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: 8683228
• 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/8683228
• 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）是一种破坏性的术后呼吸系统疾病，给公共卫生资源带来沉重负担。由于缺乏有效的治疗方法，开发新的方法来预防ALI可能会更成功。造成难以制定成功的预防战略的两个关键知识差距包括： 1)在ALI发生之前，不能可靠地早期识别高危患者，2对导致ALI的机制不完全理解。申请人的长期目标有两个方面：1）成为一名成功的独立翻译临床医生-科学家，领导一个专注于预防术后ALI的多学科研究团队，2）制定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。本申请的科学目标是解决 上文确定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。目标1：开发并验证ALI预测模型，该模型包括来自术前和术中领域的循证变量，使用创新的、可扩展的、实时的围手术期数据采集策略。 - 目标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。这些数据将用于开发一种预测术后ALI风险的工具，包括术前和术中变量。将使用高度创新的数据提取技术捕获变量，这将允许未来实时连续的ALI风险 监视 该新模型将与最近开发的仅包括术前变量的模型进行比较。目标二：采用巢式病例对照设计，比较发生术后ALI患者与未发生术后ALI患者术中和术后早期sCD 40 L和TXA 2的血液水平。 - 在目标2中，我们将确定高危患者 术后ALI（10%的机会）。对于同意参加研究的患者，我们将在手术期间和手术后立即采集血液样本，以评估两种不同的血小板活化标志物（sCD 40 L和TXA 2）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sCD 40 L和TXA 2的水平，从患者谁开发的ALI和类似的患者谁没有，以确定这两个标志物是如何与ALI。申请人是麻醉学助理教授，拥有重症监护医学特殊认证。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。虽然申请人最近的培训作为一个KL 2辅导职业发展奖获得者提供了一个重要的基础，在以病人为导向的研究的职业生涯，三个关键的培训需求仍然是确保他的进展到独立的调查。这些培训需求包括：1）生物医学建模; 2）医学信息学; 3）肺生物学/转化免疫学。为满足这些培训需求，本提案制定了一个全面的职业发展计划和一个出色的辅导团队。培训计划包括生物医学建模，生物医学信息学和肺生物学/免疫学领域的有针对性的教学机会，以及所有这些领域的额外“动手”经验。本提案的指导/科学咨询委员会已注意到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 Chute博士）、围手术期呼吸并发症（大卫O. Warner）、转化免疫学（基思克努森博士）和统计建模（里基卡特博士）。咨询团队的所有成员都有很好的指导记录。马约诊所的研究环境是世界一流的，并大力支持初级研究者的研究事业的发展。所有必要的设施和其他资源，为拟议的工作和职业发展可在赞助机构。

项目成果

Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators.

• DOI: 10.1001/jamasurg.2016.4065
• 发表时间: 2017-02-01
• 期刊: JAMA surgery
• 影响因子: 16.9
• 作者: Fernandez-Bustamante A;Frendl G;Sprung J;Kor DJ;Subramaniam B;Martinez Ruiz R;Lee JW;Henderson WG;Moss A;Mehdiratta N;Colwell MM;Bartels K;Kolodzie K;Giquel J;Vidal Melo MF
• 通讯作者: Vidal Melo MF

Lung Injury Prediction Score in Hospitalized Patients at Risk of Acute Respiratory Distress Syndrome.

• DOI: 10.1097/ccm.0000000000002001
• 发表时间: 2016-12
• 期刊: Critical care medicine
• 影响因子: 8.8
• 作者: Soto GJ;Kor DJ;Park PK;Hou PC;Kaufman DA;Kim M;Yadav H;Teman N;Hsu MC;Shvilkina T;Grewal Y;De Aguirre M;Gunda S;Gajic O;Gong MN
• 通讯作者: Gong MN

Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications.

• DOI: 10.1111/j.1537-2995.2012.03886.x
• 发表时间: 2013-06
• 期刊: Transfusion
• 影响因子: 2.9
• 作者: Clifford L;Singh A;Wilson GA;Toy P;Gajic O;Malinchoc M;Herasevich V;Pathak J;Kor DJ
• 通讯作者: Kor DJ

Prevention of acute respiratory distress syndrome.

• DOI: 10.1097/mcc.0000000000000174
• 发表时间: 2015-02
• 期刊: Current opinion in critical care
• 影响因子: 3.3
• 作者: Festic E;Kor DJ;Gajic O
• 通讯作者: Gajic O