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 大流行暴露了肺炎治疗的严重失败。 肺炎是传染病导致死亡的主要原因，导致超过 20,000 人住院治疗 退伍军人管理局系统每年都有数千人死亡。近三十年来，中流砥柱 治疗方法是抗生素和支持性护理，对病毒病原体和宿主的认识很少 免疫反应。我们对抗生素的依赖不仅导致了抗生素的过度使用和耐药性，而且还导致了抗生素的耐药性。 诊断和治疗研究的停滞使我们没有能力应对病毒大流行。 意义：新冠病毒的破坏清楚地表明，我们旧的疾病模型是不够的 以实现呼吸道感染的最佳管理。围绕经验性治疗的现有证据 肺炎的研究很差，之前的研究受到异质性和失败的挑战 足够详细地描述患者特征，以确定有益的治疗方法。不太可能 更多相同的方法将促进护理。该提案有助于确定临床方法的方向 走向更复杂的感染因果模型，需要复杂的解决方案。 创新和影响：我们将使用最先进的探索性混合方法来整合 EHR 数据 通过调查和定性数据来检查实践变化。国家分析将更具包容性 以及在不同的 VA 设置中可行的实施解决方案。这项提案在弗吉尼亚州打破了科学基础 通过利用最先进的因果推理方法的变化来进行信息学。如果我们抓住机会 为了研究基于更复杂的临床评估的新治疗方法，我们将采取重要的 朝着开发更好的肺炎治疗方法并为未来的流行病做好更好的准备迈出一步。 具体目标： 目标 1. 描述抗生素和类固醇经验性使用中出现的新变化 使用国家实践数据和定性访谈来评估肺炎。目标 2. 确定与以下方面相关的当地条件 使用探索性混合方法设计，经验性抗生素和类固醇的使用发生了紧急变化。 目标 3. 识别和评估经验性抗生素和类固醇的优化、可解释、定制决策树 治疗患有肺炎的退伍军人。 方法：我们的混合方法包括对患者、提供者和患者的二次数据分析。 设置级别的 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模拟所选治疗和结果的变化 大流行前和大流行后（2021 年 7 月至今）之间的（住院、死亡、继发感染） 周期，并表征这些变量在 VA 站点上的轨迹的异质性。对此 定量分析中，我们将添加定性数据来检查 VA 提供者认知过程的变化 肺炎的诊断和管理，包括有关治疗的信念和规范。我们将 与我们的面部专家咨询小组一起进行配置分析并验证我们的分析结果 有效性、可行性和实用性。然后，我们将确定优化的治疗方案，其形式为 可解释的决策树，可最大限度地降低退伍军人经验性抗生素和类固醇使用的 30 天死亡率 使用基于机器学习的因果推理算法并结合临床专业知识来治疗肺炎。 后续步骤/实施：结果将为退伍军人管理提供建议 可以与其他证据流整合并通过国家计划传播的肺炎 顾问小组的董事。我们将提出实施策略的建议 退伍军人肺炎护理干预措施将在未来的工作中开发和测试。我们还将 为未来的研究提出建议，包括（1）实用的临床试验； (2) VHA的创建- 批准的肺炎护理生活指南； (3) 决策支持和其他实施策略。