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.
Acute lung injury (ALI) is the most common cause of postoperative respiratory failure, and up to 45% of patients who develop ALI after surgery will die in the hospital. There are few available treatments, and prevention may prove more effective than treatment of established ALI. By identifying surgical patients who are at high risk for this serious condition and gaining an improved understanding of the mechanisms underlying postoperative ALI, this application will generate important preliminary data and create a research infrastructure to support future efforts aimed at preventing this life-threatening postoperative complication.