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

基本信息

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
项目类别：
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

项目摘要

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的医学强化医生和助理教授，他研究的主要目标是有效的。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。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 Wardi 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，败血症仍然存在。仍然是一个重大的公共卫生挑战，目前脓毒症护理方法强调“一刀切”。使用新的数据分析方法可能会导致某些亚组患者受到伤害。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 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，并且在急诊室的脓毒症 NPOA 患者中得到验证，其死亡率很高，并且更好地量化表型可能有助于通过识别新群体来改善护理。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）。在他的第二个目标中，沃迪博士试图确定临床机制。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最后，他试图确定可穿戴贴片的应用是否有助于重新入院。与机器学习相结合，脓毒症患者出院到长期急症护理医院该算法可以减少意外的 30 天脓毒症再入院。这项研究和职业发展计划。为 Wardi 博士发展成为一名致力于改善疾病的杰出临床医生科学家奠定了令人印象深刻的基础开发和实施脓毒症患者检测和分类的新方法来提供护理。 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。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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.