Literature DB >> 22092220

Predicting massive blood transfusion using clinical scores post-trauma.

B Mitra1, T H Rainer, P A Cameron.   

Abstract

BACKGROUND AND OBJECTIVES: Early prediction of massive transfusion (MT) post-trauma may reduce mortality by earlier delivery of blood products. A clinical prediction tool (PWH score) for this purpose was developed at the Prince of Wales Hospital, Hong Kong. The aims of this study were to apply this tool to major trauma patients in Victoria, Australia and compare the score to the Assessment of Blood Consumption (ABC) score and the Trauma-Associated Severe Haemorrhage (TASH) score.
METHODS: A retrospective review of patients entered into the The Alfred Trauma Registry between January 2006 and December 2009 was conducted. The performance of the PWH score to predict MT defined by 5 units of packed red blood cells in 4 h was compared with the ABC and TASH scores. Included patients presented to the Emergency & Trauma Centre from the scene and had had complete datasets with respect to the components of the three scores.
RESULTS: There were 1234 patients included in the study with 195 (15·8%) receiving a MT and an overall mortality of 14·0%. The PWH score had an area under the receiver operating characteristics (ROC) curve of 0·842 (95% CI: 0·820-0·862). The area under the ROC curve of the PWH score was significantly less than that of the TASH score (χ(2)=19·8, P<0·001) and significantly greater than that of the ABC score (χ(2)=9·3, P=0·002).
CONCLUSIONS: The PWH score performs with similar accuracy when applied to an Australian population as in its derivation population. The relative simplicity of the PWH score makes it a viable tool for clinical use, although utility of such tools may be more suited for research in determining inclusion or exclusion criteria for comparative outcome studies.
© 2011 The Author(s). Vox Sanguinis © 2011 International Society of Blood Transfusion.

Entities:  

Mesh:

Year:  2011        PMID: 22092220     DOI: 10.1111/j.1423-0410.2011.01564.x

Source DB:  PubMed          Journal:  Vox Sang        ISSN: 0042-9007            Impact factor:   2.144


  13 in total

1.  External validation of a smartphone app model to predict the need for massive transfusion using five different definitions.

Authors:  E I Hodgman; M W Cripps; M J Mina; E M Bulger; M A Schreiber; K J Brasel; M J Cohen; P Muskat; J G Myers; L H Alarcon; M H Rahbar; J B Holcomb; B A Cotton; E E Fox; D J Del Junco; C E Wade; H A Phelan
Journal:  J Trauma Acute Care Surg       Date:  2018-02       Impact factor: 3.313

2.  If not now, when? The value of the MTP in managing massive bleeding.

Authors:  Mark H Yazer; Jason L Sperry; Andrew P Cap; Jansen H Seheult
Journal:  Blood Transfus       Date:  2020-09-18       Impact factor: 3.443

3.  Clinical gestalt and the prediction of massive transfusion after trauma.

Authors:  Matthew J Pommerening; Michael D Goodman; John B Holcomb; Charles E Wade; Erin E Fox; Deborah J Del Junco; Karen J Brasel; Eileen M Bulger; Mitch J Cohen; Louis H Alarcon; Martin A Schreiber; John G Myers; Herb A Phelan; Peter Muskat; Mohammad Rahbar; Bryan A Cotton
Journal:  Injury       Date:  2015-02-04       Impact factor: 2.586

4.  Fibrinolysis greater than 3% is the critical value for initiation of antifibrinolytic therapy.

Authors:  Michael P Chapman; Ernest E Moore; Christopher R Ramos; Arsen Ghasabyan; Jeffrey N Harr; Theresa L Chin; John R Stringham; Angela Sauaia; Christopher C Silliman; Anirban Banerjee
Journal:  J Trauma Acute Care Surg       Date:  2013-12       Impact factor: 3.313

Review 5.  Massive bleeding in cardiac surgery. Definitions, predictors and challenges.

Authors:  A Petrou; P Tzimas; S Siminelakis
Journal:  Hippokratia       Date:  2016 Jul-Sep       Impact factor: 0.471

6.  Predictive Models and Algorithms for the Need of Transfusion Including Massive Transfusion in Severely Injured Patients.

Authors:  Marc Maegele; Thomas Brockamp; Ulrike Nienaber; Christian Probst; Herbert Schoechl; Klaus Görlinger; Philip Spinella
Journal:  Transfus Med Hemother       Date:  2012-03-08       Impact factor: 3.747

7.  Diversity in clinical management and protocols for the treatment of major bleeding trauma patients across European level I Trauma Centres.

Authors:  Nadine Schäfer; Arne Driessen; Matthias Fröhlich; Ewa K Stürmer; Marc Maegele
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2015-10-01       Impact factor: 2.953

8.  Relationship between Obesity and Massive Transfusion Needs in Trauma Patients, and Validation of TASH Score in Obese Population: A Retrospective Study on 910 Trauma Patients.

Authors:  Audrey De Jong; Pauline Deras; Orianne Martinez; Pascal Latry; Samir Jaber; Xavier Capdevila; Jonathan Charbit
Journal:  PLoS One       Date:  2016-03-24       Impact factor: 3.240

9.  Early Prediction of Ongoing Hemorrhage in Severe Trauma: Presentation of the Existing Scoring Systems.

Authors:  Martin L Tonglet
Journal:  Arch Trauma Res       Date:  2016-06-20

10.  Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity.

Authors:  Nareg H Roubinian; Edward L Murphy; Bix E Swain; Marla N Gardner; Vincent Liu; Gabriel J Escobar
Journal:  BMC Health Serv Res       Date:  2014-05-10       Impact factor: 2.655

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.