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.

项目摘要/摘要肺炎是全球死亡的主要感染原因。而炎症是一种标志性的病理肺炎的特征，使用皮质类固醇来钝化深刻的炎症反应仍然存在不明确的。数十年的异质种群研究未能表现出一致的好处，导致使用皮质类固醇的指南相互矛盾。尽管已证明皮质类固醇在 COVID-19肺炎，在其他感染性肺炎中使用皮质类固醇的使用尚不清楚临床实践的变异性。实践中的这种可变性为查看皮质类固醇如何可以针对患者特定特征进行优化和量身定制肺炎的给药。目的这项研究是为了使高级统计和机器学习方法适应已经可用的来自电子健康记录的观察数据，以识别临床恶化的预测指标并发展在社区获得的肺炎患者中使用类固醇的个性化治疗规则。这将是通过三个特定目标完成：1）开发和验证机器学习预测工具院内疾病进展，2）制定和测试个性化治疗规则（ITR）以供类固醇使用， 3）进行比较ITR和生物标志物皮质类固醇的单个中心可行性临床试验在社区获得的肺炎患者中使用并给予通常的护理。我们假设：1）人口统计学，生理参数，临床和实验室数据的结合将是准确的预测院内疾病进展的风险，并确定受益的类固醇响应性患者从辅助皮质类固醇治疗大于潜在危害，2）ITR和生物标志物引导皮质类固醇的使用和剂量是可行的。该职业发展奖将为重要未来大型临床试验的初步数据侧重于优化肺炎的皮质类固醇使用培训初级调查人员成为独立研究人员所需的基本技能。