Potential early predictors for outcomes of experimental hemorrhagic shock induced by uncontrolled internal bleeding in rats.

Abstract

Uncontrolled hemorrhage, resulting from traumatic injuries, continues to be the leading cause of death in civilian and military environments. Hemorrhagic deaths usually occur within the first 6 hours of admission to hospital; therefore, early prehospital identification of patients who are at risk for developing shock may improve survival. The aims of the current study were: 1. To establish and characterize a unique model of uncontrolled internal hemorrhage induced by massive renal injury (MRI), of different degrees (20-35% unilateral nephrectomy) in rats, 2. To identify early biomarkers those best predict the outcome of severe internal hemorrhage. For this purpose, male Sprague Dawley rats were anesthetized and cannulas were inserted into the trachea and carotid artery. After abdominal laparotomy, the lower pole of the kidney was excised. During 120 minutes, hematocrit, pO2, pCO2, base excess, potassium, lactate and glucose were measured from blood samples, and mean arterial pressure (MAP) was measured through arterial tracing. After 120 minutes, blood loss was determined. Statistical prediction models of mortality and amount of blood loss were performed. In this model, the lowest blood loss and mortality rate were observed in the group with 20% nephrectomy. Escalation of the extent of nephrectomy to 25% and 30% significantly increased blood loss and mortality rate. Two phases of hemodynamic and biochemical response to MRI were noticed: the primary phase, occurring during the first 15 minutes after injury, and the secondary phase, beginning 30 minutes after the induction of bleeding. A Significant correlation between early blood loss and mean arterial pressure (MAP) decrements and survival were noted. Our data also indicate that prediction of outcome was attainable in the very early stages of blood loss, over the first 15 minutes after the injury, and that blood loss and MAP were the strongest predictors of mortality.