Injury
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Splenic artery embolisation (SAE) has been shown to be an effective treatment for haemodynamically stable patients with high-grade blunt splenic injury. However, there are no local estimates of how much treatment costs. The purpose of this study was to evaluate the cost of providing SAE to patients in the setting of blunt abdominal trauma at an Australian level 1 trauma centre. ⋯ Splenic embolisation is a low-cost procedure for management of blunt splenic injury. The cost to provide SAE at our centre was much lower than previously modelled data from overseas studies. From a cost perspective, the use of ICU for monitoring after the procedure significantly increased cost and necessity may be considered on a case-by-case basis. Further research is advised to directly compare the cost of SAE and splenectomy in an Australian setting.
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Self-inflicted injury is a leading cause of death worldwide. It is hypothesized that due to instincts for self-preservation, the severity of abdominal injury would be decreased following suicidal self-stabbing in comparison to stab wounds from assault, and therefore a more conservative management might be considered. ⋯ In this study, patients with isolated self-inflicted abdominal injuries had higher mortality and more frequently underwent abdominal surgery.
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Management of colon injuries has significantly evolved in the recent decades resulting in considerably decreased morbidity and mortality. We set out to investigate penetrating colon injuries in a high-volume urban academic trauma center in South Africa. ⋯ Contemporary overall complication rate remains high in penetrating colon injuries, however, anastomotic leak rate is decreasing. Colon injury associated mortality is related to overall injury burden and hemorrhage rather than to colon injuries.
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Trauma injury severity scores are currently calculated retrospectively from the electronic health record (EHR) using manual annotation by certified trauma coders. Natural language processing (NLP) of clinical documents in the EHR may enable automated injury scoring. We hypothesize that NLP with machine learning can discriminate between cases of severe and non-severe injury to the thorax after trauma. ⋯ Both CUIs and unigrams demonstrated excellent discrimination in predicting severity of chest injury using the first eight hours of clinical documents. Our model demonstrates that automated anatomical injury scoring is feasible and may be used for aggregation of data for trauma research and quality programs.