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: 10612933
• 负责人: Gabriel Wardi
• 金额: $ 18.03万
• 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/10612933
• 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 Infarction; Natural Language Processing; Organ failure; Patient Care; Patient Discharge; 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; 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 care; 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 该建议概述了Gabriel Wardi博士的5年研究和职业发展计划，紧急情况 UCSD的医学强化主义者和助理教授。他的研究的主要目标是有效 对临床实践的深入学习算法，以改善败血症患者的护理。这个K23 提案概述并为他的职业发展计划提供支持，特别关注（1）能力 设计有意义的败血症研究和必要的统计培训，（2）对机器的强烈了解 学习方法，以及（3）专注于实施科学，以改善对新型败血症患者的护理 深度学习算法。沃迪博士已经组建了一个由合作专家组成的潜水团队，以支持他的 职业发展和指导他由Atul Malhotra博士组成，他是国际认可的专家 重症监护生理学和呼吸衰竭以及Shamim Nemati博士，机器学习专家 非常重点是实时预测败血症。此外，他的培训团队还包括 UCSD的传播与实施科学中心（DISS）的实施科学也 作为临床试验设计和生物统计学专家（Sonia Jain博士）。尽管进行了数十年的研究，败血症 仍然是一个主要的公共卫生挑战。当前的败血症护理方法强调“一件大小适合所有人” 可能导致某些亚组的患者伤害的束。较新的数据分析方法 现在，多层非线性算术操作可以将败血症患者聚集到新型临床上 可能提供更多个性化护理的表型。 PI将评估败血症的潜在表型 AIM 1中没有入院（NPOA）。 在急诊室的患者中得到验证。 NPOA患者的死亡率很高， 更好地量化表型可以通过识别新组来帮助改善护理。沃迪博士寻求 在此目的中评估2个相互关联的假设：一个是表型可以代表疾病轨迹 可通过接受的疗法（例如，流体复苏的时间和数量）进行修改。第二个小说 表型存在于住院环境中。在第二个目标中，沃迪博士试图确定临床机制 通过多种方法在败血症患者中进行30天的再入院，包括鉴定新颖的方法 出院和使用大型数据集的自然语言处理时，败血症患者集群以识别 可行的理由。最后，他试图确定是否将可穿戴贴剂应用于 败血症患者与机器学习结合后出院到长期急性护理医院 算法可能会减少意外的30天败血症再入院。这项研究和职业发展计划 为沃迪博士提供了一个令人印象深刻的基础，可以发展成为一个著名的临床科学家，以改善 通过开发和实施新的败血症患者检测和分类的方法来护理。博士 沃迪（Wardi）完全致力于通过采用创新策略来改善败血症患者的护理。

项目成果

The etiology and outcomes of cardiopulmonary resuscitation in patients who are on V-V ECMO, a letter to the editor.

• DOI: 10.1016/j.resplu.2023.100536
• 发表时间: 2024-03
• 期刊: RESUSCITATION PLUS
• 影响因子: 2.4
• 作者: Odish, Mazen;Roberts, Erin;Pollema, Travis;Pentony, Erica;Yi, Cassia;Owens, Robert L.;Wardi, Gabriel;Sell, Rebecca E.
• 通讯作者: Sell, Rebecca E.

Ketamine is not associated with more post-intubation hypotension than etomidate in patients undergoing endotracheal intubation.

• DOI: 10.1016/j.ajem.2022.08.054
• 发表时间: 2022-11
• 期刊: The American journal of emergency medicine