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

基于多生物标志物的预测工具开发，以确定严重钝性创伤患者感染相关结果的风险

基本信息

批准号：
10322737
负责人：
Amy Tsurumi
金额：
$ 8.4万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2023-12-31
项目状态：
已结题

项目摘要

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.

严重的创伤使患者容易受到感染以及随后发生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），这是死亡的主要原因尽管感染是 MOF 的主要危险因素已被证实，但并非所有患者都会出现这种情况。经历过医院感染而发展为 MOF，强调考虑潜在因素的重要性感染后 MOF 发育易感性异质性的分子生物学机制（即感染相关的 MOF）在当前的临床实践中，基于生理学的 MOF 特定评分系统。监测丹佛评分和马歇尔评分等测量结果并用于诊断 MOF 患者在这里，我们建议在发病前建立与感染相关的 MOF 的预测模型。分子特征，以显着提高快速（即立即）方法的预测准确性。检测到感染后）并准确识别出高易感染患者—— 相关成果预计将有助于做出知情决策并确保适当交付因此，控制 MOF 发病率的预防措施可能会改善患者的健康状况。该提案旨在采用公正的计算方法进行调查。全基因组转录组图谱并开发一组生物标志物来预测感染相关的 MOF 以前在感染背景下进行的转录组研究通常是在检测到感染后立即进行的。相比之下，我们建议重点关注患者对感染的反应。在发生之前预测与感染相关的特定不良结果。提出了两个目标来预测该不良结果。钝性创伤患者中感染相关 MOF 的结果，该人群极易受到感染目标 1：使用来自炎症和宿主对损伤的反应研究的血液样本（“胶水”） Grant”），我们将利用我们的早期血液转录组多生物标志物开发机器学习管道建立模型来预测钝性创伤患者队列中感染相关的 MOF 结果目标 2：我们将使用伤害严重程度评分和其他常见的人口统计和临床变量建立预测模型感染相关的 MOF 并将其性能与多生物标志物模型进行比较。与基于临床评分的模型相比，我们提出的基于转录组特征的策略将导致越来越准确的预测，此外，提供对潜在分子的见解感染后导致 MOF 的机制的鉴定可能最终有助于确定感染后发生 MOF 的机制。总体而言，这项研究的结果可能会提供药物干预的潜在目标。也为进一步研究不同钝性创伤队列中感染相关的结果预测奠定了基础正如受其他类型创伤影响的队列一样，本研究的方法和结果也可能适用。其他免疫功能低下的人群，例如癌症患者和手术后患者。