过程是 很少被证据或指导方针所引导。此外，患者和家属很少有关于如何 病人被转移。需要有经验证据来确定国际卫生措施的有意义的指标，并指导国际卫生措施的实施。 关于转让方式的决定。以前的研究工作几乎完全依赖于后- 疾病的转运阶段，在调查导致患者需要转移到 另一家医院。为了调查导致IHT的因素，我们为以下患者开发了一个数据库： 从一家医院转到另一家医院。该存储库包括以下人员的电子病历： 直升机和喷气式飞机转运，以及发送和接收医院EMR数据。 本研究的目的是建立以患者为中心的复杂数据模型， 通过鉴定转运前电子表型，将从IHT中获益的患者，并确定 他们将从空中和地面转移中受益。为了充分利用数据存储库，本研究将采用 一种利用统计学习技术实现以下目标的数据科学方法：1）识别 并对合并症、活动性医学问题和生理不稳定指标的特定组合进行排名 根据频率和对IHT后死亡率的影响，以及2）识别IHT患者的电子表型 根据健康状况（出院状态和功能状态）从空气转移中受益。 在进行探索性数据分析并通过重复测量减少变量后，我们将采用 关联规则，用于识别协变量的重要组合，以纳入最终模型开发（Aim 1）。然后在目标2中，我们将使用随机森林从所有可用的变量中识别最具影响力的变量。 将通过分类和回归树分析的变量，以识别不同的患者亚组 通过空运从国际人道主义运输中受益。通过这种数据驱动方法确定的个体特征 将提供支持未来应用所需的证据，以开发临床决策支持， 临床医生、患者和家属在做出转移决定时。

Project Summary/Abstract Approximately 1.6 million patients undergo interhospital transfer (IHT) each year in the United States. Of those IHTs, approximately 550,000 are conducted by air (i.e., helicopters or jets), or roughly one every 60 seconds, at an estimated annual cost of $6 billion. Contrary to common belief, patients who undergo IHT experience worse outcomes, which include double the length of stay, twice the cost, and higher mortality than non- transferred patients. While worse outcomes are not necessarily due to higher severity of illness, factors contributing to unfavorable outcomes have yet to be identified. For about 30% of the patients experiencing an immediately life-threatening condition such as trauma or heart attack, immediate IHT by air is beneficial; but for the other 70% of patients not experiencing a life-threatening condition, the benefit is less clear. The current decision-making process regarding who should be transferred and how (air vs. ground) is rarely guided by evidence or guidelines. Furthermore, patients and families rarely have input regarding how a patient is transferred. Empirical evidence is needed to identify meaningful indicators for IHT and to guide the decision regarding mode of transfer. Previous research efforts relied almost exclusively on data from the post- transport phase of illness, a major limitation when investigating what leads up to a patient needing transfer to another hospital. To investigate the factors that lead to IHT, we developed a data repository for patients who are transported from one hospital to another. This repository includes the electronic medical record of helicopter and jet transfers, as well as the sending and receiving hospital EMR data. The purpose of this study is to model complex patient-centered data that may predict those patients that will benefit from IHT by identifying pre-transport electronic phenotypes, and to determine who will benefit from air versus ground transfer. To fully leverage the data repository, this study will employ a data science approach that leverages statistical learning techniques to achieve the following aims: 1) Identify and rank specific combinations of comorbidities, active medical problems, and physiologic instability indicators according to frequency and impact on post-IHT mortality, and 2) Identify electronic phenotypes of IHT patients that benefit from air transfer according to health outcomes (hospital discharge status and functional status). After conducting exploratory data analysis and reducing variables with repeated measures, we will employ Association Rules to identify significant combinations of covariates to include in final model development (Aim 1). Then in Aim 2 we will use Random Forest to identify the most impactful variables from all of the available variables that will be analyzed via Classification and Regression Tree to identify distinct subgroups of patients that benefit from IHT by air transport. The individual characteristics identified from this data driven approach will provide the evidence needed to support future applications to develop clinical decision support for use by clinicians, patients, and families when making transfer decisions.