Implementation of Continuum of Care Sepsis Phenotyping and Risk Stratification

项目概要/摘要 该提案概述了 Gabriel Wardi 博士的 5 年研究和职业发展计划，这是一个紧急情况 加州大学圣地亚哥分校医学强化医生和助理教授。他研究的主要目标是有效 将深度学习算法应用于临床实践，以改善脓毒症患者的护理。这个K23 提案概述并为其职业发展计划提供支持，特别关注（1）能力 设计有意义的脓毒症研究和必要的统计培训，（2）对机器的深刻理解 学习方法，(3) 注重实施科学，以改善脓毒症患者的护理 深度学习算法。沃迪博士组建了一支多元化的协作专家团队来支持他的研究 他的职业发展和指导由国际公认的专家 Atul Malhotra 博士组成。 与机器学习专家 Shamim Nemati 博士一起研究重症监护生理学和呼吸衰竭 重点关注脓毒症的实时预测。此外，他的培训团队包括以下领域的专家： 加州大学圣地亚哥分校传播与实施科学中心 (DISC) 的实施科学 作为临床试验设计和生物统计学专家（Sonia Jain 博士）。尽管经过数十年的研究，脓毒症 仍然是一个重大的公共卫生挑战。当前脓毒症护理方法强调“一刀切” 可能会对某些亚组的患者造成伤害的捆绑。更新的数据分析方法，使用 多层非线性算术运算现在允许将脓毒症患者聚类到新的临床中 可以提供更个性化护理的表型。 PI 将评估脓毒症的潜在表型 目标 1 中入院时未出现 (NPOA)。之前已对表型进行了调查，并且 在急诊室的患者中得到验证。脓毒症 NPOA 患者死亡率高 更好地量化表型可能有助于通过识别新群体来改善护理。瓦尔迪博士致力于 为此目的评估两个相互关联的假设：一个是表型可能代表疾病轨迹， 可以通过接受的疗法进行修改（例如液体复苏的时间和数量）。第二个是那本小说 表型存在于住院环境中。沃迪博士的第二个目标是确定临床机制 通过多种方法，包括识别新的脓毒症患者 30 天再入院率 出院时脓毒症患者的集群，并使用大数据集的自然语言处理来识别 重新入院的可行理由。最后，他试图确定可穿戴贴片的应用是否可以 与机器学习相结合，脓毒症患者出院到长期急症护理医院 算法可以减少意外的 30 天脓毒症再入院率。本研究和职业发展计划 为 Wardi 博士奠定了令人印象深刻的基础，使他能够发展成为一位致力于改善 通过开发和实施脓毒症患者检测和分类的新方法来提供护理。博士。 Wardi 完全致力于通过采用创新策略来改善脓毒症患者的护理。

PROJECT SUMMARY/ABSTRACT This proposal outlines a 5-year research and career development plan for Dr. Gabriel Wardi, an emergency medicine intensivist and assistant professor at UCSD. The major objective of his research is the effective implementation of deep-learning algorithms to clinical practice to improve care of sepsis patients. This K23 proposal outlines and provides support for his career development plan, specifically focusing on (1) the ability to design meaningful sepsis studies and necessary statistical training, (2) strong understanding of machine- learning approaches, and (3) a focus on implementation science to improve care of sepsis patients with novel deep-learning algorithms. Dr. Wardi has assembled a diverse team of collaborative experts to support his career development and mentor him consisting of Dr. Atul Malhotra, an internationally recognized expert in critical care physiology and respiratory failure along with Dr. Shamim Nemati, a machine-learning expert with a strong focus in prediction of sepsis in real-time. Additionally, his training team includes experts in implementation science from the Dissemination and Implementation Science Center (DISC) at UCSD as well as an expert in clinical trial design and biostatistics (Dr. Sonia Jain). Despite decades of research, sepsis remains a major public health challenge. Current approaches to sepsis care emphasize “one-size fits all” bundles that may result in patient harm in certain subgroups. Newer approaches to data analysis, using multiple layers of non-linear arithmetic operations now allow for clustering of sepsis patients into novel clinical phenotypes that may provide for more personalized care. The PI will evaluate potential phenotypes of sepsis not present on admission (NPOA) in Aim 1. Prior investigations into phenotyping have been developed and validated in patients present in the emergency department. Patients with sepsis NPOA have high mortality and better quantification of phenotypes may help improve care by identifying novel groups. Dr. Wardi seeks to evaluate 2 inter-related hypotheses in this aim: one is that phenotypes may represent disease trajectories that are modifiable by accepted therapies (e.g. time to, and quantity of fluid resuscitation). The second is that novel phenotypes exist in the inpatient setting. In his second aim, Dr. Wardi seeks to determine clinical mechanisms of 30-day readmissions in sepsis patients through a variety of approaches, including identification of novel clusters of sepsis patients at discharge and use of natural language processing of a large data set to identify actionable reasons for readmissions. Finally, he seeks to determine if the application of a wearable patch to sepsis patients discharged to a long-term acute care hospital when combined with a machine-learning algorithm may reduce unanticipated 30-day sepsis readmissions. This research and career development plan affords Dr. Wardi an impressive foundation to develop into a prominent clinician-scientist working to improve care by developing and implementing novel approaches to detection and classification of sepsis patients. Dr. Wardi is fully committed to improving the care of sepsis patients by embracing innovative strategies.