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

• 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
• 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

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.

摘要： 每天至少有400名老年人在72小时内从美国急诊科出院 经历急诊室回访，导致入院(RVA)。老年人的RVA显著高于 发病率和死亡率高于首次急诊时入院的患者。这些结果，再加上 老年病临床表现的复杂性，表明了对临床决策支持(CDS)的迫切需求 用于老年人的ED出院决策和改善ED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。现有的老年急诊风险评估 工具缺乏预测性有效性，并且不能识别72小时内RVA事件的多因素风险 艾德出院后数小时。我们的长期目标是使用机器学习来改善老年人的结果 急诊医学临床决策支持(CDS)模型。这项研究的目标是开发和 验证预测老年急救药物72小时RVA(GEMRA)的机器学习模型，并可以 可作为一种可行的ED CDS工具。为了最大限度地提高全球环境影响评估的影响和推广能力 在广泛的美国ED环境和人群中，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，并因此在紧急健康记录(EHR)中广泛可用。GEMRA 将使用来自两个医疗系统的五家不同医院的数据进行开发和验证，这些系统跨越了广泛的 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 包括急诊医学、机器学习和CDS专家在内的跨学科协作团队， 具有丰富的老年急诊医学研究、开发和评估经验 技术驱动的干预措施，以改善教育后的结果。我们的初步工作表明，一个 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血压性ED患者 RVA的风险，超过使用六个临床派生风险的现有的、未经验证的传统RVA风险得分 各种因素。我们的具体目标包括：(1)通过模型改进、追溯验证来优化GEMRA 来自看不见的人群的数据，以及不同临床情况下模型性能差异的解释 (2)通过在三家不同的医院进行前瞻性验证来评估GEMRA的临床价值。 将模型性能与现有的ED老年病和RVA风险工具以及实时临床医生判断进行比较； (3)让多学科利益攸关方参与设计GEMRA CDS原型和补充方案 多学科临床RVA风险评估工作流程；并随后评估这些工作流程的可行性 产品在急诊科临床实践期间进行了短期中试实施研究。完成这些目标可以 利用计算机的计算能力和可扩展性，转变老年人ED后风险筛查 学会识别有早期ED后不良后果风险的患者。全球环境影响评估的后续实施 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。