Risk Prediction and Mechanistic Evaluation of Postoperative Acute Lung Injury

术后急性肺损伤的风险预测和机制评估

• 批准号: 8278395
• 负责人: Daryl J Kor
• 金额: $ 13.12万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-16 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8278395
• 关键词: Acute; Acute Lung Injury; Address; Advisory Committees; Anesthesiology; Area; Aspirin; Biological Markers; Biology; Blood; Blood Platelets; Blood specimen; CD40 Ligand; Certification; Clinic; Clinical; Complication; Critical Care; Data; Development; Development Plans; Disease; Ensure; Environment; Epidemiology; Evaluation; Foundations; Funding; Future; Goals; Health Resources; Hospitals; Immunologics; Immunology; Incidence; Institution; Interdisciplinary Study; Intervention; Investigation; K-Series Research Career Programs; Knowledge; Lead; Life; Lung; Measurement; Measures; Medical Informatics; Medical Records; Medicine; Mentors; Modeling; National Heart, Lung, and Blood Institute; Operative Surgical Procedures; Pathogenesis; Pathway interactions; Patients; Perioperative; Physiological; Platelet Activation; Platelet Factor 4; Population; Postoperative Complications; Postoperative Period; Prevention; Prevention strategy; Public Health; Records; Research; Research Infrastructure; Research Personnel; Resources; Respiratory Failure; Risk; Risk Assessment; Risk Estimate; Risk Factors; Role; Scientist; Serum; Statistical Models; Syndrome; Techniques; Thromboxane A2; Time; Training; Work; biomedical informatics; career; career development; case control; design; effective therapy; evidence base; experience; high risk; improved; injury prevention; innovation; insight; interest; lung injury; member; mortality; multidisciplinary; neutrophil; novel; patient oriented research; prevent; professor; programs; respiratory; response; skills; tool

DESCRIPTION (provided by applicant): Acute lung injury (ALI) is a devastating postoperative respiratory condition that places a heavy burden on public health resources. As effective treatments are lacking, developing new ways to prevent ALI will likely be more successful. Two critical knowledge gaps that make it difficult to develop successful prevention strategies include: 1) an inability to reliably identify high-risk patients early, before ALI is already present, and 2 an incomplete understanding of the mechanisms that lead to ALI. The long-range goals of the applicant are two-fold: 1) to become a successful independent translational clinician-scientist leading a multidisciplinary research team focused on the prevention of postoperative ALI, and 2) to develop effective strategies to prevent postoperative ALI, one of the most significant postoperative complications today. The scientific objectives of this application are to address the two critical knowledge gaps identified above. To achieve these objectives, the project proposes the following specific aims. Aim 1: To develop and validate an ALI prediction model that includes evidence-based variables from both the preoperative and intraoperative domains using innovative, scalable, real-time perioperative data capture strategies. - Aim 1 will be addressed by using existing data from patients who have allowed the use of their medical records for research. This data will be used to develop a tool to predict risk of postoperative ALI which includes both preoperative and intraoperative variables. The variables will be captured using highly innovative data extraction techniques which will allow future real-time continuous ALI risk surveillance. This new model will be compared to a recently developed model which includes only preoperative variables. Aim 2: To compare blood levels of intraoperative and early postoperative sCD40L and TXA2 in those who develop postoperative ALI versus those who do not using a nested case-control design. - In aim 2, we will identify patients who are at high risk for postoperative ALI (e 10% chance). For those who agree to be in the study, we will then obtain blood samples during and immediately after surgery to assess the levels of two different markers of platelet activation (sCD40L and TXA2). We will follow all patients to see who develops ALI. We will then compare the levels of sCD40L and TXA2 from patients who developed ALI to similar patients who did not, to determine how these two markers are related to ALI. The applicant is an Assistant Professor of Anesthesiology with special certification in Critical Care Medicine. The applicant's research interest is postoperative ALI with specific interest in preventing this life-threatening syndrome. While the applicant's recent training as a KL2 Mentored Career Development Award recipient has provided an essential foundation for a career in patient-oriented research, three critical training needs remain to ensure his progression to independent investigation. These training needs include: 1) biomedical modeling; 2) medical informatics, and 3) lung biology/translational immunology. To address these training needs, this proposal has constructed a comprehensive career development plan and an outstanding mentoring team. The training plan includes targeted didactic opportunities in the areas of biomedical modeling, biomedical informatics, and lung biology/immunology with additional "hands-on" experience in all of these areas. This proposal's mentoring/scientific advisory committee has noted experts in ALI epidemiology (Dr. Ognjen Gajic) and lung biology/ALI mechanism (Dr. Rolf Hubmayr), medical informatics (Dr. Christopher Chute), perioperative respiratory complications (Dr. David O. Warner), translational immunology (Dr. Keith Knutson) and statistical modeling (Dr. Rickey Carter). All members of the advisory team have strong mentoring track records. The research environment at Mayo Clinic is world-class and strongly supports the development of junior investigator's research careers. All of the necessary facilities and other resources for the proposed work and career development are available at the sponsoring institution.

描述(申请人提供)：急性肺损伤(ALI)是一种毁灭性的术后呼吸道疾病，给公共卫生资源带来沉重负担。由于缺乏有效的治疗方法，开发预防ALI的新方法可能会更成功。使制定成功的预防战略变得困难的两个关键知识差距包括： 1)在ALI已经存在之前，不能可靠地及早识别高危患者，以及2对导致ALI的机制的不完全理解。申请者的长期目标有两个：1)成为一名成功的独立翻译临床医生兼科学家，领导一个专注于术后ALI预防的多学科研究团队，以及2)开发有效的策略来预防术后ALI，这是当今最重要的术后并发症之一。本应用程序的科学目标是解决 上面指出的两个关键知识差距。为实现这些目标，该项目提出了以下具体目标。目的1：利用创新的、可扩展的、实时的围手术期数据采集策略，开发和验证ALI预测模型，该模型包括来自术前和术中领域的循证变量。-目标1将通过使用允许使用其医疗记录进行研究的患者的现有数据来实现。这些数据将被用来开发一种工具来预测术后ALI的风险，包括术前和术中变量。变量将使用高度创新的数据提取技术来捕获，这将允许未来实时连续的ALI风险 监视系统。这一新模型将与最近开发的仅包括术前变量的模型进行比较。目的：对术中和术后早期发生ALI的患者和未采用嵌套式病例对照设计的患者的血sCD40L和TXA2水平进行比较。-在目标2中，我们将确定高危患者 对于术后ALI(10%的几率)。对于那些同意参与这项研究的人，我们将在手术期间和术后立即采集血样，以评估两种不同的血小板激活标志物(sCD40L和TXA2)的水平。我们将跟踪所有患者，看看谁会发展成ALI。然后，我们将比较发生ALI的患者和没有发生ALI的类似患者的sCD40L和TXA2水平，以确定这两个标志物与ALI的关系。申请人是一名麻醉学助理教授，拥有重症监护医学的特殊认证。申请人的研究兴趣是术后ALI，特别是预防这种危及生命的综合征。虽然申请人最近作为KL2指导职业发展奖获得者接受的培训为以患者为导向的研究职业生涯提供了重要的基础，但仍有三个关键培训需求确保他进入独立调查阶段。这些培训需求包括：1)生物医学建模；2)医学信息学；3)肺部生物学/翻译免疫学。针对这些培训需求，这项建议构建了全面的职业发展计划和一支优秀的指导团队。培训计划包括在生物医学建模、生物医学信息学和肺部生物学/免疫学领域有针对性的教学机会，并在所有这些领域提供额外的实践经验。这项提案的指导/科学咨询委员会注意到了以下领域的专家：ALI流行病学(Ognjen Gajic博士)和肺生物学/ALI机制(Rolf Hubmayr博士)、医学信息学(Christopher Chant博士)、围手术期呼吸道并发症(David O.Warner博士)、转换免疫学(Keith Knutson博士)和统计建模(Rickey Carter博士)。顾问团队的所有成员都有良好的指导记录。梅奥诊所的研究环境是世界级的，有力地支持了初级调查员研究事业的发展。提议的工作和职业发展的所有必要设施和其他资源都可在赞助机构获得。