GEMRA: Geriatric Emergency Medicine Risk Prediction Model for Return VisitAdmissions

GEMRA：老年急诊医学回访住院风险预测模型

基本信息

批准号：
10587202
负责人：
Peter Arthur DeBlieux Steel
金额：
$ 65.36万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2028-02-29
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10587202
关键词：
Accident and Emergency department Acute Admission activity Adult Assessment tool Caring Categories Classification Clinical Clinical Data Cognitive Communicable Diseases Complement Complex Critical Care Data Decision Making Diagnosis Disease Disease Progression Elderly Electronic Health Record Emergency Care Emergency Department patient Emergency Medicine Emergency Situation Emergency department screening Emergency department visit Ensure Environment Ethnic Origin Etiology Event Feasibility Studies Geography Geriatric Assessment Goals Guidelines Health system Hospital Mortality Hospitalization Hospitals Hour Informatics Intervention Interview Judgment Leadership Link Machine Learning Managed Care Manuals Medicare claim Methods Modeling Morbidity - disease rate Operative Surgical Procedures Outcome Output Patient Admission Patient Noncompliance Patient-Focused Outcomes Patients Performance Physicians Population Practice Guidelines Process Recommendation Research Risk Risk Assessment Risk Factors Screening procedure Severities Site Structure Subgroup Surveys Technology Testing Time Translating Validation Variant Visit Vulnerable Populations Work adverse outcome arm clinical care clinical decision support clinical practice comorbidity comparative design experience health data health record high risk implementation design implementation study improved improved outcome innovation interdisciplinary collaboration machine learning model machine learning prediction mortality multidisciplinary novel patient subsets predict clinical outcome predictive modeling prospective prototype randomized, clinical trials risk mitigation risk prediction model screening social health determinants socioeconomics support tools theories tool usability

项目摘要

Summary: At least 400 older adults a day are discharged from US emergency departments (EDs) and within 72 hours experience a return ED visit resulting in hospital admission (RVA). Geriatric RVA have dramatically higher morbidity and mortality than patients admitted to hospital on their initial ED visit. These outcomes, combined with the clinical complexity of geriatric presentations, demonstrate a critical need for clinical decision support (CDS) for ED discharge decisions and improved post-ED care management in older adults. National guidelines recommend that all older adults receive formal risk screening in the ED. Existing geriatric ED risk assessment tools lack predictive validity and are not designed to identify the multifactorial risk of an RVA event within 72 hours after ED discharge. Our long-term goal is to improve the outcomes of older adults using machine learning models for clinical decision support (CDS) in emergency medicine. The goal of this study is to develop and validate a machine learning model that predicts geriatric emergency medicine 72-hour RVA (GEMRA), and can be used as a feasible ED CDS tool. In order to maximize the impact and generalizability of GEMRA across a wide range of US ED environments and populations, the model input variables used will be clinical data collected in the course of normal clinical care, and thus widely available in emergency health records (EHRs). GEMRA will be developed and validated with data from five diverse hospitals across two health systems that span a wide range of demographic, socioeconomic, and ethnic backgrounds. The study will be conducted by a closely collaborating interdisciplinary team that includes emergency medicine, machine learning, and CDS experts, with extensive experience in geriatric emergency medicine research as well as developing and evaluating technological driven interventions to improve post-ED outcomes. Our preliminary work demonstrates that an early machine learning model using 478 clinical data input variables can accurately identify ED patients at high risk of RVA, outperforming an existing, unvalidated traditional RVA risk score that used six clinically derived risk factors. Our specific aims include: (1) Optimize GEMRA through model refinement, validation with retrospective data from unseen populations, as well as explanation of model performance variation across different clinical subgroups; (2) Assess GEMRA's clinical value through prospective validation at three different hospitals, comparing model performance to existing ED geriatric and RVA risk tools, as well as real-time clinician judgment; (3) Engage multidisciplinary stakeholders in the design of both a GEMRA CDS prototype and a complementary multidisciplinary clinical RVA risk assessment workflow; and subsequently evaluate the feasibility of these products in ED clinical practice during a short-term pilot implementation study. Completion of these aims could transform older adult post-ED risk screening, leveraging the computational power and scalability of machine learning to identify patients at risk of early post-ED adverse outcomes. Subsequent implementation of GEMRA CDS would inform risk-mitigating interventions, potentially impacting outcomes in this vulnerable population.

概括：美国急诊科 (ED) 每天至少有 400 名老年人在 72 小时内出院经历急诊室回访并导致入院 (RVA)。老年人的 RVA 显着更高发病率和死亡率高于首次急诊就诊时入院的患者。这些结果，结合老年病临床表现的复杂性表明对临床决策支持 (CDS) 的迫切需求用于 ED 出院决策和改善老年人的 ED 后护理管理。国家指南建议所有老年人在急诊室接受正式的风险筛查。现有老年 ED 风险评估工具缺乏预测有效性，并且并非旨在识别 72 年内 RVA 事件的多因素风险 ED 出院后数小时。我们的长期目标是利用机器学习改善老年人的结果急诊医学临床决策支持（CDS）模型。本研究的目标是开发和验证预测老年急诊医学 72 小时 RVA (GEMRA) 的机器学习模型，并且可以可以作为可行的 ED CDS 工具。为了最大限度地提高 GEMRA 的影响力和普遍性广泛的美国急诊环境和人群，使用的模型输入变量将是收集的临床数据在正常临床护理过程中，因此可以在紧急健康记录（EHR）中广泛使用。吉姆拉将利用来自两个卫生系统的五家不同医院的数据进行开发和验证，这些医院跨越了广泛的领域人口、社会经济和种族背景的范围。该研究将由一个密切合作的机构进行跨学科合作团队，包括急诊医学、机器学习和 CDS 专家，在老年急诊医学研究以及开发和评估方面拥有丰富的经验技术驱动的干预措施，以改善 ED 后的结果。我们的初步工作表明使用 478 个临床数据输入变量的早期机器学习模型可以准确识别高危 ED 患者 RVA 风险，优于现有的、未经验证的传统 RVA 风险评分（使用六种临床衍生风险）因素。我们的具体目标包括： (1) 通过模型细化、回顾性验证来优化 GEMRA 来自未见人群的数据，以及不同临床模型性能差异的解释亚组； (2) 通过在三个不同医院的前瞻性验证来评估 GEMRA 的临床价值，将模型性能与现有 ED 老年和 RVA 风险工具以及实时临床医生判断进行比较； (3) 让多学科利益相关者参与 GEMRA CDS 原型和补充方案的设计多学科临床 RVA 风险评估工作流程；并随后评估这些措施的可行性在短期试点实施研究期间将产品应用于急诊临床实践。完成这些目标可以利用机器的计算能力和可扩展性，转变老年人急诊后风险筛查学习识别处于 ED 后早期不良后果风险的患者。 GEMRA的后续实施 CDS 将为降低风险的干预措施提供信息，从而可能影响这一弱势群体的结果。