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: 8511810
• 负责人: Daryl J Kor
• 金额: $ 13.08万
• 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/8511810
• 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（由申请人提供）：急性肺损伤（ALI）是一种毁灭性的术后呼吸系统疾病，给公共卫生资源带来了沉重的负担。由于缺乏有效的治疗方法，开发新的防止ALI的方法可能会更成功。两个关键的知识差距使得开发成功的预防策略变得困难包括： 1）无法在Ali之前早期可靠地识别高危患者，以及对导致ALI的机制的不完全理解。申请人的远距离目标是两个方面：1）成为成功的独立转化临床医生，领导一个多学科研究团队，专注于预防术后ALI，2）制定有效的策略，以防止术后ALI，其中一项，其中一项当今最重要的术后并发症。本应用程序的科学目标是解决 上面确定的两个关键知识差距。为了实现这些目标，该项目提出了以下特定目标。目的1：使用创新的，可扩展的实时围手术期数据捕获策略，开发和验证一个ALI预测模型，该模型包括术前和术中域中的循证变量。 - AIM 1将通过使用允许使用其病历进行研究的患者的现有数据来解决。该数据将用于开发一种工具来预测术后ALI的风险，该工具包括术前和术中变量。这些变量将使用高度创新的数据提取技术捕获，这将允许将来实时连续ALI风险 监视。 该新模型将与最近开发的模型进行比较，该模型仅包括术前变量。 AIM 2：在那些发展术后ALI的人与不使用嵌套的病例对照设计的人相比，在术中术中和早期术后SCD40L和TXA2的血液水平。 - 在AIM 2中，我们将确定有高风险的患者 术后ALI（E 10％机会）。对于那些同意参加研究的人，我们将在手术后和立即获得血液样本，以评估两个不同的血小板激活标记的水平（SCD40L和TXA2）。我们将跟随所有患者，看看谁发展了Ali。然后，我们将比较开发ALI的患者的SCD40L和TXA2水平，以确定这两个标记与ALI的关系。申请人是麻醉学的助理教授，具有重症监护医学特殊认证。申请人的研究兴趣是术后ALI，对防止这种威胁生命的综合症具有特别的兴趣。尽管申请人最近作为KL2指导的职业发展奖获得者的培训为以患者为导向的研究为职业提供了重要的基础，但仍保留三种关键的培训需求，以确保他发展为独立调查。这些培训需求包括：1）生物医学建模； 2）医学信息学和3）肺部生物学/转化免疫学。为了满足这些培训需求，该提案构建了一项全面的职业发展计划和杰出的指导团队。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/免疫学领域的有针对性的教学机会，并在所有这些领域具有额外的“动手”经验。该提案的指导/科学咨询委员会指出，ALI流行病学专家（Ognjen Gajic博士）和肺部生物学/ALI机制（Rolf Hubmayr博士），医学信息学（Christopher Chute Dr.华纳），转化免疫学（基思·诺特森博士）和统计建模（Rickey Carter博士）。咨询团队的所有成员都有强大的指导记录。梅奥诊所的研究环境是世界一流的，并强烈支持初级研究者研究事业的发展。赞助机构提供了所有必要的设施和其他资源，用于拟议的工作和职业发展。