PED Screen: Pediatric Sepsis EHR Registry, Clinical Outcomes, and Predictive Model

PED 筛查：儿科败血症 EHR 登记、临床结果和预测模型

• 批准号: 9419932
• 负责人: Elizabeth Rachel Alpern
• 金额: $ 62.39万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9419932
• 关键词: Accident and Emergency department; Achievement; Acute; Address; Adherence; Algorithms; Applied Research; Blood Pressure; Caring; Child; Childhood; Clinical; Clinical Data; Clinical Trials Design; Complex; Data; Data Sources; Derivation procedure; Detection; Diagnostic; Diagnostic Procedure; Disease; Early Intervention; Electronic Health Record; Emergency Care; Emergent care; Foundations; Functional disorder; Funding; Future; Goals; Health Expenditures; Healthcare; Hospitals; Hour; Inpatients; Intervention; Life; Link; Machine Learning; Manuals; Measurement; Measures; Methodology; Methods; Morbidity - disease rate; Natural Language Processing; Organ; Organ failure; Outcome; Outcome Measure; Patient-Focused Outcomes; Patients; Positioning Attribute; Process; Quality of Care; Registries; Research; Research Infrastructure; Resources; Risk; Risk Factors; Sampling; Savings; Sepsis; Shock; Site; Structure; Time; United States; United States Agency for Healthcare Research and Quality; Visit; Work; base; cohort; comparative effectiveness; data registry; design; effective intervention; electronic registry; high risk; improved; innovation; member; mortality; novel; patient registry; pediatric emergency; pediatric patients; predictive modeling; repository; septic patients; support tools; wasting

Project Summary/Abstract Each year over 75,000 children develop sepsis in the United States resulting in substantial morbidity, up to 20% mortality, and billions in US health care expenditures. There have been substantial advances that demonstrate improved patient outcomes with adherence to early aggressive emergency care. However, methods to accurately, reliably, and rapidly identify children who require these resource-intensive therapies are lacking. In addition, understanding the impact of these therapies on near-term outcomes, before significant morbidity occurs, is also lacking. Current algorithms do not reliably discriminate between patients who develop sepsis and those who are clinically similar upon initial presentation but do not progress to sepsis. As a result, children requiring life-saving treatments do not receive them, or do not receive them in a timely fashion, and others may be over-treated, wasting healthcare resources and potentially diverting emergency care from those in need. With the advent of electronic health records (EHR), there are now information-enabled solutions that offer unique opportunities to identify non-biased, heterogeneous samples of children and allow us to accurately and reliably measure risk factors and near-term outcomes for sepsis. This work addresses the critical need to improve pediatric sepsis outcomes by developing methods to accurately identify at-risk children presenting for emergency care. Utilizing the infrastructure of the Pediatric Emergency Care Applied Research Network (PECARN), this proposal will innovatively capture EHR data to create a multi-center registry with the ultimate goal to improve the detection and treatment of pediatric sepsis in the ED setting. To accomplish this, we propose the following specific aims: We will develop an expanded multicenter sepsis registry for pediatric patients from merged electronic health record clinical data from different hospitals with different EHR data sources. We will automate the determination of organ dysfunction in children with sepsis directly from structured and narrative data within the multicenter EHR registry. From the registry and outcome data, we will derive and validate a prediction model of pediatric sepsis using emergency department EHR data from the first 4 hours of care that predicts subsequent organ dysfunction within 48 hours. Each of these aims works to the goal of improving the emergent care for pediatric sepsis with innovative deliverables from this project including the existence of a broad and rich EHR registry, an automated process of outcome determination, and a prediction model of risk of sepsis. We will also have a strong foundation for future projects to implement and evaluate decision support tools, improve diagnostic techniques, engage in comparative effectiveness studies, measure quality of care, establish linked bio-repositories, and guide clinical trial design. The proposed project, thus, has enormous potential to improve our ability to improve the quality of care provided to our most acutely ill children.

项目总结/摘要 在美国，每年有超过75，000名儿童发展败血症，导致大量的发病率，高达 20%的死亡率，以及数十亿美元的美国医疗保健支出。已经取得了实质性的进展， 证明了通过坚持早期积极的急救护理改善了患者的预后。然而，在这方面， 准确、可靠和快速识别需要这些资源密集型治疗的儿童的方法是 缺乏此外，了解这些治疗对近期结果的影响，在显著性 发病率，也缺乏。目前的算法不能可靠地区分患有 脓毒症和那些在最初表现时临床相似但未进展为脓毒症的患者。因此，在本发明的一个方面， 需要救生治疗的儿童得不到治疗，或得不到及时治疗， 其他人可能被过度治疗，浪费医疗资源，并可能转移紧急护理， 有需要的随着电子健康记录（EHR）的出现，现在有一些信息化的解决方案， 提供了独特的机会，以确定无偏见，异质样本的儿童，并使我们能够准确地 并可靠地测量脓毒症的风险因素和近期结果。这项工作满足了以下迫切需要： 通过开发准确识别高危儿童的方法来改善儿科脓毒症的预后， 紧急护理。利用儿科急救护理应用研究网络的基础设施 （PECARN），该提案将创新性地捕获EHR数据，以创建一个多中心注册中心， 目的是改善艾德环境中儿科败血症的检测和治疗。为了做到这一点，我们 提出以下具体目标：我们将开发一个扩展的多中心脓毒症登记， 来自具有不同EHR数据的不同医院的合并电子健康记录临床数据的患者 源我们将自动化确定器官功能障碍的儿童败血症直接从 多中心EHR注册中的结构化和叙述性数据。根据登记和结局数据，我们将 使用第一次急诊科EHR数据推导并验证儿科脓毒症的预测模型 4小时的护理可预测48小时内随后的器官功能障碍。这些目标中的每一个都致力于 通过本项目的创新成果改善儿科脓毒症的紧急护理的目标，包括 广泛而丰富的EHR注册表的存在，结果确定的自动化过程，以及 脓毒症风险的预测模型。我们还将为未来的项目实施奠定坚实的基础， 评估决策支持工具，改进诊断技术，进行比较有效性研究， 衡量护理质量，建立相关的生物库，指导临床试验设计。拟议的项目， 因此，有巨大的潜力，以提高我们的能力，以改善护理质量提供给我们最严重的 生病的孩子