Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors
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Multicenter Study
Dispatch Categories as Indicators of Out-of-Hospital Time Critical Interventions and Associated Emergency Department Outcomes.
Emergency medical services (EMS) systems increasingly grapple with rising call volumes and workforce shortages, forcing systems to decide which responses may be delayed. Limited research has linked dispatch codes, on-scene findings, and emergency department (ED) outcomes. This study evaluated the association between dispatch categorizations and time-critical EMS responses defined by prehospital interventions and ED outcomes. Secondarily, we proposed a framework for identifying dispatch categorizations that are safe or unsafe to hold in queue. ⋯ In general, Determinant levels aligned with time-critical responses; however, a notable minority of lower acuity Determinant level Protocols met criteria for unsafe to hold. This suggests a more nuanced approach to dispatch prioritization, considering both Protocol and Determinant level factors.
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Out-of-hospital cardiac arrest (OHCA) is a major health problem and one of the leading causes of death in adults older than 40. Multiple prior studies have demonstrated survival disparities based on race/ethnicity, but most of these focus on a single racial/ethnic group. This study evaluated OHCA variables and outcomes among on 5 racial/ethnic groups. ⋯ The Black, Asian, Hispanic, and Pacific Islander groups were less likely to survive to hospital discharge from OHCA when compared with the White reference group. No variables were associated with decreased survival across any of these 4 groups.
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Case Reports
Acute non-ST segment elevation myocardial infarction following intravenous injection of sublingual Suboxone.
Non-ST segment elevation myocardial infarction (NSTEMI) is a relatively unknown complication of injecting sublingual Suboxone (buprenorphine/naloxone). Buprenorphine/naloxone should be taken as a sublingual tablet or a buccal film and not injected, so its effects from this mode of administration are not well known. While the differential diagnosis for chest pain is very broad, many practitioners do not associate chest pain with the use of buprenorphine/naloxone. We recommend considering serial electrocardiograms (ECGs) and high-sensitivity troponins for a patient who presents with chest pain after buprenorphine/naloxone use.
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Evidence suggests that Extracorporeal Cardiopulmonary Resuscitation (ECPR) can improve survival rates for nontraumatic out-of-hospital cardiac arrest (OHCA). However, when ECPR is indicated over 50% of potential candidates are unable to qualify in the current hospital-based system due to geographic limitations. This study employs a Geographic Information System (GIS) model to estimate the number of ECPR eligible patients within the United States in the current hospital-based system, a prehospital ECPR ground-based system, and a prehospital ECPR Helicopter Emergency Medical Services (HEMS)-based system. ⋯ The study demonstrates a two-fold increase in ECPR eligibility for a prehospital ECPR ground-based system and a four-fold increase for a prehospital ECPR HEMS-based system compared to the current hospital-based ECPR system. This novel GIS model can inform future ECPR implementation strategies, optimizing systems of care.
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Observational Study
Development of a Computable Phenotype for Prehospital Pediatric Asthma Encounters.
Asthma exacerbations are a common cause of pediatric Emergency Medical Services (EMS) encounters. Accordingly, prehospital management of pediatric asthma exacerbations has been designated an EMS research priority. However, accurate identification of pediatric asthma exacerbations from the prehospital record is nuanced and difficult due to the heterogeneity of asthma symptoms, especially in children. Therefore, this study's objective was to develop a prehospital-specific pediatric asthma computable phenotype (CP) that could accurately identify prehospital encounters for pediatric asthma exacerbations. ⋯ We modified existing and developed new pediatric asthma CPs to retrospectively identify prehospital pediatric asthma exacerbation encounters. We found that machine learning-based models greatly outperformed rule-based models. Given the high performance of the machine-learning models, the development and application of machine learning-based CPs for other conditions and diseases could help accelerate EMS research and ultimately enhance clinical care by accurately identifying patients with conditions of interest.