Predictive Monitoring: IMPact of Real-time Predictive Monitoring in Acute Care Cardiology Trial (PM-IMPACCT)

预测性监测：实时预测性监测在急性护理心脏病试验中的影响 (PM-IMPACCT)

• 批准号: 10693155
• 负责人: Jamieson MacDonald Bourque
• 金额: $ 40万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693155
• 关键词: Predictive; Monitoring; IMPact; Real; time

Keim-Malpass/Bourque PM-IMPACCT PROJECT SUMMARY/ABSTRACT Patients on the acute care wards who deteriorate and are emergently transferred to the intensive care unit have poor outcomes. Early identification of subtly worsening patients might allow for earlier clinical action leading to reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (i.e. artificial intelligence (AI)-based risk prediction) make a wealth of data available to healthcare providers and can form the foundation for computational algorithms that integrate real-time bedside monitor physiologic data to provide early warning of potentially catastrophic physiologic events. The future of acute hospital care includes monitoring systems that integrate data streams of rapidly changing clinical information to estimate and communicate risk of imminent events. This will allow a paradigm change in care from reactive to proactive by predicting patient trajectories and acting early to promote optimal patient trajectories. Predictive analytics monitoring is a promising technology that will yield families of these new tools. Here, we propose a multi- disciplinary cluster randomized controlled trial (NCT04359641) to test the use of CoMET (Continuous Monitoring of Event Trajectories), an AI-based visual analytic that displays risk estimates for multiple adverse outcomes. It is expected that having access to a visual risk analytic for impending catastrophic outcomes can draw the clinician’s attention to patients warranting early or extra consideration. Specifically, in our proposed cluster RCT we will evaluate the impact of predictive analytics monitoring on: (1) improvement in patient outcomes, (2) response time to proactive clinical action, and (3) costs to the healthcare system. This proposal is led by an immensely promising interdisciplinary mPI early stage investigators who are members of the Center for Advanced Medical Analytics. This proposal will strengthen the ability of health care organizations to evolve as learning health systems that apply bioinformatics data to improve patient outcomes by incorporating artificial intelligence into knowledge tools that are successfully integrated for use by health care providers and determine if they improve patient outcomes. We anticipate developing standard processes that can be leveraged and are scalable for general implementation of predictive analytics monitoring algorithms in real-life practice contexts. Additionally, we anticipate building on this R01 with future work including a multi-center randomized control trial testing effectiveness of our artificial intelligence-based risk analytic.

凯姆-马尔帕斯/布尔克 PM-IMPACCT 项目摘要/摘要 急症病房中病情恶化并被紧急转送到重症监护室的病人 结果很差。及早识别病情轻微恶化的患者可能有助于更早的临床行动 从而降低发病率和死亡率。床边持续预测分析监测研究进展 (即基于人工智能(AI)的风险预测)使医疗保健提供者和 可以构成集成实时床边监护生理数据的计算算法的基础 为潜在的灾难性生理事件提供早期预警。急诊医院护理的未来 包括监控系统，集成快速变化的临床信息的数据流以估计和 传达迫在眉睫事件的风险。这将允许从被动护理到主动护理的范式转变 预测患者轨迹并及早采取行动，以促进最佳患者轨迹。预测分析 监测是一项很有前途的技术，它将产生这些新工具的家族。在这里，我们提出了一个多- 学科群随机对照试验(NCT04359641)，以测试彗星的使用(连续 监视事件轨迹)，这是一种基于人工智能的可视分析，显示对多个不良事件的风险估计 结果。预计能够访问即将到来的灾难性后果的可视风险分析可以 提醒临床医生注意需要及早或额外考虑的患者。具体地说，在我们提出的 我们将评估预测分析监测在以下方面的影响：(1)患者的改善 结果，(2)主动临床行动的反应时间，以及(3)医疗保健系统的成本。这项建议 是由一位非常有前途的跨学科MPI早期调查人员领导的，他们是 高级医学分析中心。这项提议将加强医疗保健组织的能力 作为应用生物信息学数据以改善患者预后的学习型医疗系统而发展 将人工智能成功地集成到知识工具中，供医疗保健提供者和 确定它们是否改善了患者的预后。我们预计将开发标准流程，这些流程可以 可利用并可扩展，用于在现实生活中全面实施预测分析监控算法 练习环境。此外，我们预计将在R01的基础上进行未来的工作，包括一个多中心 随机对照试验测试我们基于人工智能的风险分析的有效性。