Articles: emergency-medical-services.
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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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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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Vital signs are important factors in assessing injury severity and guiding trauma resuscitation, especially among severely injured patients. Despite this, physiological data are frequently missing from trauma registries. This study aimed to evaluate the extent of missing prehospital data in a hospital-based trauma registry and to assess the associations between prehospital physiological data completeness and indicators of injury severity. ⋯ In this single center trauma registry, key prehospital variables were frequently missing, particularly among more severely injured patients. Patients with missing data had higher mortality, more severe injury characteristics and received more life-saving interventions in the trauma bay, suggesting an injury severity bias in prehospital vital sign missingness. To ensure the validity of research based on trauma registry data, patterns of missingness must be carefully considered to ensure missing data is appropriately addressed.
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Background: Children have differing utilization of emergency medical services (EMS) by socioeconomic status. We evaluated differences in prehospital care among children by the Child Opportunity Index (COI), the agreement between a child's COI at the scene and at home, and in-hospital outcomes for children by COI. Methods: We performed a retrospective study of pediatric (<18 years) scene encounters from approximately 2,000 United States EMS agencies from the 2021-2022 ESO Data Collaborative. ⋯ Conclusion: Patterns of EMS utilization among children with prehospital emergencies differ by COI. Some measures, such as for in-hospital mortality, occurred more frequently among children transported from Very Low COI areas, whereas others, such as admission, occurred more frequently among children from Very High COI areas. These findings have implications in EMS planning and in alternative out-of-hospital care models, including in regional placement of ambulance stations.
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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.