San Francisco Injury Center (SFIC) »  Full Research Descriptions »  Use of Tissue Oxygen Monitoring in Critically Injured Patients

Use of Tissue Oxygen Monitoring in Critically Injured Patients

Project Director/Lead Investigator: Mitchelle Cohen, MD

Brief summary of project: Our assessment of resuscitation status in trauma patients is limited by a lack of precise markers that reflect perfusion of critical organ beds. The purpose of this prospective observational study of severely injured polytrauma patients is to investigate tissue oxygen tension and microcirculatory flow as markers for resuscitation status. The study methodology is based on our previous laboratory and clinical work measuring brain and muscle tissue oxygen during hemorrhagic shock and resuscitation. Data collected from the initial pilot study will be used to develop hypotheses and interventional studies using tissue oxygen tension and microcirculatory flow as guides for resuscitation of trauma patients.

Specific Aims:
1. To establish critically abnormal levels of tissue oxygen partial pressure (PmO2) and microcirculatory flow (Qmc) in the deltoid muscle of severely injured patients as a surrogate for the adequacy of global tissue oxygenation in critically-injured patients.
2. To determine the effects of blood transfusion on peripheral muscle Qmc and PmO2 after severe injury.
3. To determine the effects of global hypoperfusion on peripheral muscle Qmc and PmO2 after severe injury.
4. To determine the effects of central and peripheral muscle temperature changes on Qmc and PmO2 after severe injury.
5. To determine the effects of fluid resuscitation on Qmc and PmO2 after severe injury.
6. To determine the effects of vasoactive pharmacologic agents on Qmc and PmO2 after severe injury.
7. To correlate PmO2 and Qmc with standard clinical measurements of resuscitation such as base deficit, mean arterial pressure, along with the incidence of sequelae from hypoperfusion (infections, organ failure/dysfunction) and outcomes (duration of mechanical ventilation, intensive care unit stay, hospital length-of-stay) in critically injured patients.
8. To apply statistical and bioinformatics techniques to visualize and quantify response of key physiological measurements such as MAP, base deficit, Qmc and PmO2 to administration of vasoactive drugs, fluid boluses and hyperoxia.

Studies and Results: In our sample-population analysis, we concluded: 1) that PmO2 correlates with base deficit and offers a minimally invasive, continuous guide for resuscitation, 2) that initial low values for either PmO2 or StO2 are associated with post-injury complications, 3) that PmO2 can be used to identify patients in the state of occult under-resuscitation who remain at risk for developing infection and Multiple Organ Failure (MOF), and 4) that the variability in deltoid StO2 readings make this reading impractical for use in the intensive care unit.

Data from 9 patients has been analyzed and an abstract describing determinants of microvascular flow (FLOW) in relation to peripheral muscle oxygen (PmO2) was submitted for presentation at the American Association for the Surgery of Trauma in September of last year. The presentation was well received and the associated manuscript was submitted to the Journal of Trauma. We hypothesized that low flow would correlate with low PmO2 in under resuscitated patients. As with our prior findings, PmO2 was higher in resuscitated patients. Interestingly, FLOW was significantly decreased in resuscitated patients. Stepwise regression indicates that FLOW increases with inspired oxygen, phenylephrine dose, and presence of head injury and is decreased with higher MAP and PmO2 (all p<0.05).

We continue to advance our unique research into the use of bioinformatic clustering analysis. We have published two papers showing that clustering methodologies from bioinformatics are applicable to continuous rapidly changing multivariate physiologic data in critically injured patients - the first article of this kind for this patient population, yielding important insight into patient physiology and outcomes. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.

In July 2010 we recruited Matt Kutcher, MD as a research resident who has been working extensively with our collaborator Alan Hubbard, PhD at UC Berkeley on the informatics portion of the Tissue Oxygen project. We now have a dataset that includes patients enrolled in the tissue oxygen monitoring study as well as other patients for a total of approximately 250 patients for bioinformatic analysis. In the next year, we intend to focus on enrolling patients who have significant torso/extremity injuries (they may or may not have head injuries). We continue our work on the computationally intensive analysis. We have included our coagulation work (see project below) to provide more data and other outcomes in this analysis.
Significance: Contrary to our hypothesis, muscle microvascular blood flow was significantly higher in under resuscitated patients and declines as PmO2 rises. This suggests that tissue oxygen content may be the primary driving force for peripheral perfusion and thus a worthy target to monitor during resuscitation.

Significance: Contrary to our hypothesis, muscle microvascular blood flow was significantly higher in under resuscitated patients and declines as PmO2 rises. This suggests that tissue oxygen content may be the primary driving force for peripheral perfusion and thus a worthy target to monitor during resuscitation. Our groundbreaking work in the field of complex systems bioinformatics has significant potential to reduce injury severity, disability, and death on a large scale. We show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

Future: Moving forward from our interim data analysis, we continue to add patient data for the observational phase of the study. We continue to use complex systems analysis to determine predictors of flow and oxygenation in the muscle after injury.

 

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