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- 1Department of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia [1]
- 1University of Aberdeen, Aberdeen, Scotland [1]
- 2Department of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Australia [1]
- 2Emergency & Trauma Centre, The Alfred Hospital, Melbourne, Australia [1]
- 3Emergency & Trauma Centre, The Alfred Hospital, Melbourne, Australia [1]
- 3Trauma Service, The Alfred Hospital, Melbourne, Australia [1]
- 4National Trauma Research Institute, The Alfred Hospital, Melbourne, Australia [1]
- 4Trauma Service, The Alfred Hospital, Melbourne, Australia [1]
- 5Monash School of Medicine, Monash University, Australia [1]
- 5National Trauma Research Institute, The Alfred Hospital, Melbourne, Australia [1]
- 6College of Health and Biomedicine, Victoria University, Melbourne, Australia [1]
- 6Department of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia [1]
- 7Department of Epidemiology & Preventive Medicine, Monash University, Australia [1]
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Diagnostic performance of the cardiac FAST in a high-volume Australian trauma centre
Background: Cardiac injury is uncommon but it is important to diagnose in order to prevent subsequent complications. Extended focused assessment with sonography in trauma (eFAST) allows rapid evaluation of the pericardium and thorax. The objective of this study was to describe cardiac injuries presenting to a major trauma centre and the diagnostic performance of eFAST in detecting haemopericardium as well as broader cardiac injuries. Methods: Data of patients with severe injuries and diagnosed cardiac injuries (Injury Severity Score >12 and AIS 2008 codes for cardiac injuries) were extracted from The Alfred Trauma Registry over a four-year period from July 2010 to June 2014. The initial eFAST results were compared to those of the final diagnosis which were determined after analysing imaging results and intraoperative findings. Results: Thirty patients who were identified with cardiac injuries met the inclusion criteria. Among these 22 patients sustained injuries under the scope of eFAST of which a positive eFAST scan in the pericardium was reported in 13 (59%) patients while nine (41%) patients had a negative scan. This resulted in a sensitivity of 59% (95% CI: 36.7%–78.5%). The sensitivity of detecting any cardiac injuries was lower at 43.3% (95% CI: 26.0–62.3). Conclusions: The low sensitivities of eFAST for detecting cardiac injuries and haemopericardium demonstrate that a negative result cannot be used in isolation to exclude cardiac injuries. A high index of suspicion for cardiac injury remains essential. Adjunct diagnostic modalities are indicated for the diagnosis of cardiac injury following major trauma.
Prediction of critical haemorrhage following trauma: A narrative review
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.