Improving the measurement and analysis of long-term, patient-centered outcomes following acute respiratory failure

改善急性呼吸衰竭后以患者为中心的长期结果的测量和分析

• 批准号: 10064003
• 负责人: Michael Oscar Harhay
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10064003
• 关键词: Accounting; Acute respiratory failure; Address; Admission activity; Algorithms; Attention; Award; Caregivers; Caring; Classification; Clinical; Clinical Data; Code; Cognitive; Communities; Competence; Consensus; Critical Care; Data; Electronic Health Record; Enrollment; Epidemiologist; Epidemiology; Face; Faculty; Financial Support; Funding; Future; Goals; Grant; Health; Hospital Mortality; Hospitals; Individual; Informatics; Intensive Care Units; Intervention; Interview; Lung; Machine Learning; Measurement; Measures; Mechanical ventilation; Mentors; Mentorship; Methodology; Methods; Modeling; Monte Carlo Method; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Natural Language Processing; Outcome; Outcome Measure; Outcomes Research; Pathway interactions; Patient Preferences; Patient-Focused Outcomes; Patients; Phase; Physical Function; Physicians; Positioning Attribute; Predictive Analytics; Process; Property; Prospective Studies; Prospective cohort; Prospective cohort study; Provider; Quality of life; Randomized Clinical Trials; Reporting; Research; Research Design; Research Methodology; Research Personnel; Risk; Rogaine; Sample Size; Science; Statistical Bias; Structure; Survivors; Testing; Time; Training; Work; base; care outcomes; career; clinically relevant; cognitive function; cohort; design; emotional functioning; experience; hands-on learning; high risk; improved; innovation; novel strategies; outcome prediction; patient health information; patient oriented; patient oriented research; prediction algorithm; prognostic; psychosocial; randomized trial; recruit; residence; simulation; statistics; structured data; survivorship; tenure track; unstructured data; ventilation

PROJECT SUMMARY/ABSTRACT This Pathway to Independence Award application is submitted by a pulmonary and critical care epidemiologist committed to improving the quality of patient-oriented research for patients experiencing acute respiratory failure (ARF). Worldwide, millions of patients develop ARF annually. In the U.S., nearly one million patients with ARF require mechanical ventilation annually, accounting for a quarter of all intensive care unit (ICU) admissions. As improvements in ICU care reduce such patients’ in-hospital mortality rates, attention has shifted to the challenges ARF survivors face in regaining their prior cognitive, physical, and psychosocial functioning. However, there is a key barrier for randomized clinical trials (RCTs) testing new interventions to improve ARF survivorship – that is, the current lack of an endpoint that (1) captures long-term patient dispositions, (2) incorporates patient preferences and perspectives, and (3) is able to be analyzed without concern for statistical biases. The overarching goal of this research is to support clinical innovation by developing new approaches to measure and report long-term patient-centered outcomes that overcome the methodological barriers currently limiting ARF RCTs. The applicant will accomplish his goals under the mentorship of established researchers in critical care, patient-centered outcomes research, statistics, and informatics to assure his transition to a tenure-track faculty position in the R00 phase and his emergence as a leading pulmonary and critical care epidemiologist. First, the applicant will use an innovative combination of qualitative and quantitative research methods to elicit and integrate ARF survivors’ and their caregivers’ perspectives into a new patient-centered, long-term composite outcome measure (K99 phase). During the R00 phase, the applicant will recruit ARF survivors to participate in a prospective cohort, and follow these patients to describe the burden of ARF survivorship over 1-year using the new endpoint developed during the K99 phase. This endeavor will also provide key data that will facilitate sample size calculations in future ARF RCTs. Data from this cohort will additionally be used to develop an electronic health record (EHR)-based algorithm to predict risks for adverse long-term outcomes among ARF patients early in their ICU stays. Thus, this K99/R00 will augment ARF research by establishing a new outcome measure anchored in patient perspectives, improving the understanding and clinical prognostication of post-ICU morbidity following ARF, and facilitate the efficiency and clinical relevance of future ARF RCTs by enabling measurement of patients’ baseline risks for different outcomes. Concurrently, the didactic work, individual study, and hands-on learning in mixed-methods research, natural language processing, and predictive analytics will fill key training gaps for the applicant, thereby positioning him for a successful, independently-funded research career advancing the science of outcomes measurement and analysis for ARF RCTs.

项目总结/摘要 这条通往独立奖的道路是由肺和重症监护流行病学家提交的 致力于提高急性呼吸道疾病患者的患者导向研究质量 失败（ARF）。全世界每年有数百万患者发生ARF。在美国，近百万患者 ARF患者每年需要机械通气，占所有重症监护病房（ICU）的四分之一 招生随着ICU护理的改善降低了此类患者的住院死亡率， 转移到ARF幸存者在恢复先前的认知、身体和心理社会方面所面临的挑战 功能然而，测试新干预措施的随机临床试验（RCT）存在一个关键障碍， 提高ARF生存率-即，目前缺乏（1）捕获长期患者的终点 （2）结合了患者的偏好和观点，（3）能够在没有 关注统计偏差。本研究的总体目标是通过以下方式支持临床创新： 开发新的方法来衡量和报告长期以患者为中心的结果， 目前限制ARF RCT的方法学障碍。申请人将在以下条件下实现其目标： 指导重症监护、以患者为中心的结局研究、统计学和 信息学，以确保他过渡到终身教职的位置在R 00阶段和他的出现， 顶尖的肺病和重症监护流行病学家首先，申请人将使用以下创新组合： 定性和定量的研究方法，以引出和整合ARF幸存者及其照顾者的 这是一个新的以患者为中心的长期复合结局指标（K99阶段）。在R 00期间 阶段，申请人将招募ARF幸存者参加前瞻性队列，并随访这些患者 使用K99期间开发的新终点描述1年内ARF生存率的负担 相位这一奋进也将提供关键 有助于未来ARF RCT中样本量计算的数据。 将额外使用该队列的数据 开发基于电子健康记录（EHR）的算法， 预测ARF患者在ICU早期的不良长期结局风险。K99/R00 将通过建立一种新的基于患者视角的结果测量来增强ARF研究， 提高对ARF后ICU后发病率的理解和临床诊断，并促进 通过测量患者的基线风险， 不同的结果。同时，教学工作，个别研究和实践学习的混合方法 研究，自然语言处理和预测分析将填补申请人的关键培训空白， 从而使他成为一个成功的、独立资助的研究事业， ARF RCT的结局测量和分析。