Articles: emergency-department.
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There are only a few models developed for risk-stratifying COVID-19 patients with suspected pneumonia in the emergency department (ED). We aimed to develop and validate a model, the COVID-19 ED pneumonia mortality index (CoV-ED-PMI), for predicting mortality in this population. We retrospectively included adult COVID-19 patients who visited EDs of five study hospitals in Texas and who were diagnosed with suspected pneumonia between March and November 2020. ⋯ The model was validated with good discriminative performance (AUC: 0.83, 95% confidence interval [CI]: 0.79-0.87), which was significantly better than the CURB-65 (AUC: 0.74, 95% CI: 0.69-0.79, p-value: < 0.001). The CoV-ED-PMI had a good predictive performance for 1-month mortality in COVID-19 patients with suspected pneumonia presenting at ED. This free tool is accessible online, and could be useful for clinical decision-making in the ED.
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Observational Study
Nature and extent of selection bias resulting from convenience sampling in the emergency department.
To compare the clinical and demographic variables of patients who present to the ED at different times of the day in order to determine the nature and extent of potential selection bias inherent in convenience sampling METHODS: We undertook a retrospective, observational study of data routinely collected in five EDs in 2019. Adult patients (aged ≥18 years) who presented with abdominal or chest pain, headache or dyspnoea were enrolled. For each patient group, the discharge diagnoses (primary outcome) of patients who presented during the day (08:00-15:59), evening (16:00-23:59), and night (00:00-07:59) were compared. Demographics, triage category and pain score, and initial vital signs were also compared. ⋯ Patients with abdominal or chest pain, headache or dyspnoea differ in a range of clinical and demographic variables depending upon their time of presentation. These differences may potentially introduce selection bias impacting upon the internal validity of a study if convenience sampling of patients is undertaken.
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Emerg Med Australas · Apr 2022
Retrospective identification of latent subgroups of emergency department patients: A machine learning approach.
This research aims to (i) identify latent subgroups of ED presentations in Australian public EDs using a data-driven approach and (ii) compare clinical, socio-demographic and time-related characteristics of ED presentations broadly using the subgroups. ⋯ Clustering Large Applications is effective in finding latent groups in large-scale mixed-type data, as demonstrated in the present study. Six types of ED presentations were identified and described using clinically relevant characteristics. The present study provides evidence for policy makers in Australia to develop alternative ED models of care tailored around the care needs of the differing groups of patients and thereby supports the sustainable delivery of acute healthcare.
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Both traumatic and nontraumatic ocular issues often present to the emergency department. Understanding the epidemiology of ocular presentations to the emergency department not only informs current resource allocation, but also provides opportunities to evaluate the efficacy of prior healthcare access interventions. ⋯ Ophthalmic emergency department visits in the United States between 2010 and 2018 were typically for non-traumatic eye issues. Diagnoses varied greatly by patient demographics, such as age and gender. Understanding these variations is valuable for preparing emergency departments for ocular presentations and providing guidance for future practice.
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Mechanical bull-riding has become a popular form of entertainment in the United States (US) over the last few decades. While mechanical bull-riding may result in injuries, the literature on such injuries is limited. This study characterized mechanical bull-riding injuries treated at US emergency departments (EDs). ⋯ The highest proportion of mechanical bull-riding injuries involved patients age 20-29 years. The majority of injuries involved the patient falling or being thrown from the mechanical bull. The most frequently reported diagnosis among mechanical bull-riding injuries was sprain or strain followed by fracture and contusion or abrasion.