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; 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.

项目摘要/摘要 这项通往独立奖的途径是由肺和重症监护流行病学家提交的 致力于改善患有急性呼吸系统的患者的患者研究质量 失败（ARF）。在全球范围内，每年数百万患者患ARF。在美国，将近一百万患者 使用ARF需要每年机械通风，占所有重症监护室（ICU）的四分之一 招生。随着ICU护理的改善降低了此类患者的院内死亡率，注意 转向ARF冲浪者在重新获得先前的认知，身体和社会心理方面所面临的挑战 功能。但是，对于随机临床试验（RCT）测试新干预措施的关键障碍 改善ARF幸存者 - 即（1）捕获长期患者的目前缺乏终点 处置，（2）包含患者的偏好和观点，并且（3）可以在没有的情况下进行分析 关注统计偏见。这项研究的总体目标是支持临床创新 开发新的方法来衡量和报告以患者为中心的成果，以克服 当前限制ARF RCT的方法论障碍。申请人将在 重症监护，以患者为中心的结果研究，统计和 提供信息的信息，以确保他过渡到R00阶段的终身制教师职位，并出现 领先的肺和重症监护流行病学家。首先，申请人将使用创新的组合 定性和定量研究方法，以引起和综合的ARF冲浪者及其护理人员的综合研究方法 透视为新的以患者为中心的长期综合结果度量（K99阶段）。在R00期间 阶段，申请人将招募ARF幸存者参加前瞻性队列，并关注这些患者 用K99期间开发的新端点来描述1年以上ARF表面的Burnen 阶段。这项工作也将提供关键 将在未来的ARF RCT中促进样本量计算的数据。 该队列中的数据还将使用 开发基于电子健康记录（EHR）的算法 预测ARF患者在ICU住宿初期的不良长期结局的风险。那是这个K99/R00 将通过在患者角度锚定的新结果度量来增加ARF研究， 提高ARF后ICU后发病率的理解和临床预后，并促进 未来ARF RCT的效率和临床相关性通过启用患者基线风险的测量 不同的结果。同时，混合方法中的教学工作，个人研究和动手学习 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， 从而将他定位为成功，独立资助的研究职业，以推进科学 ARF RCT的结果测量和分析。