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Individualizing Steroid Use in Pneumonia

肺炎中类固醇的个体化使用

基本信息

批准号：
10643595
负责人：
Yewande Odeyemi
金额：
$ 16.9万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10643595
关键词：
Acute Respiratory Distress Syndrome Adoption Adrenal Cortex Hormones Adult Anti-Inflammatory Agents Biological Markers Biometry C-reactive protein COVID-19 pandemic COVID-19 patient COVID-19 pneumonia Cause of Death Characteristics Clinic Clinical Clinical Trials Clinical Trials Design Complication Coupled Critical Care Critical Illness Data Data Science Demographic Analyses Deterioration Disease Progression Dose Electronic Health Record Future Goals Grant Guidelines Hospitalization Hospitals Hypoxemia Individual Inflammation Inflammatory Response Investigation K-Series Research Career Programs Knowledge Laboratories Methodology Outcome Outcomes Research Oxygen Pathologic Patient Selection Patient-Focused Outcomes Patients Physiological Pneumonia Population Population Heterogeneity Pragmatic clinical trial Recovery Research Research Personnel Resolution Retrospective cohort Risk SARS-CoV-2 infection Sample Size Steroids Testing Time Training acute hypoxemic respiratory failure clinical practice clinical predictors community acquired pneumonia demographics effectiveness testing electronic health data evidence base experience implementation science improved individualized medicine machine learning method machine learning prediction multidisciplinary patient response personalized approach predictive tools primary outcome prognostic prognostic tool risk prediction skills statistical and machine learning success treatment as usual trial comparing

项目摘要

PROJECT SUMMARY/ABSTRACT Pneumonia is the leading infectious cause of death worldwide. While inflammation is a hallmark pathologic feature of pneumonia, the use of corticosteroids to blunt the profound inflammatory response remains undefined. Decades of studies in heterogeneous populations failed to show a consistent benefit, leading to conflicting guidelines on the use of corticosteroids. Although corticosteroids have been shown to be effective in COVID-19 pneumonia, the use of corticosteroids in other infectious pneumonia remains unclear with high variability in clinical practice. This variability in practice presents an opportunity to see how corticosteroid administration in pneumonia can be optimized and tailored to patient specific characteristics. The objective of this study is to adapt advanced statistical and machine learning methods to already available robust observational data from the electronic health record to identify predictors of clinical deterioration and to develop an individualized treatment rule for steroid use in patients with community acquired pneumonia. This will be accomplished through three specific aims: 1) To develop and validate a machine learning prediction tool for in-hospital disease progression, 2) To develop and test an individualized treatment rule (ITR) for steroid use, and 3) To conduct a single center feasibility clinical trial comparing ITR and biomarker guided corticosteroid use and dosing to usual care in patients with community acquired pneumonia. We hypothesize that: 1) a combination of demographics, physiological parameters, clinical and laboratory data will be accurate in predicting risk of in-hospital disease progression and identifying steroid-responsive patients in whom benefit from adjunct corticosteroid treatment outweighs potential harm, and 2) ITR and biomarker-guided corticosteroid use, and dosing will be feasible. This career development award will provide important preliminary data for future larger clinical trials focused on optimizing corticosteroid use in pneumonia while training a junior investigator in the essential skills needed to become an independent researcher.

项目总结/摘要肺炎是全球主要的传染性死亡原因。虽然炎症是一个标志性的病理由于肺炎的特点，使用皮质类固醇来减弱严重的炎症反应仍然存在，未定义。数十年的异质性人群研究未能显示一致的益处，导致关于皮质类固醇使用的相互矛盾的指南。虽然皮质类固醇已被证明是有效的 COVID-19肺炎，皮质类固醇在其他感染性肺炎中的使用尚不清楚，临床实践的可变性。实践中的这种可变性提供了一个机会，看看皮质类固醇如何可以优化肺炎中的给药并使其适合于患者的具体特征。的目标这项研究是为了适应先进的统计和机器学习方法，以已经可用的强大的电子健康记录的观察数据，以确定临床恶化的预测因素，社区获得性肺炎患者使用类固醇的个体化治疗规则。这将是通过三个具体目标实现：1）开发和验证机器学习预测工具， 2）开发和测试类固醇使用的个体化治疗规则（ITR），和3）进行比较ITR和生物标志物指导的皮质类固醇的单中心可行性临床试验在社区获得性肺炎患者中的使用和给药。我们假设：1）a 人口统计学、生理参数、临床和实验室数据的组合将准确，预测院内疾病进展的风险，并确定类固醇反应患者，来自辅助皮质类固醇治疗的风险超过潜在危害，2）ITR和生物标志物指导的皮质类固醇的使用和剂量将是可行的。这一职业发展奖将提供重要的未来更大规模临床试验的初步数据集中在优化皮质类固醇在肺炎中的使用，培训初级研究员成为独立研究员所需的基本技能。