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

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10322737
• 关键词: APACHE II; Adult; Affect; Area; Assessment tool; Biological; Biological Markers; Blood; Blood specimen; Blunt Trauma; Cancer Patient; Caring; Clinical; Clinical Data; Clinical assessments; Cohort Studies; Confidence Intervals; Decision Making; Detection; Development; Diagnosis; Early Diagnosis; Ensure; Etiology; Foundations; Functional disorder; Glossary; Glues; Goals; Grant; Health; Health Care Costs; Heterogeneity; Immune response; Immunocompromised Host; Immunosuppression; Incidence; Infection; Infection prevention; Inflammation; Injury; Injury Severity Score; Intervention; Lead; Life; Machine Learning; Measurement; Methods; Modeling; Molecular; Molecular Profiling; Monitor; Morbidity - disease rate; Multiple Organ Failure; Nosocomial Infections; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Performance; Pharmacology; Physiological; Population; Predictive Value; Predisposition; Preventive measure; Prognosis; Prognostic Marker; Receiver Operating Characteristics; Resources; Risk; Risk Factors; Severities; Sterility; Syndrome; System; Trauma; Trauma patient; Work; adverse outcome; base; biomarker development; biomarker discovery; biomarker panel; clinical database; clinical practice; cohort; experience; genome-wide; high risk; improved; infection risk; innovation; insight; machine learning pipeline; mortality; mortality risk; new therapeutic target; outcome prediction; patient oriented; patient response; personalized medicine; precision medicine; predictive modeling; prognostic model; prognostic tool; rapid technique; resilience; response; response to injury; tool development; transcriptome; transcriptomics

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.

严重创伤使患者易受感染，随后出现感染相关风险 结果，包括多器官衰竭/功能障碍综合征（MOF/MODS），死亡的主要原因， 发病率虽然感染是MOF的主要危险因素是公认的，但并不是所有感染的患者都是MOF的主要危险因素。 经验医院感染发展多器官功能衰竭，强调考虑潜在的重要性 感染后对MOF发展的易感性的异质性的分子生物学机制（即， 感染相关的MOF）。在目前的临床实践中，基于生理学的MOF特异性评分系统 监测诸如Denver和马歇尔评分的测量并用于诊断患有MOF的患者 发病后。在这里，我们建议建立预测模型，感染相关的多器官功能衰竭发病前，使用 分子特征，以显著提高预测准确性。快速的方法（如立即 在检测到感染后）和准确识别高度易受感染的患者- 预计相关成果将有助于作出知情决策，并确保适当提供 采取预防措施控制MOF的发生。因此，这些方法可以改善患者的健康状况 降低医疗费用。这项建议旨在采用一种无偏的计算方法来调查 全基因组转录谱，并开发一组生物标志物来预测感染相关的MOF 在发现感染后立即进行。以前在感染背景下的转录组研究通常 关注病人对感染的反应相比之下，我们建议专注于生物标志物面板的开发， 在发生之前预测特定感染相关的不良后果。提出了两个目标来预测 钝器创伤患者中感染相关多器官衰竭的结局，该人群极易受到感染 感染.目的1：使用来自炎症和宿主对损伤的反应研究的血液样品（“Glue 格兰特”），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， 建立模型预测钝性创伤患者队列中感染相关的MOF结局。目标二： 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建立预测模型， 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组签名的策略将 导致越来越准确的预测，而且，提供了对潜在分子的见解 感染后导致MOF的机制。这些分子机制的鉴定可能最终有助于 发现药物干预的潜在目标。总的来说，这项研究的结果可以提供 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， 与其他类型创伤的受影响人群一样。本研究的方法和发现也可能适用于 其他免疫功能低下的人群，如癌症患者和手术后患者。