Emergency medicine Australasia : EMA
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Emerg Med Australas · Jun 2020
Emergency presentation of new onset versus recurrent undiagnosed seizures: A retrospective review.
To identify clinical factors that may assist emergency physicians to delineate between patients with new onset seizures (NOS) versus patients with recurrent undiagnosed seizures (RUS) among those presenting with apparent 'first seizures' to EDs. In addition, to provide a summary of current evidence-based guidelines regarding the workup of seizure presentations to ED. ⋯ Emergency physicians should be wary of patients presenting with non-motor 'first seizures': they are more likely to have experienced prior seizures (the 'recurrent untreated seizure' group), and thus meet epilepsy diagnostic criteria. Almost half of those with actual NOS may also meet epilepsy criteria, largely driven by abnormal neuroimaging. Distinguishing RUS from NOS patients in the ED allows accurate prognostication and timely initiation of appropriate therapy.
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Emerg Med Australas · Jun 2020
ReviewReview article: A primer for clinical researchers in the emergency department: Part XI. Inertia before investigation: Pre-test probability in emergency medicine.
In this series, we address research topics in emergency medicine. Rational clinical decision making is based on knowledge of the disease prevalence, clinical assessment features and test characteristics such as sensitivity and specificity. The concept of pre-test probability is important as it will allow the clinician and patient decide together if a 'test threshold' or 'treatment threshold' has been reached, or if further investigations are required to make such a decision. This research primer uses three case scenarios to explore these concepts.
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Emerg Med Australas · Jun 2020
Using data mining to predict emergency department length of stay greater than 4 hours: Derivation and single-site validation of a decision tree algorithm.
Health services have an imperative to reduce prolonged patient length of stay (LOS) in ED. Our objective is to develop and validate an accurate prediction model for patient LOS in ED greater than 4 hours using a data mining technique. ⋯ This model performed very well in predicting ED LOS >4 hours for each individual patient and demonstrated a number of clinically relevant patterns. Identifying patterns that influence ED LOS is important for health managers in order to develop and implement interventions targeted at those clinical scenarios. Future work should look at the utility of displaying individual patient risk of ED LOS >4 hours using this model in real-time at the point-of-care.