Leveraging pandemic practice changes to optimize evidence-based pneumonia care

利用大流行实践的变化来优化基于证据的肺炎护理

• 批准号: 10640043
• 负责人: Barbara Ellen Jones
• 金额: --
• 依托单位: VA SALT LAKE CITY HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640043
• 关键词: Acute Respiratory Distress Syndrome; Adopted; Adoption; Algorithms; Antibiotics; Belief; COVID-19; COVID-19 pandemic; Caring; Cause of Death; Cessation of life; Clinical; Clinical Practice Guideline; Clinical assessments; Communicable Diseases; Complex; Coupled; Data; Data Analyses; Decision Making; Decision Trees; Diagnosis; Diagnostics Research; Disease model; Etiology; Face; Failure; Future; Healthcare; Heterogeneity; Hospitalization; Immune response; Infection; Informatics; Intervention; Interview; Knowledge; Left; Machine Learning; Medical; Methodology; Methods; Modeling; Natural Language Processing; Natural experiment; Observational Study; Patient-Focused Outcomes; Patients; Pneumonia; Pragmatic clinical trial; Provider; Recommendation; Research; Research Methodology; Research Personnel; Resistance; Respiratory Tract Infections; Sepsis; Site; Steroids; Stream; Supportive care; Surveys; System; Testing; Therapeutic Research; United States; Variant; Veterans; Viral; Work; behavior change; causal model; clinical care; cognitive process; community acquired pneumonia; compare effectiveness; design; evidence base; experience; future pandemic; implementation efforts; implementation strategy; individual patient; innovation; mortality; novel strategies; novel therapeutic intervention; pandemic disease; pathogen; pathogenic virus; personalized approach; pre-pandemic; programs; provider adoption; randomized trial; response; secondary infection; standard care; treatment and outcome; treatment optimization; viral pandemic

Background: The COVID-19 pandemic exposed critical failures in the management of pneumonia. Pneumonia is the leading cause of death from infectious diseases, resulting in over 20,000 hospitalizations and thousands of deaths across the VA system each year. For the past thirty years, the mainstays of treatment have been antibiotics and supportive care, with little recognition of viral pathogens and the host immune response. Our reliance on antibiotics has led not only to overuse and resistance, but also to a stagnation in diagnostic and therapeutic research that left us ill-equipped for the viral pandemic. Significance: The devastation of COVID19 has made it clear that our old models of disease are inadequate for the optimal management of respiratory infection. Existing evidence surrounding empiric treatment in pneumonia is poor, fraught with previous research that has been challenged by heterogeneity and a failure to characterize patients with enough detail to identify beneficial treatment approaches. It is unlikely that more of the same approach will advance care. This proposal contributes to a direction of clinical approach toward a more complex causal model of infection that requires complex solutions. Innovation and Impact: We will use state-of-the-art exploratory mixed methods that integrate EHR data with survey and qualitative data to examine practice change. National analyses will allow for more inclusive and feasible implementation solutions in diverse VA settings. This proposal breaks scientific ground in VA informatics by leveraging variation with state-of-the-art causal inference methods. If we take the opportunity to study new treatment approaches based on more complex clinical assessments, we will take an important step toward developing better treatments in pneumonia and being better prepared for future pandemics. Specific Aims: Aim 1. Describe emerging changes in the empiric use of antibiotic and steroids for pneumonia using national practice data and qualitative interviews. Aim 2. Identify local conditions related to emergent change in the use of empiric antibiotics and steroids using an exploratory mixed-methods design. Aim 3. Identify and evaluate optimized, interpretable, tailored decision trees for empiric antibiotic and steroid treatments in Veterans with pneumonia. Methodology: Our mixed methods approach includes secondary data analyses of patient-, provider-, and setting-level EHR data including treatment decisions and patient outcomes, combined with natural language processing. We will apply mixed effects models to model the changes in selected treatments and outcomes (hospitalization, deaths, secondary infection) between the pre-pandemic and later (July 2021-present) periods, and to characterize heterogeneity in the trajectories of these variables across VA sites. To that quantitative analysis, we will add qualitative data examining changes in VA providers’ cognitive processes of diagnosis and management of pneumonia, including beliefs and norms surrounding treatment. We will conduct configurational analyses and validate our analytic results with our expert advisory group for face validity, feasibility and usefulness. We will then identify a optimized treatment regimes, in the form of interpretable decision trees that minimize 30-day mortality, for empiric antibiotic and steroid use in Veterans with pneumonia using machine-learning-based, causal inference algorithms, coupled with clinical expertise. Next Steps/Implementation: Results will inform recommendations for the management of Veterans with pneumonia that can be integrated with other evidence streams and disseminated by the national program directors in the Advisory Group. We will produce recommendations for implementation strategies of interventions in pneumonia care for Veterans that will be developed and tested in future work. We will also produce recommendations for future research, including (1) pragmatic clinical trials; (2) creation of VHA- approved living guidance for pneumonia care; and (3) decision support and other implementation strategies.

背景：Covid-19大流行在肺炎治疗中暴露了严重失败。 肺炎是传染病死亡的主要原因，导致超过20,000个住院治疗 每年VA系统中成千上万的死亡。在过去的三十年中， 治疗一直是抗生素和支持性护理，几乎没有病毒病原体和宿主的认可 免疫反应。我们对抗生素的责任不仅导致过度使用和抵抗，而且导致了 在诊断和治疗研究中停滞，这使我们无法接受病毒大流行。 意义：Covid19的破坏已经明确表明，我们的旧疾病模型不足 用于最佳呼吸道感染。围绕经验治疗的现有证据 肺炎很差，以前的研究受到异质性和失败的挑战。 表征具有足够细节的患者以识别有益的治疗方法。不太可能 更多相同的方法将提高护理。该提案促进了临床方法的方向 迈向需要复杂解决方案的更复杂的感染因果模型。 创新和影响：我们将使用整合EHR数据的最先进的探索性混合方法 通过调查和定性数据进行考试实践的变化。国家分析将允许更具包容性 和可行的实施解决方案在VA DAME环境中。该提案破坏了弗吉尼亚州的科学基础 通过最新因果推理方法利用变化来提供信息。如果我们抓住机会 为了根据更复杂的临床评估来研究新的治疗方法，我们将采用重要的 迈向在肺炎中开发更好的治疗方法，并为将来的大流行做好准备。 具体目的：目标1。描述抗生素和类固醇经验使用的新兴变化 肺炎使用国家实践数据和定性访谈。目标2。确定与 使用探索性混合方法设计的经验性抗生素和类固醇的急剧变化。 目标3。确定和评估经验抗生素和类固醇的优化，可解释的，量身定制的决策树 肺炎的退伍军人治疗。 方法：我们的混合方法方法包括对患者，提供者和提供者的辅助数据分析 设定级别的EHR数据，包括治疗决策和患者结果，并结合自然语言 加工。我们将应用混合效应模型来建模选定的治疗和结果的变化 （住院，死亡，继发感染）在流行前和后期（2021年7月至今）之间 周期，并表征跨VA位点的这些变量的轨迹中的异质性。为此 定量分析，我们将添加定性数据检查VA提供商认知过程中的变化 肺炎的诊断和管理，包括围绕治疗的信念和规范。我们将 进行配置分析并通过我们的专家咨询小组验证我们的分析结果 有效性，可行性和实用性。然后，我们将以 可解释的决策树，可最大程度地减少30天死亡率，用于老兵的经验性抗生素和类固醇 使用基于机器学习的因果推理算法的肺炎，再加上临床专业知识。 下一步/实施：结果将为退伍军人管理的建议提供信息 肺炎可以与其他证据流融合并被国家计划传播 咨询小组的董事。我们将提出有关实施策略的建议 将在未来工作中开发和测试的退伍军人的肺炎干预措施。我们也会 未来研究的生产建议，包括（1）实用临床试验； （2）创建VHA- 批准的肺炎护理生活指导； （3）决策支持和其他实施策略。