Severe trauma injury renders patients vulnerable to infections and subsequent risk of infections-related outcomes, including multiple organ failure/dysfunction syndrome (MOF/MODS), a major cause of mortality and morbidity. Although it is well-established that infection is a major risk factor for MOF, not all patients who experience nosocomial infections develop MOF, highlighting the importance of considering the underlying molecular biological mechanisms of heterogeneity in susceptibility to MOF development after infections (ie. infections-related MOF). In current clinical practices, MOF-specific score systems based on physiological measurements such as the Denver and Marshall Scores are monitored and used to diagnose patients with MOF after its onset. Here we propose to build prediction models for infections-related MOF before its onset using molecular signatures in order to significantly increase prediction accuracy. Methods of rapid (ie. immediately after the detection of infection) and accurate identification of patients who are highly susceptible to infections- related outcomes are expected to aid in informed decision-making and ensuring appropriate delivery of preventative measures to control MOF incidence. Such methods may thus result in improved health of patients and reduced health care costs. This proposal aims to employ an unbiased computational approach to investigate genome-wide transcriptome profiles and develop a panel of biomarkers to predict infections-related MOF immediately after the detection of infection. Previous transcriptome studies in the context of infections often focus on patient responses to infection. In contrast, we propose to focus on biomarker panel development to predict a specific infections-related adverse outcome before it occurrs.
Two Aims are proposed to predict the outcome of infections-related MOF among blunt trauma patients, a population that is highly susceptible to infections.
Aim 1 : using blood samples from the Inflammation and the Host Response to Injury Study (?Glue Grant?), we will utilize our early blood transcriptome multi-biomarker development machine learning pipeline to build models for prediction of infections-related MOF outcome among a cohort of blunt trauma patients.
Aim 2 : we will build prediction models using injury severity scores and other common demographic and clinical variables for infections-related MOF and compare their performance with the multi-biomarker model. We hypothesize that, in comparison to models based on clinical scores, our proposed strategy based on transcriptomic signatures will result in an increasingly accurate prediction and, furthermore, provide insights into the underlying molecular mechanisms leading to MOF after infection. Identification of these molecular mechanisms may ultimately aid in uncovering potential targets for pharmacological interventions. Overall, results from this study may provide the foundation for further studies of infections-related outcome prediction in different blunt trauma cohorts, as well as in cohorts affected by other types of trauma. The methods and findings from this study may also be applicable to other immunocompromised populations, such as cancer patients and post-surgery patients.
Severe trauma renders patients immunocompromised and vulnerable to infections that often lead to life- threatening conditions, such as multiple organ failure (MOF). Our study proposes to provide a personalized medicine strategy for rapidly and accurately identifying patients who are at high risk of developing MOF immediately after they experience infection. Early risk profiling will aid clinicians in informed decision-making to improve patient outcomes and may also lead to the development of new preventative methods.
