Annals of emergency medicine
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Multicenter Study
Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.
This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models. ⋯ Machine learning models were developed to accurately make predictions regarding the probability of inpatient or ICU admission throughout the entire duration of a patient's encounter in ED and not just at the time of triage. These models remained accurate for a patient cohort beyond the time period of the initial training data and were integrated to run on live electronic health record data, with similar performance.
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Multicenter Study
Diagnosed and Undiagnosed COVID-19 in US Emergency Department Health Care Personnel: A Cross-sectional Analysis.
We determine the percentage of diagnosed and undiagnosed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among a sample of US emergency department (ED) health care personnel before July 2020. ⋯ In late spring and early summer 2020, the estimated prevalence of severe acute respiratory syndrome coronavirus 2 infection was 4.6%, and greater than one third of infections were undiagnosed. Undiagnosed SARS-CoV-2 infection may pose substantial risk for transmission to other staff and patients.
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Multicenter Study Observational Study
Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study.
To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. ⋯ Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.
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Multicenter Study
Adverse Events Among Emergency Department Patients With Cardiovascular Conditions: A Multicenter Study.
We aim to determine incidence and type of adverse events (adverse outcomes related to emergency care) among emergency department (ED) patients discharged with recent-onset atrial fibrillation, acute heart failure, and syncope. ⋯ Among adverse events after ED discharge for patients with these 3 sentinel cardiovascular diagnoses, we identified quality improvement opportunities such as strengthening dual diagnosis detection and evidence-based clinical practice guideline adherence.
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Randomized Controlled Trial Multicenter Study
The Use of Tranexamic Acid to Reduce the Need for Nasal Packing in Epistaxis (NoPAC): Randomized Controlled Trial.
Epistaxis is a common emergency department (ED) presentation and, if simple first aid measures fail, can lead to a need for anterior nasal packing. Tranexamic acid is an agent that contributes to blood clot stability. The aim of this study is to investigate the effectiveness of topical intranasal tranexamic acid in adult patients presenting to the ED with persistent epistaxis, and whether it reduces the need for anterior nasal packing. ⋯ In patients presenting to an ED with atraumatic epistaxis that is uncontrolled with simple first aid measures, topical tranexamic acid applied in the bleeding nostril on a cotton wool dental roll is no more effective than placebo at controlling bleeding and reducing the need for anterior nasal packing.