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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.

总结：每天至少有400名老年人从美国急诊室（ED）出院，再次接受艾德访视，导致住院（RVA）。老年RVA显著高于发病率和死亡率高于初次艾德就诊时入院的患者。这些成果，加上老年病临床表现的复杂性，证明了对临床决策支持（CDS）的迫切需求用于老年人的艾德出院决定和改善的艾德后护理管理。国家指导方针建议所有老年人在急诊室接受正式的风险筛查。现有的老年艾德风险评估工具缺乏预测有效性，并且设计不用于在72 艾德出院后10小时。我们的长期目标是使用机器学习改善老年人的结果急诊医学中的临床决策支持模型。本研究的目的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，作为一个可行的艾德CDS工具。为了最大限度地发挥GEMRA在全球范围内的影响力和普遍性广泛的美国艾德环境和人群，使用的模型输入变量将是收集的临床数据在正常的临床护理过程中，因此在紧急健康记录（EHR）中广泛可用。GEMRA 将开发和验证来自两个卫生系统的五家不同医院的数据，一系列的人口、社会经济和种族背景。这项研究将由一个密切合作的跨学科团队，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具有丰富的经验，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地识别高风险的艾德患者。 RVA的风险，优于现有的，未经验证的传统RVA风险评分，该评分使用六种临床衍生风险因素我们的具体目标包括：（1）通过模型的细化、验证和追溯，优化GEMRA 来自未知人群的数据，以及不同临床模型性能差异的解释（2）通过在三家不同医院的前瞻性验证评估GEMRA的临床价值，将模型性能与现有的艾德老年和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原型和补充性多学科临床RVA风险评估工作流程;随后评估这些风险评估的可行性。产品在艾德临床实践中的短期试点实施研究。实现这些目标，改变老年人ED后风险筛查，利用机器的计算能力和可扩展性学习识别有早期ED后不良结局风险的患者。GEMRA的后续实施 CDS将为降低风险的干预措施提供信息，可能会影响这一弱势群体的结果。