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Updated: May 20, 2026
Integrated Compensatory Responses in a Human Model of Hemorrhage
Published on: November 20, 2016
Predicting on-going hemorrhage and transfusion requirement after severe trauma: a validation of six scoring systems
Introduction:
The early aggressive management of the acute coagulopathy of trauma may improve survival in the trauma population. However, the timely identification of lethal exsanguination remains challenging. This study validated six scoring systems and algorithms to stratify patients for the risk of massive transfusion (MT) at a very early stage after trauma on one single dataset of severely injured patients derived from the TR-DGU (TraumaRegister DGU of the German Trauma Society (DGU)) database.
Methods:
Retrospective internal and external validation of six scoring systems and algorithms (four civilian and two military systems) to predict the risk of massive transfusion at a very early stage after trauma on one single dataset of severely injured patients derived from the TraumaRegister DGU database (2002-2010). Scoring systems and algorithms assessed were: TASH (Trauma-Associated Severe Hemorrhage) score, PWH (Prince of Wales Hospital/Rainer) score, Vandromme score, ABC (Assessment of Blood Consumption/Nunez) score, Schreiber score and Larsen score. Data from 56,573 patients were screened to extract one complete dataset matching all variables needed to calculate all systems assessed in this study. Scores were applied and area-under-the-receiver-operating-characteristic curves (AUCs) were calculated. From the AUC curves the cut-off with the best relation of sensitivity-to-specificity was used to recalculate sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV).
Results:
A total of 5,147 patients with blunt trauma (95%) was extracted from the TR-DGU. The mean age of patients was 45.7 ± 19.3 years with a mean ISS of 24.3 ± 13.2. The overall MT rate was 5.6% (n = 289). 95% (n = 4,889) patients had sustained a blunt trauma. The TASH score had the highest overall accuracy as reflected by an AUC of 0.889 followed by the PWH-Score (0.860). At the defined cut-off values for each score the highest sensitivity was observed for the Schreiber score (85.8%) but also the lowest specificity (61.7%). The TASH score at a cut-off ≥ 8.5 showed a sensitivity of 84.4% and also a high specificity (78.4%). The PWH score had a lower sensitivity (80.6%) with comparable specificity. The Larson score showed the lowest sensitivity (70.9%) at a specificity of 80.4%.
Conclusions:
Weighted and more sophisticated systems such as TASH and PWH scores including higher numbers of variables perform superior over simple non-weighted models. Prospective validations are needed to improve the development process and use of scoring systems in the future.
