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，危机标准的指导指导分类团队被迫 选择哪些患者获得稀缺的生命支持治疗。类似于器官分配系统，这些 算法将道德原则转换为重症监护室（ICU）候选人的具体排名排序 生命支持分配分数的治疗。产生稀缺性的灾难往往最严重 处境不利的社区，尤其是种族和少数民族群体。设计算法分配时 稀缺的生活支持，公共卫生官员应考虑到这一背景。 为了识别患病患者的治疗可能性最高的重症患者， 美国大多数州将优先考虑那些具有低顺序器官衰竭评估（SOFA）得分的人。但 沙发是为ICU中已经在ICU中的生命支持的患者而设计的，使用常规测量的实验室值， 药物剂量和生命体征监测对治疗的反应。当大多数患者的沙发得分很低时 首先认识到危害疾病，沙发无法准确预测使用生命前数据的死亡风险 分配了支持。我们证明了基于沙发的分类方案的预测性能差 由于肾脏成分错误而导致黑人患者的存活不足，部分解释了 沙发得分。显然需要开发和验证一种新颖的生命支持分配方案 旨在为Debias现有分数并挽救更多的生命。基于位置的劣势指数，例如 区域剥夺指数（ADI）和社会脆弱性指数提供了潜在的解决方案。使用这些 经过验证的邻里剥夺的地理措施分配稀缺的医疗保健资源 抵消社会劣势的风险增加的影响，包括种族化产生的劣势 住宅隔离。我们假设使用基于地点的精心设计的生活支持分配得分 缺点指数可以挽救更多的生命，并减轻危机中的医疗保健不平等。 该项目的总体目的是准确地开发一种生命支持分配算法 公平地在危机中分配了稀缺的ICU治疗方法。在AIM 1中，我们将使用结构方程建模 使用基于位置的劣势指数来创建公平的生命支持分配（ELSA）得分 沙发。在AIM 2中，开发ICU危机仿真模型（ICSM），这是一个离散的事件模拟，模型 患者流量和生存，作为生命支持分配方案的测试和评估环境。在AIM 3中， 我们将在国家库维德队列协作数据飞地中外部验证ELSA和ICSM，这 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 应用公共卫生道德中最紧迫的挑战，产生1）经验得出的分数 在危机中更准确，更准确地分配生活支持，2）开源模拟软件 评估生命支持分配方案的效率和公平性。