Coagulopathy is a pathophysiological disorder affecting the body’s ability to form a stable blood clot. Up to a quarter of trauma patients develop an acute traumatic coagulopathy (ATC) soon after their injury. These patients have a considerably higher risk of bleeding and death since the body’s protective mechanisms to stop bleeding are deranged. Several effective treatment options are available if ATC can be identified early. However, standard laboratory tests to identify ATC take over an hour to produce useable results whereas treatment is most effective if instituted immediately.
The primary aim of the ATC Bayesian Network (BN) is to predict ATC with the earliest available patient information. The ATC BN is able to calculate predictions some of its input variables are unknown. The research is a collaboration between the Risk and Information Management group, Queen Mary University of London , and the Trauma Sciences Unit, Barts and the London School of Medicine and Dentistry.