Injury Prevention: Why So Important?

Data driven linear algebraic methods for analysis of molecular pathways: application to disease progression in shock/trauma

MOTIVATION

Although trauma is the leading cause of death for those below 45years of age, there is a dearth of information about the temporal behavior of the underlying biological mechanisms in those who survive the initial trauma only to later suffer from syndromes such as multiple organ failure. Levels of serum cytokines potentially affect the clinical outcomes of trauma; understanding how cytokine levels modulate intra-cellular signaling pathways can yield insights into molecular mechanisms of disease progression and help to identify targeted therapies. However, developing such analyses is challenging since it necessitates the integration and interpretation of large amounts of heterogeneous, quantitative and qualitative data. Here we present the Pathway Semantics Algorithm (PSA), an algebraic process of node and edge analyses of evoked biological pathways over time for in silico discovery of biomedical hypotheses, using data from a prospective controlled clinical study of the role of cytokines in multiple organ failure (MOF) at a major US trauma center. A matrix algebra approach was used in both the PSA node and PSA edge analyses with different matrix configurations and computations based on the biomedical questions to be examined. In the edge analysis, a percentage measure of crosstalk called XTALK was also developed to assess cross-pathway interference.

RESULTS

In the node/molecular analysis of the first 24h from trauma, PSA uncovered seven molecules evoked computationally that differentiated outcomes of MOF or non-MOF (NMOF), of which three molecules had not been previously associated with any shock/trauma syndrome. In the edge/molecular interaction analysis, PSA examined four categories of functional molecular interaction relationships--activation, expression, inhibition, and transcription--and found that the interaction patterns and crosstalk changed over time and outcome. The PSA edge analysis suggests that a diagnosis, prognosis or therapy based on molecular interaction mechanisms may be most effective within a certain time period and for a specific functional relationship.

Early cytokine production risk stratifies trauma patients for multiple organ failure

BACKGROUND

Shock is a prime inciting event for postinjury multiple organ failure (MOF), believed to induce a state of injurious systemic inflammation. In animal models of hemorrhagic shock, early (< 24 hours) changes in cytokine production are an index of the systemic inflammatory response syndrome. However, their predictive value in trauma patients remains to be fully elucidated.

STUDY DESIGN

In a prospective observational pilot study of > 1 year at an urban Level I trauma center, serial (every 4 hours) serum cytokine levels were determined during a 24-hour period using multiplex suspension immunoassay in patients with major torso trauma (excluding severe brain injury) who met criteria for standardized shock resuscitation. Temporal cytokine expression was assessed during shock resuscitation in severe trauma patients to predict risk for MOF. MOF was assessed with the Denver score.

RESULTS

Of 48 study patients (mean age 39 +/- 3 years, 67% men, 88% blunt mechanism, mean Injury Severity Score 25 +/- 2), MOF developed in 11 (23%). MOF patients had a considerably higher mortality (64% versus 3%) and fewer ICU-free days (3.5 +/- 2 versus 17.8 +/- 1.3 days) compared with non-MOF patients. Traditional predictors of MOF, including age (45 +/- 7 versus 38 +/- 3 years; p=0.21), Injury Severity Score (26 +/- 3 versus 25 +/- 2; p=0.67), admission hemoglobin (11.4 +/- 0.9 versus 12.1 +/- 0.5 g/dL; p=0.22), international normalized ratio (1.6 +/- 0.2 versus 1.4 +/- 0.06; p=0.17), and base deficit (9.0 +/- 2 versus 7.1 +/- 0.8; p=0.19), were not significantly different between MOF and non-MOF patients. Statistical analysis identified six candidate predictors of MOF: inducible protein 10, macrophage inflammatory protein-1beta, interleukin-10, interleukin-6, interleukin-1Ra, and eotaxin.

CONCLUSIONS

These data provide insight into cytokine expression during traumatic shock that can enable earlier identification of patients at risk for development of MOF.