Multi Biomarker-based prediction tool development to determine risk of infections-related outcomes among severe blunt trauma patients

严重创伤损伤使患者容易受到感染，以及随后与感染相关的风险 结果，包括多器官衰竭/功能障碍综合征(MOF/MODS)，这是死亡和 发病率。尽管众所周知，感染是多器官功能衰竭的主要风险因素，但并不是所有 经历医院感染发展成MOF，强调考虑潜在感染的重要性 感染后MOF易感性异质性的分子生物学机制(即。 与感染相关的MOF)。在目前的临床实践中，MOF特定的评分系统基于生理学 监测丹佛和马歇尔评分等指标，并用于诊断多器官功能衰竭患者。 在发病后。在这里，我们建议在感染相关多器官功能衰竭发病前建立预测模型，使用 分子签名，以显著提高预测精度。快速方法(即立即 在检测到感染之后)，并准确识别高度易受感染的患者- 预计相关成果将有助于知情决策和确保适当交付 控制MOF发病的预防措施。因此，这些方法可能会改善患者的健康 并降低医疗保健成本。这项提案旨在使用一种公正的计算方法来调查 全基因组转录组图谱和开发一组生物标志物以预测感染相关的多器官功能衰竭 在检测到感染后立即采取行动。以前在感染背景下的转录组研究通常 重点关注患者对感染的反应。相反，我们建议将重点放在生物标记面板的开发上，以 在发生之前预测与感染相关的特定不良后果。提出了两个目标来预测 钝性创伤患者感染相关性多器官功能衰竭的结局，这是一个高度易感的人群 感染。目的1：使用炎症和宿主损伤反应研究中的血液样本 Grant“)，我们将利用我们早期的血液转录组多生物标记物开发机器学习管道来 建立模型以预测钝性创伤患者队列中感染相关的多器官功能衰竭结局。目标2： 我们将使用损伤严重程度评分和其他常见的人口统计学和临床变量来建立预测模型 用于感染相关的多器官功能衰竭，并将其性能与多生物标志物模型进行比较。我们假设， 与基于临床评分的模型相比，我们提出的基于转录签名的策略将 结果是预测越来越准确，并进一步提供了对潜在分子的洞察 感染后发生多器官功能衰竭的机制。对这些分子机制的识别最终可能有助于 发现药理干预的潜在靶点。总体而言，这项研究的结果可能提供 也为进一步研究不同钝性创伤队列中感染相关结局预测奠定了基础 就像在受其他类型创伤影响的队列中一样。这项研究的方法和发现也可能适用 对其他免疫功能低下的人群，如癌症患者和术后患者。

Severe trauma injury renders patients vulnerable to infections and subsequent risk of infections-related outcomes, including multiple organ failure/dysfunction syndrome (MOF/MODS), a major cause of mortality and morbidity. Although it is well-established that infection is a major risk factor for MOF, not all patients who experience nosocomial infections develop MOF, highlighting the importance of considering the underlying molecular biological mechanisms of heterogeneity in susceptibility to MOF development after infections (ie. infections-related MOF). In current clinical practices, MOF-specific score systems based on physiological measurements such as the Denver and Marshall Scores are monitored and used to diagnose patients with MOF after its onset. Here we propose to build prediction models for infections-related MOF before its onset using molecular signatures in order to significantly increase prediction accuracy. Methods of rapid (ie. immediately after the detection of infection) and accurate identification of patients who are highly susceptible to infections- related outcomes are expected to aid in informed decision-making and ensuring appropriate delivery of preventative measures to control MOF incidence. Such methods may thus result in improved health of patients and reduced health care costs. This proposal aims to employ an unbiased computational approach to investigate genome-wide transcriptome profiles and develop a panel of biomarkers to predict infections-related MOF immediately after the detection of infection. Previous transcriptome studies in the context of infections often focus on patient responses to infection. In contrast, we propose to focus on biomarker panel development to predict a specific infections-related adverse outcome before it occurrs. Two Aims are proposed to predict the outcome of infections-related MOF among blunt trauma patients, a population that is highly susceptible to infections. Aim 1: using blood samples from the Inflammation and the Host Response to Injury Study (“Glue Grant”), we will utilize our early blood transcriptome multi-biomarker development machine learning pipeline to build models for prediction of infections-related MOF outcome among a cohort of blunt trauma patients. Aim 2: we will build prediction models using injury severity scores and other common demographic and clinical variables for infections-related MOF and compare their performance with the multi-biomarker model. We hypothesize that, in comparison to models based on clinical scores, our proposed strategy based on transcriptomic signatures will result in an increasingly accurate prediction and, furthermore, provide insights into the underlying molecular mechanisms leading to MOF after infection. Identification of these molecular mechanisms may ultimately aid in uncovering potential targets for pharmacological interventions. Overall, results from this study may provide the foundation for further studies of infections-related outcome prediction in different blunt trauma cohorts, as well as in cohorts affected by other types of trauma. The methods and findings from this study may also be applicable to other immunocompromised populations, such as cancer patients and post-surgery patients.