Friday, April 26, 2024
4/26/2024
PARENT R01 PROJECT ABSTRACT The initial resuscitation of a trauma patient is often described as chaos and the clinician directing the care must create calm while making life and death decisions often with inadequate information. Despite some advances in understanding the biology of hemorrhage, injury still accounts for over 5 million deaths per year, represents 1 out of every 10 deaths worldwide, and remains the leading U.S. cause of death for those under 45. While > 90% of trauma patients do well, the largest contribution to preventable death remains for those suffering from hemorrhage and its related complications. Trauma has a known time zero of onset which makes it an ideal model to study the immediate pathophysiologic changes associate with hemorrhage that lead to differential outcome. To date, treatment pathways are considered rudimentary reflecting attempts to optimize outcome based upon the average treatment effect, rather than being adaptable for unique patient phenotypes. The parent R01 proposal is focused on exploring a deeper understanding of the mechanistic modeling of initial patient response to injury (Aim 1) and coupling this with improved real-time point of care bedside decision support technology (Aim 2) to identify early those at risk of poor outcome. The net product of the parent proposal is to define novel patient phenotypes that may require precision resuscitation approaches to maximize outcome following hemorrhage. The goal of the parent R01 is to develop digital biomarkers for precision trauma resuscitation with a focus on understanding the cross talk between the inflammatory and coagulation profiles that occur as a systemic response to traumatic injury. The overall goal of the parent R01 project remains unchanged and are to address limitations of our current knowledge by improving forecasts of patient outcome trajectory at the point of care for those suffering from traumatic injury. The parent R01 project addresses these gaps through two interrelated aims which also remain unchanged: AIM 1. To develop a knowledge network (neural net) approach for characterizing early patient trajectory following hemorrhage. We hypothesize that (1A) predictive trajectories for mortality and complications can be ascertained through deep learning approaches; (1B) the addition of biologic data (inflammatory and coagulation markers) will further improve the prediction of patient states or unique phenotypic patient profiles (also known as digital biomarkers) attributable to differential outcome, and (1C) these phenotypes could be utilized to improve earlier recognition of patients off their predicted trajectory and thus, optimize triage and treatment pathways. AIM 2. To develop pilot prediction models for the detection of occult hemorrhage through the integration of high fidelity, integrated point-of-care data and bedside imaging. We hypothesize that the poor sensitivity for prediction of occult hemorrhage can be improved by developing (2A) an automated computer algorithm using advanced machine learning to detect any free fluid in the abdominal cavity on point- of-care sonographic images (2B) by integrating multimodality data sources with 2A, we can predict clinically significant occult hemorrhage, and (2C) combining the imaging prediction models with the knowledge network in Aim 1, we can develop pilot enhanced digital biomarkers to identify patients suffering from clinically occult hemorrhage, those most likely to develop complications, and those most likely to succumb to their injuries. Our work to date on the Parent R01 has shown that there is differential risk of thrombotic, bleeding, respiratory, and mortality outcomes based on initial coagulation profiles3-4. Our findings have suggested that biology plays a major factor in distinguishing between two seemingly identical patients who have disparate outcomes following critically injury3-19. This includes demonstrating that severely injured patients initially suffer from bleeding due to hyperfibrinolysis, platelet dysfunction, and disordered coagulation. Those who survive rapidly transition to a state of hypercoagulability. The mechanisms for this remains elusive but disordered inflammatory cytokine upregulation has been implicated and we continue to explore this in the parent R01.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
