AIVIS: Next Generation Vigilant Information Seeking Artificial Intelligence-based Clinical Decision Support for Sepsis

AIVIS：下一代警惕信息寻求基于人工智能的脓毒症临床决策支持

基本信息

批准号：
10699457
负责人：
Christopher Josef
金额：
$ 25.77万
依托单位：
HEALCISIO, INC.
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-07 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10699457
关键词：
Accident and Emergency department Address Adherence Adverse event Affect American Antibiotics Artificial Intelligence Awareness Behavior California Caregivers Caring Cessation of life Classification Clinical Clinical Data Clinical Decision Support Systems Clinical Research Collaborations Computer software Critical Care Data Development Diagnosis Effectiveness Electronic Health Record Ensure Expert Systems FDA approved Fast Healthcare Interoperability Resources Feedback Frequencies General Ward Geography Goals Health Health Insurance Portability and Accountability Act Healthcare Systems Hospitalization Hospitals Hour Infection Inflammation Intensive Care Units Intervention Intuition Laboratories Learning Length of Stay Licensing Life Liquid substance Machine Learning Measures Medical Device Modeling Morbidity - disease rate Names Nursing Assessment Organ failure Patient Care Patient-Focused Outcomes Patients Pattern Pattern Recognition Performance Pharmaceutical Preparations Phase Prospective Studies Protocols documentation Research Research Personnel Resuscitation Risk Safety Sepsis Shoulder Specificity Surveys Syndrome System Technology Testing Time Uncertainty United States Centers for Medicare and Medicaid Services Universities Validation Weight Work advanced analytics antimicrobial care systems clinical application clinical decision support commercialization cost dashboard data access data integration design diagnostic accuracy evidence base hemodynamics high dimensionality improved innovation mortality new technology next generation nonbinary novel patient population patient response patient safety personalized care predictive modeling predictive tools prospective prototype quality assurance response tool treatment response usability web services

项目摘要

Abstract Sepsis, a heterogeneous syndrome characterized by whole-body inflammation caused by the body's response to an infection, is the most expensive and deadly condition treated in hospitals, with over 270,000 cases of sepsis-related deaths in the U.S. alone. The cornerstones of optimal sepsis care are early recognition accompanied by appropriate antimicrobial therapy, and use of evidence-based hemodynamic therapies such as fluid resuscitation and vasoactive medications. While data-driven approaches based on machine learning (ML) have shown promise in finding patterns in high-dimensional clinical data to forecast sepsis among hospitalized patients, there are no clinically validated and FDA-approved clinical decision support (CDS) system that can reliably identify patients at risk of developing sepsis. Moreover, existing ML-based solutions are as good as the quality of the data presented to them, and the presence of outliers and missingness can have deleterious effects on their performance. For instance, it has been suggested that such systems are essentially looking over clinician's shoulders-using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. As such, there is a critical need for sepsis prediction tools that can effectively use the routinely collected EHR data, assess prediction confidence, and if needed, take necessary steps to gather additional information to reduce prediction uncertainty and improve diagnostic accuracy without significant demand on the end-users. This project aims to assess the clinical utility, safety, and efficacy of a novel uncertainty-aware sepsis prediction system designed and developed in collaboration between UC San Diego Health and Healcisio Inc., a UCSD start-up focused on scalable development and commercialization of advanced analytical systems in critically care settings. The Healcisio system is explicitly designed to improve compliance with the Centers for Medicaid and Medicare Services (CMS) care protocol for sepsis (the SEP1 bundle) and to address the existing delays and variabilities in determining the sepsis onset time, so that life-saving antibiotics and hemodynamic support can be delivered in a timely fashion. To maintain software quality assurance a quality management system (QMS) will be developed to accompany a 510(k) FDA submission package to demonstrate safety and effectiveness. To enhance hospital quality improvement (QI) teams’ ability to measure impact of earlier recognition and SEP-1 bundle compliance, a novel quality measure (SEP1+) and a causal impact analysis tool is introduced. Ultimately, the novel technologies developed and tested under this project will enhance our ability to use advanced analytics to predict adverse events, assess patients’ response to therapy, and optimize and personalize care at the beside through a rapid-cycle ‘learning healthcare system’ framework.

抽象的脓毒症是一种异质综合征，其特征是由身体的炎症引起的全身炎症对感染的反应是医院治疗中最昂贵和最致命的疾病，超过 270,000 仅在美国就有与脓毒症相关的死亡病例最佳脓毒症治疗的基石是早期治疗。识别并辅以适当的抗菌治疗，并使用基于证据的血流动力学液体复苏和血管活性药物等疗法是基于数据驱动的方法。机器学习 (ML) 在寻找高维临床数据模式以进行预测方面表现出了希望住院患者败血症，尚无经过临床验证和 FDA 批准的临床决策支持（CDS）系统，可以可靠地识别有败血症风险的患者。基于机器学习的解决方案的好坏取决于提供给他们的数据的质量以及异常值的存在例如，有人提出，缺失可能会对他们的表现产生有害影响。这些系统本质上是在监视临床医生的肩膀——使用临床行为作为表达因此，迫切需要利用预先存在的直觉和怀疑来做出预测。预测工具可以有效地使用常规收集的 EHR 数据、评估预测置信度，以及如果需要，采取必要措施收集额外信息以减少预测不确定性提高诊断准确性，而对最终用户没有太大要求。该项目旨在评估新型不确定性脓毒症的临床实用性、安全性和有效性加州大学圣地亚哥分校健康中心和 Healcisio 合作设计和开发的预测系统 Inc.，一家加州大学圣地亚哥分校初创公司，专注于高级分析的可扩展开发和商业化 Healcisio 系统的明确设计目的是提高重症监护环境中的依从性。医疗补助和医疗保险服务中心 (CMS) 脓毒症护理方案（SEP1 捆绑包）以及解决确定败血症发病时间时存在的延迟和变量问题，以便挽救生命可以及时提供抗生素和血流动力学支持，以保持软件质量。确保将开发质量管理体系 (QMS) 以配合 510(k) FDA 提交提高医院质量改进 (QI) 团队的安全性和有效性。能够衡量早期认可和 SEP-1 捆绑合规性的影响，这是一种新颖的质量衡量标准 (SEP1+) 和因果影响分析工具最终被开发和引入。在该项目下进行的测试将增强我们使用高级分析来预测不良事件的能力，评估患者对治疗的反应，并通过快速周期优化和个性化旁边的护理 “学习医疗保健系统”框架。