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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.

项目总结/摘要该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，紧急加州大学圣地亚哥分校的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于临床实践，以改善脓毒症患者的护理。K23 建议书概述了他的职业发展计划，并提供了支持，特别侧重于（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，和（3）注重实施科学，以改善新的脓毒症患者的护理深度学习算法Wardi博士组建了一个由合作专家组成的多元化团队，职业发展和指导他包括博士阿图尔马尔霍特拉，一个国际公认的专家，重症监护生理学和呼吸衰竭沿着Shamim Nemati博士，机器学习专家，实时预测脓毒症。此外，他的培训团队还包括以下方面的专家：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）。尽管经过几十年的研究，败血症仍然是一个重大的公共卫生挑战。目前的脓毒症治疗方法强调“一刀切” 在某些亚组中可能导致患者伤害的捆绑。数据分析的新方法，使用多层非线性算术运算现在允许将脓毒症患者聚类为新的临床可以提供更个性化的护理的表型。PI将评价脓毒症的潜在表型目标1中的入院时不在场（NPOA）。先前对表型的研究已经发展，在急诊科的患者中得到验证。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的群体来改善护理。沃迪博士试图评估2个相互关联的假设：一个是表型可能代表疾病轨迹，可通过接受的治疗进行修改（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。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，包括确定新的脓毒症患者在出院时的集群和使用自然语言处理的大数据集，以确定重新入院的可行理由。最后，他试图确定是否应用可穿戴贴片，脓毒症患者出院到长期急性护理医院时，结合机器学习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博士提供了一个令人印象深刻的基础，使其发展成为一名杰出的临床科学家，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。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。

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.