Prediction of critical haemorrhage following trauma: A narrative review

Research output: Contribution to journalReview ArticleResearchpeer-review

6 Citations (Scopus)

Abstract

Introduction: Traumatic haemorrhagic shock can be difficult to diagnose. Models for predicting critical bleeding and massive transfusion have been developed to aid clinicians. The aim of this review is to outline the various available models and report on their performance and validation.

Methods: A review of the English and non-English literature in Medline, PubMed and Google Scholar was conducted from 1990 to September 2015. We combined several terms for i) haemorrhage AND ii) prediction, in the setting of iii) trauma. We included models that had at least two data points. We extracted information about the models, their developments, performance and validation.

Results: There were 36 different models identified that diagnose critical bleeding, which included a total of 36 unique variables. All models were developed retrospectively. The models performed with variable predictive abilities–the most superior with an area under the receiver operating characteristics curve of 0.985, but included detailed findings on imaging and was based on a small cohort. The most commonly included variable was systolic blood pressure, featuring in all but five models. Pattern or mechanism of injury were used by 16 models. Pathology results were used by 15 models, of which nine included base deficit and eight models included haemoglobin. Imaging
was utilised in eight models. Thirteen models were known to be validated, with only one being prospectively validated.

Conclusions: Several models for predicting critical bleeding exist, however none were deemed accurate enough to dictate treatment. Potential areas of improvement identified include measures of variability in vital signs and point of care imaging and pathology testing.
Original languageEnglish
Article number5339
Pages (from-to)1 - 16
Number of pages16
JournalJournal of Emergency Medicine Trauma & Acute Care
Volume2016
Issue number1
DOIs
Publication statusPublished - 2016

Keywords

  • Haemorrhage
  • Haemorrhagic
  • Massive transfusion
  • Prediction
  • Severity of illness index
  • Shock
  • Trauma severity indices
  • Wounds and injuries

Cite this

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title = "Prediction of critical haemorrhage following trauma: A narrative review",
abstract = "Introduction: Traumatic haemorrhagic shock can be difficult to diagnose. Models for predicting critical bleeding and massive transfusion have been developed to aid clinicians. The aim of this review is to outline the various available models and report on their performance and validation.Methods: A review of the English and non-English literature in Medline, PubMed and Google Scholar was conducted from 1990 to September 2015. We combined several terms for i) haemorrhage AND ii) prediction, in the setting of iii) trauma. We included models that had at least two data points. We extracted information about the models, their developments, performance and validation.Results: There were 36 different models identified that diagnose critical bleeding, which included a total of 36 unique variables. All models were developed retrospectively. The models performed with variable predictive abilities–the most superior with an area under the receiver operating characteristics curve of 0.985, but included detailed findings on imaging and was based on a small cohort. The most commonly included variable was systolic blood pressure, featuring in all but five models. Pattern or mechanism of injury were used by 16 models. Pathology results were used by 15 models, of which nine included base deficit and eight models included haemoglobin. Imagingwas utilised in eight models. Thirteen models were known to be validated, with only one being prospectively validated.Conclusions: Several models for predicting critical bleeding exist, however none were deemed accurate enough to dictate treatment. Potential areas of improvement identified include measures of variability in vital signs and point of care imaging and pathology testing.",
keywords = "Haemorrhage, Haemorrhagic, Massive transfusion, Prediction, Severity of illness index, Shock, Trauma severity indices, Wounds and injuries",
author = "Alexander Olaussen and Prasanthan Thaveenthiran and Fitzgerald, {Mark C.} and Jennings, {Paul A.} and Jessica Hocking and Biswadev Mitra",
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Prediction of critical haemorrhage following trauma : A narrative review. / Olaussen, Alexander; Thaveenthiran, Prasanthan; Fitzgerald, Mark C.; Jennings, Paul A.; Hocking, Jessica; Mitra, Biswadev.

In: Journal of Emergency Medicine Trauma & Acute Care, Vol. 2016, No. 1, 5339, 2016, p. 1 - 16.

Research output: Contribution to journalReview ArticleResearchpeer-review

TY - JOUR

T1 - Prediction of critical haemorrhage following trauma

T2 - A narrative review

AU - Olaussen, Alexander

AU - Thaveenthiran, Prasanthan

AU - Fitzgerald, Mark C.

AU - Jennings, Paul A.

AU - Hocking, Jessica

AU - Mitra, Biswadev

PY - 2016

Y1 - 2016

N2 - Introduction: Traumatic haemorrhagic shock can be difficult to diagnose. Models for predicting critical bleeding and massive transfusion have been developed to aid clinicians. The aim of this review is to outline the various available models and report on their performance and validation.Methods: A review of the English and non-English literature in Medline, PubMed and Google Scholar was conducted from 1990 to September 2015. We combined several terms for i) haemorrhage AND ii) prediction, in the setting of iii) trauma. We included models that had at least two data points. We extracted information about the models, their developments, performance and validation.Results: There were 36 different models identified that diagnose critical bleeding, which included a total of 36 unique variables. All models were developed retrospectively. The models performed with variable predictive abilities–the most superior with an area under the receiver operating characteristics curve of 0.985, but included detailed findings on imaging and was based on a small cohort. The most commonly included variable was systolic blood pressure, featuring in all but five models. Pattern or mechanism of injury were used by 16 models. Pathology results were used by 15 models, of which nine included base deficit and eight models included haemoglobin. Imagingwas utilised in eight models. Thirteen models were known to be validated, with only one being prospectively validated.Conclusions: Several models for predicting critical bleeding exist, however none were deemed accurate enough to dictate treatment. Potential areas of improvement identified include measures of variability in vital signs and point of care imaging and pathology testing.

AB - Introduction: Traumatic haemorrhagic shock can be difficult to diagnose. Models for predicting critical bleeding and massive transfusion have been developed to aid clinicians. The aim of this review is to outline the various available models and report on their performance and validation.Methods: A review of the English and non-English literature in Medline, PubMed and Google Scholar was conducted from 1990 to September 2015. We combined several terms for i) haemorrhage AND ii) prediction, in the setting of iii) trauma. We included models that had at least two data points. We extracted information about the models, their developments, performance and validation.Results: There were 36 different models identified that diagnose critical bleeding, which included a total of 36 unique variables. All models were developed retrospectively. The models performed with variable predictive abilities–the most superior with an area under the receiver operating characteristics curve of 0.985, but included detailed findings on imaging and was based on a small cohort. The most commonly included variable was systolic blood pressure, featuring in all but five models. Pattern or mechanism of injury were used by 16 models. Pathology results were used by 15 models, of which nine included base deficit and eight models included haemoglobin. Imagingwas utilised in eight models. Thirteen models were known to be validated, with only one being prospectively validated.Conclusions: Several models for predicting critical bleeding exist, however none were deemed accurate enough to dictate treatment. Potential areas of improvement identified include measures of variability in vital signs and point of care imaging and pathology testing.

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KW - Trauma severity indices

KW - Wounds and injuries

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M3 - Review Article

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JO - Journal of Emergency Medicine Trauma & Acute Care

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