ORCID Profile
0000-0003-4885-0832
Current Organisation
Alfred Health
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Publisher: MDPI AG
Date: 28-09-2019
Abstract: Background and objectives: Prompt identification of patients with acute traumatic coagulopathy (ATC) is necessary to expedite appropriate treatment. An early clinical prediction tool that does not require laboratory testing is a convenient way to estimate risk. Prediction models have been developed, but none are in widespread use. This systematic review aimed to identify and assess accuracy of prediction tools for ATC. Materials and Methods: A search of OVID Medline and Embase was performed for articles published between January 1998 and February 2018. We searched for prognostic and predictive studies of coagulopathy in adult trauma patients. Studies that described stand-alone predictive or associated factors were excluded. Studies describing prediction of laboratory-diagnosed ATC were extracted. Performance of these tools was described. Results: Six studies were identified describing four different ATC prediction tools. The COAST score uses five prehospital variables (blood pressure, temperature, chest decompression, vehicular entrapment and abdominal injury) and performed with 60% sensitivity and 96% specificity to identify an International Normalised Ratio (INR) of .5 on an Australian single centre cohort. TICCS predicted an INR of .3 in a small Belgian cohort with 100% sensitivity and 96% specificity based on admissions to resuscitation rooms, blood pressure and injury distribution but performed with an Area under the Receiver Operating Characteristic (AUROC) curve of 0.700 on a German trauma registry validation. Prediction of Acute Coagulopathy of Trauma (PACT) was developed in USA using six weighted variables (shock index, age, mechanism of injury, Glasgow Coma Scale, cardiopulmonary resuscitation, intubation) and predicted an INR of .5 with 73.1% sensitivity and 73.8% specificity. The Bayesian network model is an artificial intelligence system that predicted a prothrombin time ratio of .2 based on 14 clinical variables with 90% sensitivity and 92% specificity. Conclusions: The search for ATC prediction models yielded four scoring systems. While there is some potential to be implemented effectively in clinical practice, none have been sufficiently externally validated to demonstrate associations with patient outcomes. These tools remain useful for research purposes to identify populations at risk of ATC.
Publisher: SAGE Publications
Date: 08-04-2020
Abstract: Early identification of trauma patients at risk of developing acute traumatic coagulopathy is important in initiating appropriate, coagulopathy-focused treatment. A clinical acute traumatic coagulopathy prediction tool is a quick, simple method to evaluate risk. The COAST score was developed in Australia and we hypothesised that it could predict coagulopathy and bleeding-related adverse outcomes in other advanced trauma systems. We validated COAST on a single-centre cohort of trauma patients from a trauma centre in Belgium. The COAST score was modified to suit available data we used entrapment, blood pressure, temperature, major chest injury and abdominal injury to calculate the score. Acute traumatic coagulopathy was defined as international normalised ratio .5 or activated partial thromboplastin time s upon arrival of the patient to the hospital. Data were extracted from the local trauma registry on patients that presented between 1 January and 31 December 2015. In all, 133 patients met the inclusion criteria ( years old, available COAST and outcome data) for analysis. The COAST score had an area under the receiver operating characteristics curve of 0.941 (95% CI: 0.884–0.999) and at COAST ≥3, it had 80% sensitivity and 96% specificity. The score also identified patients with higher rates of mortality, blood transfusion and emergent surgery. This retrospective cohort study demonstrated the utility of the COAST score in identifying trauma patients who are likely to have bleeding-related poor outcomes. The early identification of these patients will facilitate timely, appropriate treatment for acute traumatic coagulopathy and minimise the risk of over-treatment. It can also be used to select patients with acute traumatic coagulopathy for trials involving therapeutic agents targeted at acute traumatic coagulopathy.
Publisher: Wiley
Date: 18-09-2019
DOI: 10.1111/TRF.15526
Publisher: Springer Science and Business Media LLC
Date: 29-04-2019
DOI: 10.1007/S00068-019-01142-0
Abstract: Early identification of trauma patients at risk of developing acute traumatic coagulopathy (ATC) is important for initiating appropriate, coagulopathy-focused treatment. A clinical ATC prediction tool is a quick, simple method to evaluate risk. The COAST score was developed and validated in Australia but is yet to be validated on a European population. We validated the ability of the COAST score to predict coagulopathy and adverse bleeding-related outcomes on a large European trauma population. The COAST score was modified and applied to a retrospective cohort of trauma patients from the German Trauma Registry (TR-DGU). The primary outcome was coagulopathy defined as INR > 1.5 or aPTT > 60 s. Secondary outcomes were massive transfusion, blood product requirements, urgent surgery and mortality. The cohort included adult trauma patients with Injury Severity Score > 15 treated in Germany/Austria in 2012-2016. 15,370 cases were included, of which 10.9% were coagulopathic. The COAST score performed with sensitivity 21.6% and specificity 94.2% at a threshold of COAST ≥ 3. The AUROC was 0.625 (95% CI 0.61-0.64). The COAST score also identified patients who had more massive transfusions (15.3% v 1.6%), more emergency surgery (49.6% v 28.2%), and higher early (21.7% v 5.4%) and total in-hospital mortality (38.1% v 14.5%). This large retrospective study demonstrated that the modified COAST score predicts coagulopathy with low sensitivity but high specificity. A positive COAST score identified a group of patients with bleeding-related adverse outcomes. This score appears adequate to act as an inclusion criterion for clinical trials targeting ATC.
No related grants have been discovered for Sophie Thorn.