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Predicting on-going hemorrhage and transfusion requirement after severe trauma: a validation of six scoring systems and algorithms on the TraumaRegister DGU®

Overview of attention for article published in Critical Care, January 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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1 policy source
twitter
1 tweeter

Citations

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99 Dimensions

Readers on

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129 Mendeley
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Title
Predicting on-going hemorrhage and transfusion requirement after severe trauma: a validation of six scoring systems and algorithms on the TraumaRegister DGU®
Published in
Critical Care, January 2012
DOI 10.1186/cc11432
Pubmed ID
Authors

Thomas Brockamp, Ulrike Nienaber, Manuel Mutschler, Arasch Wafaisade, Sigune Peiniger, Rolf Lefering, Bertil Bouillon, Marc Maegele

Abstract

ABSTRACT: 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
Germany 2 2%
Czechia 1 <1%
South Africa 1 <1%
Italy 1 <1%
United Kingdom 1 <1%
Mexico 1 <1%
Spain 1 <1%
United States 1 <1%
Other 0 0%
Unknown 117 91%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 20 16%
Student > Master 20 16%
Researcher 20 16%
Other 18 14%
Student > Bachelor 11 9%
Other 32 25%
Unknown 8 6%
Readers by discipline Count As %
Medicine and Dentistry 106 82%
Nursing and Health Professions 5 4%
Agricultural and Biological Sciences 2 2%
Engineering 2 2%
Computer Science 1 <1%
Other 3 2%
Unknown 10 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 June 2016.
All research outputs
#5,231,148
of 17,353,889 outputs
Outputs from Critical Care
#3,127
of 5,317 outputs
Outputs of similar age
#38,577
of 133,312 outputs
Outputs of similar age from Critical Care
#18
of 77 outputs
Altmetric has tracked 17,353,889 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 5,317 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.7. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 133,312 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.