Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors
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The concept of early administration of P2Y12 inhibitor in ST-elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PCI) is widely accepted, but whether prehospital administration results in greater coronary reperfusion remains unclear. Our study aims to analyze the benefit and safety of prehospital P2Y12 inhibitor compared to in-hospital P2Y12 inhibitor administration. ⋯ Prehospital P2Y12 inhibitor compared to in-hospital P2Y12 inhibitor is associated with a significantly higher rate of pre-PCI and post-PCI TIMI flow grade 2-3, a reduced risk of recurrent MI, and no increase in major bleeding in STEMI patients undergoing primary PCI.
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Multicenter Study
Is a positive prehospital FAST associated with severe bleeding? A multicenter retrospective study.
Severe hemorrhage is the leading cause of early preventable death in severe trauma patients. Delayed diagnosis is a poor prognostic factor, and severe hemorrhage prediction is essential. The aim of our study was to investigate if there was an association between the detection of peritoneal or pleural fluid on prehospital sonography for trauma and posttraumatic severe hemorrhage. ⋯ A positive FAST performed in the prehospital setting is associated with severe hemorrhage and all prognostic criteria we studied.
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Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database. Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. ⋯ Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data. Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models.
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Emergency Medical Services (EMS) and law enforcement (LE) frequently work as a team in encounters with individuals experiencing acute behavioral emergencies manifesting with severe agitation and aggression. The optimal management is a rehearsed, coordinated effort by law enforcement and EMS providing the necessary interventions to address behaviors that endanger the patient, the responders, and the public. ⋯ A coordinated and unified response enhances the safety and effective management of potentially serious situations posed by individuals experiencing such acute behavioral emergencies. This paper provides the framework for an approach endorsed by NAEMSP, IACP, and the IAFC.
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Early detection and treatment of sepsis improves chances of survival; however, sepsis is often difficult to diagnose initially. This is especially true in the prehospital setting, where resources are scarce, yet time is of great significance. Early warning scores (EWS) based on vital signs were originally developed to guide medical practitioners in determining the degree of illness of a patient in the in-patient setting. These EWS were adapted for use in the prehospital setting to predict critical illness and sepsis. We performed a scoping review to evaluate the existing evidence for use of validated EWS to identify prehospital sepsis. ⋯ All studies demonstrated inconsistency for the identification of prehospital sepsis. The variety of available EWS and study design heterogeneity suggest it is unlikely that new research can identify a single gold standard score. Based on our findings in this scoping review, we recommend future efforts focus on combining standardized prehospital care with clinical judgment to provide timely interventions for unstable patients where infection is considered a likely etiology, in addition to improving sepsis education for prehospital clinicians. At most, EWS can be used as an adjunct to these efforts, but they should not be relied on alone for prehospital sepsis identification.