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

败血症，感染加上全身感染的表现，是住院死亡的主要原因。每年约有70万人死于美国医院，其中16%被诊断为脓毒症(包括严重脓毒症和主要并发症的高发)。除了致命之外，败血症是与住院相关的最昂贵的疾病，导致的住院时间比其他任何疾病都长75%。败血症给美国医疗体系造成的总负担估计为203亿美元，其中大部分由联邦医疗保险和医疗补助支付。事实上，2015年6月，医疗保险和医疗补助服务中心(CMS)报告称，脓毒症占医疗保险支出的70亿美元以上(仅次于主要的关节置换)，比前一年增加了近10%。卫生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，在使用抗生素治疗严重败血症/休克的过程中，每延迟一小时，患者的存活概率就会降低10%。如果有更好的护理系统，这些死亡中的许多人本可以避免或推迟。这项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，以提供一个基于证据的框架，以诊断和准确地对脓毒症谱内的患者进行风险分层，并制定和验证为脓毒症治疗决策提供信息的干预策略。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科学知识、预测脓毒症并防止败血症相关的健康恶化，从而为患者护理增加价值。除了这些发现的临床翻译可能带来的社会影响外，该项目还将为工程和计算机科学专业的学生和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两个大型医疗机构--梅奥诊所罗切斯特和克里斯蒂娜护理卫生系统--的患者水平的EHR，并为脓毒症的临床决策提供信息。这种多机构、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，并开发决策分析模型来个性化诊断和治疗决策，并在考虑到患者结果和反应影响的情况下为诊断和治疗决策提供信息，从而开发出脓毒症的卫生保健解决方案。脓毒症早期预测支持实施系统(S.E.P.S.I.)该项目的目标将是：1)开发数据驱动的模型，根据患者的临床进展对患者进行分类，以诊断脓毒症并预测恶化的风险，从而指导治疗行动。2)为脓毒症谱系内的患者制定个性化干预政策。3)开发用于个性化干预的决策支持系统(DSS)，重点关注资源影响和在真实医院环境中的可用性。该团队将1)根据贝叶斯指数家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，以识别脓毒症谱内具有相似进展模式的患者群；3)提供一个分析框架，以支持临床实践中使用双层优化的脓毒症分期。他们将1)使用多变量统计模型和模拟来预测单个患者的短期和长期结果；2)开发半马尔可夫决策过程和部分可观测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间。此外，该小组将1)预测资源需求，并开发和验证混合整数规划和排队模型，以优化系统级别的分配；2)利用人为因素分析和可用性测试来评估决策支持系统的实施情况。

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.