Implementation of Continuum of Care Sepsis Phenotyping and Risk Stratification

脓毒症表型分析和风险分层连续护理的实施

• 批准号: 10429829
• 负责人: Gabriel Wardi
• 金额: $ 18.09万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10429829
• 关键词: Accident and Emergency department; Admission activity; Adoption; Algorithms; Antibiotics; Arithmetic; Artificial Intelligence; Biometry; Caring; Cessation of life; Classification; Clinical; Clinical Trials Design; Complex; Continuity of Patient Care; Critical Care; Data; Data Analyses; Decision Making; Detection; Deterioration; Development; Development Plans; Diagnosis; Disease; Documentation; Early identification; Early treatment; Electronic Health Record; Emergency Medicine; Evolution; Foundations; Goals; Health Personnel; Heart failure; Heterogeneity; Hospitalization; Hospitals; Hour; Immune response; Infection; Inpatients; International; Intervention; Intravenous; Investigation; Liquid substance; Machine Learning; Medication Errors; Mentors; Modeling; Myocardial; Natural Language Processing; Organ failure; Patient Care; Patient Readmission; Patients; Pattern; Personal Satisfaction; Persons; Phenotype; Physiology; Pilot Projects; Pneumonia; Provider; Public Health; Research; Research Priority; Respiratory Failure; Resuscitation; Risk; Scientist; Seminal; Sepsis; Septic Shock; Subgroup; Syndrome; Technology; Testing; Therapeutic; Time; Training; Translating; Triage; Update; Vasoconstrictor Agents; Work; acute care; base; career development; clinical phenotype; clinical practice; cohort; combat; cost; deep learning algorithm; deep learning model; design; dissemination science; follow-up; high risk; hospice environment; hospital readmission; implementation science; improved; innovation; large datasets; machine learning algorithm; mortality; mortality risk; new technology; novel; novel strategies; novel therapeutics; operation; personalized approach; personalized care; personalized intervention; portability; professor; readmission risk; recurrent infection; research and development; risk stratification; septic patients; treatment response; wearable device

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.

项目摘要/摘要 这份提案概述了加布里埃尔·沃迪博士的5年研究和职业发展计划，这是一项紧急情况 医学强化专家，加州大学圣地亚哥分校助理教授。他研究的主要目标是有效地 将深度学习算法应用到临床实践中，改善脓毒症患者的护理。这个K23 建议书概述并为他的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)对机器有很强的理解- 学习方法，以及(3)注重实施科学，以改进对脓毒症患者的护理 深度学习算法。Wardi博士已经组建了一个不同的协作专家团队来支持他的 职业发展和他的导师由Atul Malhotra博士组成，他是国际公认的 重症监护生理学和呼吸衰竭以及Shamim Nemati博士，一位机器学习专家 强烈关注脓毒症的实时预测。此外，他的培训团队还包括 加州大学圣迭戈分校传播和实施科学中心(DISC)的实施科学 作为临床试验设计和生物统计学方面的专家(索尼娅·贾恩博士)。尽管经过几十年的研究，脓毒症 仍然是一个重大的公共卫生挑战。目前的败血症护理方法强调“一刀切”。 在某些子组中可能导致患者伤害的捆绑包。较新的数据分析方法，使用 多层非线性算术运算现在允许将脓毒症患者聚集到新的临床 可提供更个性化护理的表型。PI将评估脓毒症的潜在表型 在AIM 1中未出现入院时(NPOA)。先前对表型的调查已经展开，并 在急诊科就诊的患者中得到了验证。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的群体来改善护理。沃迪博士试图 在这个目标中评估两个相互关联的假设：一个是表型可能代表疾病轨迹， 可通过公认的治疗方法(例如，液体复苏的时间和数量)进行修改。第二个是那本小说 表型存在于住院患者环境中。在他的第二个目标中，Wardi博士试图确定临床机制 通过各种方法对脓毒症患者的30天再住院进行评估，包括确定新的 脓毒症患者在出院时使用自然语言处理的大数据集的聚类识别 重新入院的可诉理由。最后，他试图确定是否将可穿戴补丁应用于 脓毒症患者在与机器学习相结合时被送往长期急性护理医院 算法可能会减少意外的30天脓毒症再入院。这项研究和职业发展计划 为Wardi医生提供了一个令人印象深刻的基础，可以发展成为一名杰出的临床医生和科学家，致力于改善 通过开发和实施新的方法来检测和分类脓毒症患者的护理。Dr。 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。