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: 1522072
• 负责人: Muge Capan
• 金额: $ 35.14万
• 依托单位: Christiana Care Health Services Incorporated
• 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 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1522072&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，感染加系统感染的表现，是院内死亡率的主要原因。每年约有70万人在美国医院死亡，其中16％被诊断出患有败血症（包括严重的败血症患病率很高，患有严重的并发症）。除了致命之外，败血症是与院内住宿相关的最昂贵的状况，导致停留时间比任何其他任何情况都长75％。 败血症对美国医疗保健系统的总负担估计为203亿美元，其中大部分是由Medicare和Medicaid支付的。实际上，2015年6月，Medicare＆Medicaid Services中心（CMS）报告说，败血症占了超过70亿美元的Medicare支付（仅次于主要的联合替代者），比上一年增加了近10％。 这种对医疗资源的无处不在耗尽，部分原因是诊断和延迟治疗困难。例如，每小时用抗生素治疗严重败血症/休克的治疗每小时降低患者的生存概率10％。如果有更好的护理体系，可能会避免或推迟其中许多死亡。这项研究的目的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，以提供一个基于证据的框架，以诊断和准确地将患者在脓毒症谱系中进行风险分层，并制定和验证干预政策，从而为脓毒症治疗决策提供了信息。 该项目旨在通过整合机器学习，决策分析模型，人力因素分析以及系统和过程建模来融合医疗保健提供者，研究人员，教育者和学生，从而为患者护理增加价值，以提高科学知识，预测败血症并防止与脓毒症相关的健康恶化。除了这些发现的临床翻译可能带来的社会影响外，该项目还将为工程学和计算机科学学生和健康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学和计算机科学方法来分析患者水平EHR的EHR，跨两个大型医疗保健设施，Mayo Clinic Clinic and Mayo Clinic and Christiana Care Sealth Systems以及为Sepsis做出临床决策。多机构的跨学科合作将通过描述和准确地分层住院的患者来开发败血症的医疗保健解决方案，并开发决策分析模型，以个性化并为诊断和治疗决策提供了诊断和治疗决策，考虑了患者的结果和反应影响。败血症早期预测支持实施系统（S.E.P.S.I.S.）项目的目的是：1）开发数据驱动的模型，根据患者的临床进展以诊断脓毒症并预测劣化风险，从而为治疗作用提供了信息。 2）为败血症患者制定个性化干预政策。 3）为个性化干预措施开发决策支持系统（DSS），重点是实际医院环境中的资源影响和可用性。团队将1）确定基于贝叶斯指数家庭主要成分分析的重要因素； 2）开发隐藏的马尔可夫模型（HMM）和输入输出HMM，以识别败血症谱系中具有相似进展模式的患者的簇； 3）提供了一个分析框架，以使用双层优化的临床实践来支持败血症分期。他们将使用多元统计模型和模拟来预测短期和长期的个人患者结果； 2）制定半马尔可夫的决策过程，并部分可观察到的半马尔可夫决策过程模型，以识别治疗动作和诊断测试的时间。此外，团队将1）预测对资源的需求，并开发和验证混合混合整数编程和排队模型，以优化系统级别分配； 2）利用人为因素分析和可用性测试来评估DSS的实施。