SCH: INT: Collaborative Research: S.E.P.S.I.S.: Sepsis Early Prediction Support Implementation System

SCH：INT：合作研究：S.E.P.S.I.S.：败血症早期预测支持实施系统

• 批准号: 1522107
• 负责人: Julie Ivy
• 金额: $ 83.43万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1522107&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; S.E.P.S.I.S.

Sepsis, infection plus systemic manifestations of infection, is the leading cause of in-hospital mortality. About 700,000 people die annually in US hospitals and 16% of them were diagnosed with sepsis (including a high prevalence of severe sepsis with major complication). In addition to being deadly, sepsis is the most expensive condition associated with in-hospital stay, resulting in a 75% longer stay than any other condition. The total burden of sepsis to the US healthcare system is estimated to be $20.3 billion, most of which is paid by Medicare and Medicaid. In fact, in June 2015 the Centers for Medicare & Medicaid Services (CMS) reported that sepsis accounted for over $7 billion in Medicare payments (second only to major joint replacement), a close to 10% increase from the previous year. This pervasive drain on health care resources is due, in part, to difficulties in diagnosis and delayed treatment. For example, every one hour delay in treatment of severe sepsis/shock with antibiotics decreases a patient's survival probability by 10%. Many of these deaths could have been averted or postponed if a better system of care was in place. The goal of this research is to overcome these barriers by integrating electronic health records (EHR) and clinical expertise to provide an evidence-based framework to diagnose and accurately risk-stratify patients within the sepsis spectrum, and develop and validate intervention policies that inform sepsis treatment decisions. The project to bring together health care providers, researchers, educators, and students to add value to patient care by integrating machine learning, decision analytical models, human factors analysis, as well as system and process modeling to advance scientific knowledge, predict sepsis, and prevent sepsis-related health deterioration. In addition to the societal impact that clinical translation of these findings may bring, the project will provide engineering and computer science students and health services researchers with cross-disciplinary educational experience.The proposed research will apply engineering and computer science methodologies to analyze patient level EHR across two large scale health care facilities, Mayo Clinic Rochester and Christiana Care Health System and to inform clinical decision making for sepsis. The multi-institutional, interdisciplinary collaboration will enable the development of health care solutions for sepsis by describing and accurately risk-stratifying hospitalized patients, and developing decision analytical models to personalize and inform diagnostic and treatment decisions considering patient outcomes and response implications. The Sepsis Early Prediction Support Implementation System (S.E.P.S.I.S.) project aims will be to: 1) Develop data-driven models to classify patients according to their clinical progression to diagnose sepsis and predict risk of deterioration, thus informing therapeutic actions. 2) Develop personalized intervention policies for patients within the sepsis spectrum. 3) Develop decision support systems (DSS) for personalized interventions focusing on resource implications and usability within a real hospital setting. The team will 1) identify important factors that uncover patient profiles based on Bayesian exponential family principal components analysis; 2) develop hidden Markov models (HMMs) and input-output HMMs to identify clusters of patients with similar progression patterns within the sepsis spectrum; 3) provide an analytical framework to support sepsis staging in clinical practice using bilevel optimization. They will 1) predict short- and long-term individual patient outcomes using multivariate statistical models and simulation; 2) develop semi-Markov decision process and partially observable semi-Markov decision process models to identify timing of therapeutic actions and diagnostic tests. Furthermore, the team will 1) predict demand for resources and develop and validate a hybrid mixed integer programming and queueing model to optimize system level allocations; 2) utilize human factors analysis and usability testing to assess the implementation of the DSS.

败血症、感染加上感染的全身表现是院内死亡的主要原因。美国医院每年约有 70 万人死亡，其中 16% 被诊断为脓毒症（其中严重脓毒症并伴有严重并发症的患病率很高）。除了致命之外，脓毒症还是与住院相关的最昂贵的疾病，导致住院时间比任何其他疾病长 75%。 脓毒症给美国医疗保健系统造成的总负担估计为 203 亿美元，其中大部分由 Medicare 和 Medicaid 支付。事实上，2015 年 6 月，医疗保险和医疗补助服务中心 (CMS) 报告称，败血症占医疗保险支付额超过 70 亿美元（仅次于主要关节置换术），比上一年增加了近 10%。 医疗保健资源普遍流失的部分原因是诊断困难和治疗延误。例如，严重败血症/休克的抗生素治疗每延迟一小时，患者的生存概率就会降低 10%。如果有更好的护理系统，许多死亡本来可以避免或推迟。本研究的目标是通过整合电子健康记录 (EHR) 和临床专业知识来克服这些障碍，提供基于证据的框架来诊断脓毒症谱系患者并准确进行风险分层，并制定和验证为脓毒症提供信息的干预政策治疗决定。 该项目将医疗保健提供者、研究人员、教育工作者和学生聚集在一起，通过集成机器学习、决策分析模型、人为因素分析以及系统和过程建模来提高患者护理的价值，以推进科学知识、预测败血症和防止败血症相关的健康恶化。除了这些发现的临床转化可能带来的社会影响外，该项目还将为工程和计算机科学专业的学生以及卫生服务研究人员提供跨学科的教育经验。拟议的研究将应用工程和计算机科学方法来分析患者级别的 EHR梅奥诊所罗切斯特和克里斯蒂娜医疗保健系统这两个大型医疗机构的研究成果，为脓毒症的临床决策提供信息。多机构、跨学科的合作将通过描述和准确地对住院患者进行风险分层，以及开发决策分析模型来个性化并告知诊断和治疗决策，并考虑患者的结果和反应影响，从而开发脓毒症的医疗保健解决方案。脓毒症早期预测支持实施系统 (S.E.P.S.I.S.) 项目的目标是： 1) 开发数据驱动模型，根据临床进展对患者进行分类，以诊断脓毒症并预测恶化风险，从而为治疗行动提供信息。 2) 为败血症患者制定个性化干预政策。 3) 开发个性化干预决策支持系统 (DSS)，重点关注真实医院环境中的资源影响和可用性。该团队将 1) 基于贝叶斯指数族主成分分析确定揭示患者概况的重要因素； 2) 开发隐马尔可夫模型 (HMM) 和输入输出 HMM，以识别脓毒症谱系中具有相似进展模式的患者群； 3）提供一个分析框架，使用双层优化支持临床实践中的脓毒症分期。他们将 1) 使用多元统计模型和模拟来预测短期和长期的个体患者结果； 2）开发半马尔可夫决策过程和部分可观察的半马尔可夫决策过程模型，以确定治疗行动和诊断测试的时机。此外，该团队将1）预测资源需求并开发和验证混合整数编程和排队模型以优化系统级分配； 2）利用人为因素分析和可用性测试来评估DSS的实施情况。