Hematological and Chemical Profiles in a Porcine Model of Severe Multiple Trauma

Abstract

<b><i>Background:</i></b> Clinical chemistry and hematological tests are widely used to monitor the clinical course of several diseases. However, these parameters are sparse in large-animal models of multiple trauma (MT). Thus, we aimed to provide these missing data to improve future experimental setups in trauma research. <b><i>Methods:</i></b> Male pigs (German Landrace pigs) were randomized into either an MT group (<i>n</i> = 8) including blunt thoracic trauma, tibial fracture, and controlled hemorrhage or a sham group (<i>n</i> = 8) without any trauma. After trauma induction, all animals received intensive care treatment for 72 h under anesthesia, including mechanical ventilation and volume resuscitation. Blood and urine samples were obtained to measure common hematological and chemical parameters before trauma (0 h), after trauma (1.5 h), during resuscitation (2.5 h), after fracture stabilization (3.5 h), and at 12, 24, 48, and 72 h. Statistical analyses were performed using a linear mixed model (group × time) and Welch’s ANOVA. <b><i>Results:</i></b> MT led to a perceptible immunological reaction. Between groups, significantly different time courses of leukocyte counts (<i>p</i> = 0.034) and lymphocyte proportions (<i>p</i> = 0.001) were observed. Moreover, MT changed the time course of total protein (<i>p</i> = 0.006). Significantly lower concentrations compared to sham were found in MT at each single time point starting at 1.5 h to the end of the observation period (all <i>p</i> &#x3c; 0.05). <b><i>Conclusions:</i></b> Our results indicate that a traumatic insult leads to significant alterations in the immune system already shortly after trauma. Together with the additional catabolic reactions observed, these alterations might contribute to the occurrence of later complications. The presented data provide valid references for further experimental setups with prolonged observation times, especially in similar porcine models of MT.