Improving the efficiency and equity of critical care allocation during a crisis with place-based disadvantage indices

利用基于地点的劣势指数提高危机期间重症监护分配的效率和公平性

• 批准号: 10638835
• 负责人: William F Parker
• 金额: $ 48.35万
• 依托单位: UNIVERSITY OF CHICAGO
• 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 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10638835
• 关键词: Adult; Algorithm Design; Algorithms; Area; Black Populations; COVID-19 pandemic; Caring; Chicago; Clinical Data; Creatinine; Critical Care; Critical Illness; Data; Databases; Disadvantaged; Disasters; Disparity population; Dose; Electronic Health Record; Ensure; Environment; Equation; Equity; Ethics; Ethnic Origin; Ethnic Population; Ethnic equity; Evaluation; Event; Geography; Healthcare; Hospitals; Inequity; Intensive Care Units; Kidney; Laboratories; Legal; Life; Mathematics; Measures; Mechanics; Modeling; Monitor; Nature; Neighborhoods; Organ failure; Outcome; Patients; Performance; Pharmaceutical Preparations; Politics; Population Distributions; Probability; Protocols documentation; Public Health; Publishing; Race; Records; Resource Allocation; Resources; Risk; Scoring Method; Series; Site; Structure; System; Testing; Time; Triage; US State; Validation; black patient; cohort; coronavirus disease; data enclave; deprivation; design; ethnic minority population; evaluation/testing; falls; health care disparity; improved; indexing; models and simulation; mortality risk; neighborhood disadvantage; novel; open source; organ allocation; public health ethics; racial bias; racial minority population; racial population; residential segregation; risk prediction; simulation; simulation software; social; social disparities; social vulnerability; survival prediction; systematic review; treatment response

PROJECT SUMMARY When a US hospital system is overwhelmed by disaster, Crisis Standards of Care guide the triage teams forced to choose which patients receive scarce life support treatments. Analogous to an organ allocation system, these algorithms convert ethical principles into a concrete rank ordering of candidates for Intensive Care Unit (ICU) treatments with life support allocation scores. Disasters that produce scarcity tend to fall hardest on disadvantaged communities, especially racial and ethnic minority groups. When designing algorithms to allocate scarce life support, public health officials should take this context into account. In an attempt to identify the critically ill patients with the highest likelihood of benefit from treatment, most US states would prioritize those with low Sequential Organ Failure Assessment (SOFA) scores. But SOFA was designed for patients already on life support in the ICU, using routinely measured laboratory values, drug doses, and vital signs to monitor response to treatment. Most patients have low SOFA scores when critical illness is first recognized, and SOFA cannot accurately predict the risk of death using data before life support was allocated. We demonstrated how the poor predictive performance of SOFA-based triage protocols is partially explained by underpredicting the survival of Black patients due to a miscalibrated renal component of the SOFA score. There is a clear need to develop and validate a novel life support allocation protocol designed to debias existing scores and save more lives. Place-based disadvantage indices, such as the Area Deprivation Index (ADI) and the Social Vulnerability Index, offer a potential solution. Using these validated geographical measures of neighborhood deprivation to allocate scarce healthcare resources counteracts the risk-increasing effects of social disadvantage, including disadvantage produced by racialized residential segregation. We hypothesize that a well-designed life support allocation score using place-based disadvantage indices can save more lives and mitigate healthcare inequity in a crisis. The overall objective of this project is to develop a life support allocation algorithm that accurately and equitably allocates scarce ICU treatments in a crisis. In Aim 1, we will use structural equation modeling to create an Equitable Life Support Allocation (ELSA) score, using place-based disadvantage indices to debias SOFA. In Aim 2, develop the ICU Crisis Simulation Model (ICSM), a discrete event simulation that models patient flow and survival, as a testing and evaluation environment for life support allocation protocols. In Aim 3, we will externally validate ELSA and ICSM in the National COVID Cohort Collaborative Data Enclave, which currently contains geocoded records from 14 million patients from 74 sites. Our project will address one of the most pressing challenges in applied public health ethics, producing 1) an empirically derived score to distribute life support more accurately and equitably in a crisis and 2) open-source simulation software to evaluate the efficiency and equity of life support allocation protocols.

项目摘要 当美国医院系统被灾难淹没时，危机护理标准指导分流团队被迫 选择哪些病人接受稀缺的生命支持治疗。类似于器官分配系统， 算法将伦理原则转换为重症监护病房（ICU）候选人的具体排名 生命支持分配评分。造成资源短缺的灾难往往是最严重的 弱势群体，特别是少数种族和族裔群体。当设计算法来分配 由于缺乏生命支持，公共卫生官员应考虑到这一情况。 为了确定最有可能从治疗中获益的重症患者， 大多数美国州将优先考虑那些具有低序贯器官衰竭评估（SOFA）分数的人。但 SOFA是为已经在ICU接受生命支持的患者设计的，使用常规测量的实验室值， 药物剂量和生命体征，以监测对治疗的反应。大多数患者的SOFA评分较低， 危重症首先被识别，SOFA无法使用生前数据准确预测死亡风险 支持已分配。我们证明了基于SOFA的分诊协议的预测性能差 部分原因是由于肾脏成分校准错误而低估了黑人患者的生存率 SOFA评分。显然需要开发和验证一种新的生命支持分配协议 旨在消除现有分数的偏见并拯救更多生命。基于地点的劣势指数，例如 地区脆弱性指数和社会脆弱性指数提供了一种可能的解决办法。使用这些 有效的邻里剥夺地理措施，以分配稀缺的医疗资源 消除社会不利因素，包括种族歧视造成的不利因素， 居住隔离。我们假设，一个设计良好的生命支持分配分数，使用基于地点的 劣势指数可以挽救更多的生命，并减轻危机中的医疗不平等。 该项目的总体目标是开发一种生命支持分配算法， 在危机中公平分配稀缺的ICU治疗。在目标1中，我们将使用结构方程模型来 创建一个公平的生命支持分配（艾尔莎）评分，使用基于地点的劣势指数来消除偏见 沙发在目标2中，开发ICU危机模拟模型（ICSM），这是一种离散事件模拟， 病人流量和生存，作为生命支持分配协议的测试和评估环境。在目标3中， 我们将在国家COVID队列协作数据飞地中对艾尔莎和ICSM进行外部验证， 目前包含来自74个站点的1400万患者的地理编码记录。我们的项目将解决一个 应用公共卫生伦理学中最紧迫的挑战，产生1）经验得出的分数， 在危机中更准确和公平地分配生命支持，2）开源模拟软件， 评估生命支持分配协议的效率和公平性。