Personalized Recommendations for Acute Kidney Injury (AKI) Care Using a Kidney Action Team: A Randomized Trial

肾脏行动小组针对急性肾损伤 (AKI) 护理的个性化建议：随机试验

• 批准号: 10211505
• 负责人: FRANCIS PERRY WILSON
• 金额: $ 38.47万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10211505
• 关键词: Personalized; Recommendations; Acute; Kidney; Injury

Project Summary The overarching goal of this proposal is to determine whether rapid response-style, personalized recommendations for the workup and treatment of patients with Acute Kidney Injury (AKI), as delivered by a remote Kidney Action Team, will improve clinical outcomes in a hospitalized population. AKI is a common complication in hospitalized patients and is associated with dramatic increases in the risk of inpatient mortality, long-term chronic kidney disease, and cost. International guidelines for AKI treatment focus on best practices that include appropriate management of drug dosing, avoidance of kidney-toxic exposures, and careful hemodynamic and electrolyte balance. However, multiple studies and our preliminary data suggest that AKI is often missed in a variety of hospital settings and that diagnostic and therapeutic actions are infrequently taken or inappropriate. Failure to engage in best practices is associated with increased morbidity and mortality among AKI patients. Our group focuses extensively on increasing provider awareness and recognition of AKI. Our randomized trials of simple electronic AKI alert systems have demonstrated clinical equipoise regarding the effectiveness of such alerting, as there was no improvement in clinical outcomes among those in the alert group. However, the alert was purely informational and provided no actionable recommendations. Because of the heterogeneity of both the AKI patient and AKI etiology, diagnostic and treatment plans should be highly individualized and consider many facets of care, including diagnostic workup, acid/base management, electrolyte management, hemodynamic management, and medication management. This proposal seeks to build on our prior experience to determine whether offering providers prompt and actionable items that are personalized to individual patients may be more beneficial in the care of AKI patients. Across two medical centers and eight hospitals, we will recruit and train Kidney Action Teams – acting as a rapid response team for AKI – consisting of an advanced practitioner, a pharmacist and a nephrologist. In a hub-and-spoke design, centralized Kidney Action Teams will make personalized recommendations on AKI diagnosis and treatment based on electronic chart review. Kidney Action Team recommendations will be created for all patients but delivered only in those randomized to the intervention. This experimental design will determine: 1) whether real-time personalized recommendations for AKI diagnosis and treatment improve improve patient outcomes; 2) which patients and providers are most likely to benefit from e-consults of this type and 3) whether a fully automated rules engine can successfully mimic human- derived recommendations. Such an automated system, if successful, would provide a highly generalizable model for integration into other health systems.

项目摘要 该提案的总体目标是确定快速响应式的个性化 对急性肾脏损伤患者（AKI）的工作和治疗的建议，由A提供 远程肾脏动作小组将改善住院人口的临床结果。 aki很普遍 住院患者的并发症，与住院死亡率的风险显着增加有关， 长期慢性肾脏疾病和费用。国际AKI治疗指南的重点是最佳实践 这包括适当管理药物给药，避免肾脏毒性暴露和仔细的 血液动力学和电解质平衡。但是，多个研究和我们的初步数据表明AKI是 在各种医院环境中通常会错过，并且很少采取诊断和治疗动作 或不合适。未能从事最佳实践与发病率和死亡率的增加有关 在AKI患者中。 我们的小组广泛着重于提高提供者的意识和对AKI的认可。我们的 简单电子AKI警报系统的随机试验已证明有关临床平衡 这种警报的有效性，因为警报中的临床结果没有改善 团体。但是，警报纯粹是信息的，没有提供可行的建议。由于 AKI患者和AKI病因，诊断和治疗计划的异质性应高度高 个性化和考虑许多护理方面，包括诊断工作，酸/基础管理， 电解质管理，血液动力学管理和药物管理。 该建议旨在以我们先前的经验来确定提供提供商是否提示 个性化对个别患者的可操作的物品可能对AKI的护理更有益 患者。在两个医疗中心和八家医院中，我们将招募和培训肾脏动作团队 - 作为AKI的快速响应团队 - 由高级从业者，药剂师和一个组成 肾脏科医生。在集线器设计中，集中式肾脏动作团队将使个性化 基于电子图表审查的AKI诊断和治疗的建议。肾脏动作团队 将为所有患者提出建议，但仅在随机进行干预的患者中提供建议。 该实验设计将确定：1）实时个性化的AKI诊断建议 治疗改善患者的预后； 2）哪些患者和提供者最有可能受益 从这种类型的电子表达和3）完全自动化的规则引擎是否可以成功模仿人类 派生的建议。这样的自动化系统（如果成功）将提供高度可推广的 集成到其他卫生系统中的模型。