Research Article: Translational approaches to coagulopathy after trauma: Towards targeted treatment

Date Published: July 25, 2017

Publisher: Public Library of Science

Author(s): Mitchell Jay Cohen

Abstract: Mitchell J. Cohen discusses why trauma care must go beyond restoring perfusion to target disorders of inflammation and coagulation in severely injured patients.

Partial Text: A decade ago, the notion was widespread that patients experienced coagulopathy solely as an unfortunate iatrogenic effect of well-meaning resuscitative practices. The pioneering shock research of the 1970s led to an emphasis on improving tissue perfusion, in the form of blood flow and oxygen-carrying capacity, as the primary goal of resuscitation. This belief, combined with the separation of blood components by blood banks in the 1970s (at first to improve resource allocation and then to limit transmission of blood-borne infections such as hepatitis C and HIV), resulted in decades of treatment in which trauma patients received large volumes of crystalloid and red blood cells at the expense of perturbations in coagulation and inflammation. When iatrogenic complications resulted, damage control surgery aimed at providing countermeasures, but the root cause of coagulopathy remained eclipsed by the primary emphasis on perfusion.

Understanding the endotheliopathy of trauma can facilitate targeting more effectively the coagulation and inflammation disturbances of severely injured patients. The important progress in hemostatic resuscitation has laid the groundwork, with significant mortality and morbidity reductions. Nonetheless, attempts at definitive trials have been negative and have failed to show benefit in the heterogeneous population of patients with severe traumatic injury [7]. Evidently, a “one-size-fits-all” approach to hemostatic resuscitation under-resuscitates some and over-resuscitates others. In the era of the advent of personalized medicine, it is now essential to understand each patient as possessing individual dynamic physiologic states that should be individually targeted with specific therapies [8]. Recent work suggests that big data approaches and dynamic modeling can identify and track physiologic states and predict clinical trajectories, raising the exciting possibility of providing decision support and driving individualized dynamic treatment [9–12].



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