Leveraging pandemic practice changes to optimize evidence-based pneumonia care

背景：新冠肺炎大流行暴露了肺炎管理的重大失误。 肺炎是传染病死亡的主要原因，导致20,000多人住院 退伍军人事务部每年有数千人死亡。在过去的三十年里， 治疗一直是抗生素和支持性护理，对病毒病原体和宿主几乎没有认识 免疫反应。我们对抗生素的依赖不仅导致了过度使用和耐药性，而且还导致了 诊断和治疗研究停滞不前，使我们没有为病毒大流行做好准备。 重要意义：新冠状病毒造成的破坏清楚地表明，我们的旧疾病模型是不充分的 以实现呼吸道感染的最佳管理。关于经验性治疗的现有证据 肺炎是糟糕的，充满了以前的研究，这些研究受到了异质性和失败的挑战。 对患者进行足够详细的描述，以确定有益的治疗方法。这是不可能的 更多的相同方法将促进医疗保健。这一建议有助于临床方法的方向。 转向更复杂的感染因果模型，这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整合EHR数据 用调查和定性数据来检验实践变化。国家分析将允许更具包容性 以及在不同退伍军人管理局环境中可行的实施解决方案。这项提议在退伍军人管理局取得了科学突破 信息学，利用变异和最先进的因果推理方法。如果我们抓住这个机会 为了在更复杂的临床评估的基础上研究新的治疗方法，我们将采取重要的 朝着开发更好的肺炎治疗方法和为未来的大流行做好更好的准备迈出一步。 具体目标：目标1.描述经验性使用抗生素和类固醇治疗的新变化 使用国家实践数据和定性访谈。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的经验性抗生素和类固醇使用的紧急变化。 目标3.识别和评估经验性抗生素和类固醇的优化、可解释、量身定制的决策树 退伍军人肺炎的治疗。 方法：我们的混合方法方法包括对患者、提供者和 与自然语言相结合的设置级别的EHR数据，包括治疗决定和患者结果 正在处理。我们将应用混合效应模型来模拟选定的治疗和结果的变化。 (住院、死亡、二次感染)从大流行前到大流行后期(2021年7月至今) 周期，并描述这些变量在VA站点间轨迹的异质性。到那时候 定量分析，我们将添加定性数据，检查退伍军人管理局提供者认知过程的变化 关于肺炎的诊断和管理，包括围绕治疗的信念和规范。我们会 与我们的Face专家顾问组一起执行配置分析并验证我们的分析结果 有效性、可行性和有用性。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，将退伍军人经验性抗生素和类固醇使用的30天死亡率降至最低 肺炎使用基于机器学习的因果推理算法，再加上临床专业知识。 下一步/实施：结果将为退伍军人的管理提供建议 可与其他证据流结合并通过国家计划传播的肺炎 顾问组的董事。我们会就以下各项的实施策略提出建议 肺炎干预措施为退伍军人提供护理，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)创建VHA- 经批准的肺炎护理生活指南；(3)决策支持和其他实施战略。

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.