Early Mobilization: Operationalizing Big Data & Implementation Science to Lead Expansion to ICUs (E-MOBILE-ICU)

早期动员：运用大数据

• 批准号: 10445278
• 负责人: Bhakti Kiran Patel
• 金额: $ 16.24万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10445278
• 关键词: Academy; Address; Adopted; Adoption; Beds; Caring; Chicago; Clinical; Clinical Trials; Complex; Consolidated Framework for Implementation Research; Critical Care; Critical Illness; Data; Data Analyses; Data Set; Decision Trees; Development Plans; Early Mobilizations; Early treatment; Enrollment; Evidence based intervention; Evidence based practice; Foundations; Funding; Future; Goals; Hospitals; Hour; Incentives; Institution; Intensive Care Units; Intervention; Interview; Investments; K-Series Research Career Programs; Lead; Long-Term Effects; Machine Learning; Mechanical ventilation; Mediating; Medicine; Mentors; Mentorship; Methodology; Methods; Muscle Weakness; Muscular Atrophy; Outcome; Outcomes Research; Patients; Penetration; Phenotype; Physical activity; Physical therapy; Population; Populations at Risk; Postdoctoral Fellow; Process; Public Health; Publications; Publishing; Qualitative Methods; Quality of life; Randomized Clinical Trials; Rehabilitation therapy; Research; Risk; Site; Source; Statistical Methods; Statistical Models; Structure; Survivors; Target Populations; Testing; Training; Translating; Treatment Efficacy; United States; United States National Institutes of Health; Vulnerable Populations; base; big-data science; career; career development; clinical care; clinical practice; clinical trial implementation; contextual factors; design; disability; disability risk; early phase clinical trial; effectiveness implementation study; effectiveness implementation trial; evidence base; field study; functional disability; functional improvement; functional independence; high risk; implementation facilitators; implementation framework; implementation science; implementation strategy; improved; innovation; insight; intervention effect; machine learning method; mortality; multidisciplinary; neuromuscular; patient subsets; precision medicine; predictive modeling; prevent; skills; treatment effect; trial design; uptake

PROJECT SUMMARY Almost 800,000 critically ill patients require mechanical ventilation every year and three quarters of the survivors suffer from persistent disability, which poses a major public health problem as critical care becomes more widely utilized and available. Although early mobilization, which engages patients in physical activity during mechanical ventilation, is a promising evidence-based intervention that may prevent disability, less than ten percent of pa- tients ever get out of bed. This proposal aims to apply precision medicine to identify patients who are most likely to benefit from early mobilization and elucidate how it can be implemented successfully to extend the benefits of early mobilization to critical care survivors at greatest risk for long-term disability. I hypothesize that this re- source-intensive intervention can be applied with greater precision to a subset of patients most likely to bene- fit, and that implementation science strategies can be devised to successfully drive adoption of this interven- tion beyond a clinical trial setting. I will test my hypothesis in three aims: Aim 1) I will identify the optimal critical illness phenotype for implementation of early mobilization by using cutting-edge machine learning methods; Aim 2) I will determine the effect of early mobilization on long-term functional disability to incentivize adoption of this practice; Aim 3) I will determine the barriers and facilitators of implementation of early mobilization across five institutions to identify the contextual features associated with successful implementation to inform strategies that can bridge the gap between evidence base and clinical practice. My long-term goal is to mitigate the com- plications of critical illness with clinical trials using precision-based methods to identify at-risk and yet apt-to- benefit populations paired with implementation science methodologies to illuminate how to bring these interven- tions to the bedside. To accomplish this, I have assembled an exceptional interdisciplinary team of mentors (Drs. Vineet Arora, Matthew Churpek, and John Kress) and advisors (Drs. Shyam Prabhakaran, Donald Hedeker, Laura Damschroder, and Matthias Eikermann) who have a track record of NIH-funding and successful mentor- ship of post-doctoral candidates. I intend to build on my foundation as an accomplished clinical trialist and have formulated an in-depth career development plan to gain expertise in machine learning methods to identify differ- ential treatment effects (Churpek and Prabhakaran), longitudinal data analysis, (Arora and Hedeker), and imple- mentation science methods (Arora, Prabhakaran, and Damschroder) to craft strategies that bring complex mul- tidisciplinary interventions from clinical trials (Kress and Eikermann) to everyday ICU care. Completion of this proposal will train me to fill an unmet need defined by a recent National Academy of Medicine publication which indicated that identification of differential treatment effects must be paired with rigorous implementation to help transition evidence base to routine clinical care. Equipped with advanced statistical skills and implementation science approaches, I will be able to design hybrid effectiveness-implementation trials to target and implement complex multidisciplinary interventions to vulnerable populations in future R01 level applications.

项目摘要 每年有近80万重症患者需要机械通气， 患有持续性残疾，随着重症监护越来越广泛，这构成了一个重大的公共卫生问题 使用和可用。虽然早期动员，使患者在机械期间进行体力活动， 通气，是一个有前途的循证干预，可以防止残疾，不到百分之十的pa- 孩子们从来没有下床。这项提案旨在应用精准医学来识别最有可能 从早期动员中受益，并阐明如何成功地实施动员， 对最有可能长期残疾的重症监护幸存者进行早期动员。我假设这是- 源密集型干预可以更精确地应用于最有可能贝内的患者子集， 适合，并实施科学的战略，可以设计成功地推动采用这种干预， 超越临床试验的范围。我将在三个目标中测试我的假设：目标1）我将确定最佳的关键 通过使用尖端机器学习方法实施早期动员的疾病表型;目的 2)我将确定早期动员对长期功能障碍的影响，以激励采用这种方法。 目标3）我将确定在五个方面实施早期动员的障碍和促进因素 各机构确定与成功实施有关的背景特征，以通报战略 可以弥合证据基础和临床实践之间的差距。我的长期目标是缓解com- 危重病的临床试验使用基于精确度的方法来识别风险，但适合 受益人群与实施科学方法相结合，以阐明如何将这些干预措施， 到床边。为了实现这一目标，我组建了一个特殊的跨学科导师团队（博士。 Vineet Arora，Matthew Churpek和John Kress）和顾问（Shyam Prabhakaran博士，Donald Hedeker博士， Laura Damschroder和Matthias Eikermann），他们有NIH资助和成功导师的记录， 博士后候选人的船。我打算建立在我的基础上，作为一个有成就的临床试验， 制定了深入的职业发展计划，以获得机器学习方法的专业知识，以识别不同的 基本治疗效果（Churpek和Prabhakaran），纵向数据分析（Arora和Hedeker），以及简单的 心理科学方法（阿罗拉，Prabhakaran和Damschroder）来制定战略，带来复杂的穆尔- 从临床试验（Kress和Eikermann）到日常ICU护理的跨学科干预。完成本 我的建议将训练我填补一个未满足的需求，该需求由最近的国家医学科学院出版物定义， 指出，区别对待效果的识别必须与严格的实施相结合，以帮助 将循证基础转变为常规临床护理。具备先进的统计技能和实施能力 科学方法，我将能够设计混合有效性实施试验，以针对和实施 在未来的R 01级应用中，对弱势群体采取复杂的多学科干预措施。