Articles: emergency-department.
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Scand J Trauma Resus · Mar 2023
ReviewAirway registries in primarily adult, emergent endotracheal intubation: a scoping review.
Emergency Department (ED) airway registries are formalized methods to collect and document airway practices and outcomes. Airway registries have become increasingly common in EDs globally; yet there is no consensus of airway registry methodology or intended utility. This review builds on previous literature and aims to provide a thorough description of international ED airway registries and discuss how airway registry data is utilized. ⋯ Airway registries are used as a crucial tool to monitor and improve intubation performance and patient care. ED airway registries inform and document the efficacy of quality improvement initiatives to improve intubation performance in EDs globally. Standardized definitions of first-pass success and peri-intubation adverse events, such as hypotension and hypoxia, may allow for airway management performance to be compared on a more equivalent basis and allow for the development of more reliable international benchmarks for first-pass success and rates of adverse events in the future.
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Background and Objectives: Opioid use in Korea is lower than in other developed countries. However, recent studies have reported an increase in opioid prescriptions and the number of chronic opioid users. The current status of adverse events (AEs) associated with opioid analgesics in Korea is unclear. ⋯ Chronic NIOA use was associated with all-cause and opioid-related ED visits (aOR = 1.32, 95% CI = 1.23-1.40; aOR = 1.56, 95% CI = 1.39-1.76, respectively). Conclusion: This study found that 13% of non-cancer patients visited the ED within six months of NIOA initiation. In addition, the NIOA use pattern was significantly associated with all-cause and opioid-related ED visits.
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Multicenter Study Observational Study
Sepsis in Brazilian emergency departments: a prospective multicenter observational study.
We aimed to assess the prevalence, patient allocation adequacy, and mortality of adults with sepsis in Brazilian emergency departments (ED) in a point-prevalence 3-day investigation of patients with sepsis who presented to the ED and those who remained there due to inadequate allocation. Allocation was considered adequate if the patient was transferred to the intensive care unit (ICU), ward, or remained in the ED without ICU admission requests. Prevalence was estimated using the total ED visit number. ⋯ Allocation within 24 h was adequate in only 52.8% of patients (public hospitals: 42.4% (81/190) vs. private institutions: 67.4% (89/132, p < 0.001) with 39.2% (74/189) of public hospital patients remaining in the ED until discharge, of whom 55.4% (41/74) died. Sepsis exerts high burden and mortality in Brazilian EDs with frequent inadequate allocation. Modifiable factors, such as resources and quality of care, are associated with reduced mortality.
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To derive and initially validate a brief bedside clinical decision support tool that identifies emergency department (ED) patients at high risk of substantial, persistent posttraumatic stress symptoms after a motor vehicle collision. ⋯ This simple 8-question tool demonstrates promise to risk-stratify individuals with substantial posttraumatic stress symptoms who are discharged to home after a motor vehicle collision. Both external validation of this instrument, and work to further develop more accurate tools, are needed. Such tools might benefit public health by enabling the conduct of preventive intervention trials and assisting the growing number of EDs that provide services to trauma survivors aimed at promoting psychological recovery.
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Observational Study
Using Machine Learning to Predict Hospital Disposition With Geriatric Emergency Department Innovation Intervention.
The Geriatric Emergency Department Innovations (GEDI) program is a nurse-based geriatric assessment and care coordination program that reduces preventable admissions for older adults. Unfortunately, only 5% of older adults receive GEDI care because of resource limitations. The objective of this study was to predict the likelihood of hospitalization accurately and consistently with and without GEDI care using machine learning models to better target patients for the GEDI program. ⋯ Our machine learning models could predict who will likely be discharged with GEDI assessment with good accuracy and thus select a cohort appropriate for GEDI care. In addition, future implementation through integration into the electronic health record may assist in selecting patients to be prioritized for GEDI care.