Prehospital Emergency Care最新文献

Objectives: ST-elevation myocardial infarction (STEMI) is an Acute Myocardial Infarction (AMI) with the greatest risk of death and disability. Getting diagnosed patients rapid definitive treatment at the correct facility is crucial in improving their outcome. Using a Question-and-Answer algorithm (Mobile Priority Dispatch System (MPDS^®)), trained Emergency Medical Dispatchers (EMDs) can help identifying STEMI at the time of dispatch. This can assist Emergency Medical Services (EMS) pre-planning transport to potential STEMI-receiving hospitals. The study aimed to determine whether hospital-confirmed STEMI cases transported by ambulance are associated with certain dispatch determinant codes and identify the treatments performed.

Methods: The retrospective study analyzed deidentified dispatch and hospital data of STEMI patients who were transported by Qatar's Ambulance Service between January 2018 and May 2021. Data analysis compared patient demographics with dispatch measures, considering chief complaint and determinant codes, and Percutaneous Coronary Intervention (PCI) treatment received.

Results: A total of 3,724 STEMI cases with MPDS^® dispatch codes were retrieved. After excluding patient transfer and pandemic-related cases, a final sample of 2,607 cases was analyzed. Most STEMI patients (86.0%) were classified as high priority levels at dispatch, had chest pain as chief complaint (62.9%), and were male (90.8%). Approximately, 99.0% of the STEMI patients received PCI treatment. Distributions of STEMI cases and PCI treatment did not significantly differ by patient demographics and dispatch measures.

Conclusions: Qatar's STEMI patients are more likely to be male and to receive adequate acute care irrespective of any demographic factor and despite potential language issues. This study highlights that the chief complaint may be described or interpreted differently when the questioning language is not their mother tongue, or when there is a language barrier between the caller, call taker, or when using the MPDS^® protocols language or when self-translating questions instantly in another language. Therefore, EMDs should be made aware of the language differences and be encouraged to further clarify the chief complaint when appropriate. There may be a need for potential refinements of the MPDS^® questioning algorithm and EMD training with AMI symptoms reinforcement. This could help improve their early identification of STEMI cases with non-classic chest pain symptoms.

Objectives: The National Emergency Medical Services Information System (NEMSIS) provides a robust set of data to evaluate prehospital care. However, a major limitation is that the vast majority of the records lack a definitive outcome. This study aimed to evaluate the performance of a recently proposed method ("MLB" method) to impute missing end-of-EMS-event outcomes ("dead" or "alive") for patient care reports in the NEMSIS public research dataset.

Methods: This study reproduced the recently published method for patient outcome imputation in the NEMSIS database and replicated the results for years 2017 through 2022 (n = 686,075). We performed statistical analyses leveraging an array of established performance metrics for binary classification from the machine learning literature. Evaluation metrics included overall accuracy, true positive rate, true negative rate, balanced accuracy, precision, F1 score, Cohen's Kappa coefficient, Matthews' coefficient, Hamming loss, the Jaccard similarity score, and the receiver operating characteristic/area under the curve.

Results: Extended metrics show consistently good imputation performance from year-to-year but reveal weakness in accurately indicating the minority class: e.g., after adjustments for conflicting labels, "dead" prediction accuracy is 77.7% for 2018 and 61.8% over the six-year NEMSIS sub-sample, even though overall accuracy is 98.8%. Slight over-fitting is also present.

Conclusions: This study found that the recently published MLB method produced reasonably good "dead" or "alive" indicators. We recommend reporting of True Positive Rate ("dead" prediction accuracy) and True Negative Rate ("alive" prediction accuracy) when applying the imputation method for analyses of NEMSIS data. More attention by EMS clinicians to complete documentation of target NEMSIS elements can further improve the method's performance.

Objectives: Large vessel occlusion (LVO) strokes may be eligible for treatment with intravenous thrombolysis (IVT) and endovascular therapy (EVT). Patients selected for treatment have better neurologic outcomes with EVT, and delays in this therapy lead to worse outcomes. However, EVT is offered at a limited number of hospitals, referred to as endovascular stroke centers (ESC). This poses a difficult decision for EMS: to take potential stroke patients to the closest primary stroke center (PSC) or longer transport time to a more distant ESC. We hypothesized that patients with LVO stroke undergoing EVT transported directly to an ESC would have more favorable outcomes as measured by the modified Rankin scale (mRS) at 90 days, compared to transport to a PSC followed by transfer to an ESC.

Methods: The OPUS-REACH consortium examined transportation patterns and outcomes in patients with LVO stroke who received endovascular treatment. This cohort includes 2400 patients with LVO stroke throughout eight endovascular centers in the Northeast U.S. from 2015 to 2020. All patients enrolled in the OPUS-REACH database were eligible for inclusion. Patients were excluded if they were missing the pickup address, had an in-hospital stroke, or arrived via mobile stroke unit. The remaining patients were separated into two groups: the bypass group, with transportation by EMS to an ESC by bypassing PSC, and the non-bypass group, with initial transport to PSC and interfacility transport to an ESC. The primary outcome was the modified Rankin scale (mRS) at 90 days, where 0-2 was defined as "good".

Results: The primary outcome did not reach significance with 40% of the bypass group as compared with the 33.1% of the non-bypass group having a "good" outcome. However, the bypass group underwent shorter times from last-known-well to both thrombolysis (120.9 vs 153.3 min, p < 0.001) and thrombectomy (356.1 vs 454.8 min, p = 0.001).

Conclusions: In patients with LVO stroke who undergo thrombectomy, EMS transport directly to an ESC results in shorter time thrombectomy, although we did not observe a difference in 90-day functional outcomes. Additionally, bypass to reach a more capable endovascular stroke center does not delay administration of IVT from time of LKW.

Objectives: The use of machine learning to identify patient 'clusters' using post-return of spontaneous circulation (ROSC) vital signs may facilitate the identification of patient subgroups at high risk of rearrest and mortality. Our objective was to use k-means clustering to identify post-ROSC vital sign clusters and determine whether these clusters were associated with rearrest and mortality.

Methods: The ESO Data Collaborative 2018-2022 datasets were used for this study. We included adult, non-traumatic OHCA patients with >2 post-ROSC vital sign sets. Patients were excluded if they had an EMS-witnessed OHCA or were encountered during an interfacility transfer. Unsupervised (k-means) clustering was performed using minimum, maximum, and delta (last minus first) systolic blood pressure (BP), heart rate, SpO₂, shock index, and pulse pressure. The assessed outcomes were mortality and rearrest. To explore the association between rearrest, mortality, and cluster, multivariable logistic regression modeling was used.

Results: Within our cohort of 12,320 patients, five clusters were identified. Patients in cluster 1 were hypertensive, patients in cluster 2 were normotensive, patients in cluster 3 were hypotensive and tachycardic (n = 2164; 17.6%), patients in cluster 4 were hypoxemic and exhibited increasing systolic BP, and patients in cluster 5 were severely hypoxemic and exhibited a declining systolic BP. The overall proportion of patients who experienced mortality stratified by cluster was 63.4% (c1), 68.1% (c2), 78.8% (c3), 84.8% (c4), and 86.6% (c5). In comparison to the cluster with the lowest mortality (c1), each other cluster was associated with greater odds of mortality and rearrest.

Conclusions: Unsupervised k-means clustering yielded 5 post-ROSC vital sign clusters that were associated with rearrest and mortality.

Objectives: Airway management is a fundamental skill that Emergency Medical Services (EMS) clinicians must be prepared to perform on patients of any age. We performed one of the first epidemiological studies of out-of-hospital pediatric airway management utilizing the ESO data set.

Methods: We used the 2019 ESO Data Collaborative public release research data set. We performed a descriptive analysis of all patients <18 years receiving at least one of the following airway management interventions: nasopharyngeal airway, oropharyngeal airway, noninvasive positive pressure ventilation (NIPPV), airway suctioning, bag-valve-mask ventilation (BVM), tracheal intubation (TI), supraglottic airway (SGA) or surgical airway placement. We determined the success rates for BVM, TI and SGA.

Results: Among 7,422,710 911 EMS activations, there were 346,912 pediatric encounters that resulted in patient care. Airway management occurred in 27,071 encounters (7,803 per 100,000 pediatric EMS patient care events). Use of BVM, intubation or supraglottic airway insertion occurred in 3,496 encounters (1,007 per 100,000 pediatric EMS patient care events). Ventilation with BVM occurred in 2,226 encounters (642 per 100,000 pediatric EMS patient care events), TI in 935 pediatric EMS patient care encounters (270 per 100,000 patient care encounters), and supraglottic airway insertion in 335 patient encounters (97 per 100,000 patient care encounters). Overall TI success was 71.4%, rapid sequence intubation success was 86.3%, and SGA success was 87.2%. Overall TI first pass success rate was 63.1%.

Conclusions: In the ESO cohort, advanced airway management of children occurred in only 5.9 in 10,000 911 emergency encounters. Overall and first pass success rates for TI were low. These data provide contemporary perspectives of pediatric prehospital airway management in the United States.

Objective: Paramedics work in a complex, unpredictable environment, subject to many external stressors including critically unwell patients, dangerous driving conditions, and prolonged shift work. Paramedic fatigue from these and other occupational demands is well documented. Ambulance services attempt to safeguard paramedics from fatigue using internal policies or procedures - a type of Fatigue Risk Management Systems (FRMSs). This study reviews ambulance service fatigue frameworks to understand the current situation in fatigue management in paramedicine, and to identify fatigue monitoring tools, strategies, and other components of these frameworks that are designed to protect personnel.

Methods: This study involved a qualitative document thematic content analysis. All eleven statutory ambulance services across Australia, New Zealand, and Papua New Guinea, represented by the Council of Ambulance Authorities, were contacted and invited to participate. Fatigue frameworks were collated and entered into NVivo where data extraction occurred through three a priori areas (fatigue, fatigue mitigation tools & fatigue management).

Results: Nine of the eleven ambulance services provided fatigue documentation, with one declining to participate, and one did not respond to invitations. Through thematic analysis and abstraction, seven themes were identified: fatigue definition, consequences of fatigue, sources of fatigue, signs and symptoms of fatigue, fatigue-related incidents, fatigue monitoring tools, and fatigue mitigation. There was also poor alignment between provided frameworks and established FRMSs components.

Conclusion: Our findings provide an initial insight into existing ambulance service fatigue frameworks across Australia, New Zealand, and Papua New Guinea. The many inconsistencies in frameworks between ambulance services highlight an opportunity to develop a more consistent, collaborative approach that follows evidence-based FRMSs guidelines.

Objectives: After identifying chest compression fraction (CCF) as a key area for improvement, our Emergency Medical Services (EMS) agency aimed to improve our baseline monthly median CCF from 81.5% to 90% or more in paramedic-attended medical cardiac arrests by December 2023. The CCF is a process measure that, if improved, has been shown to increase likelihood of survival from cardiac arrest. Working as a hospital EMS agency within a large urban 9-1-1 system, our interventions focused on paramedics once they arrived on scene.

Methods: This project used repeated Plan-Do-Study-Act (PDSA) cycles with brainstorming sessions, focus groups, and data review to achieve improvement. Interventions included standardized clinician feedback forms, increased follow-up for patients with ongoing resuscitation, a designated CPR team leader during resuscitations, and a pre-charged defibrillator prior to rhythm checks. These interventions were evaluated by tabulating weekly and monthly median CCF performance, seeking participant feedback, and reviewing control charts. These results were reported according to the Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0).

Results: Our control chart analysis revealed special cause variation and an increase in average CCF to 89.0%. This improvement was achieved through successful implementation of process changes using PDSA cycles. Our most effective and popular intervention was our clinician feedback forms. Additionally, re-unifying patients and their successful resuscitation teams, participating in resuscitation academy events, and pre-charging the defibrillator to minimize CPR pauses collectively resulted in systemic improvement in resuscitation performance.

Conclusions: The findings illustrate that targeted education, increased clinician feedback, patient-team reunification, and high-performance resuscitation strategies produce measurable improvement in CCF.

Objectives: Emergency medical services (EMS) provide health care in situations with limited time and resources. Challenges arise when introducing novel medications, treatments, or technologies or modifying existing practices in these settings. Effective implementation strategies are pivotal for their success. This study aims to identify and categorize potential facilitators and barriers in the implementation of prehospital EMS through a review of relevant research articles.

Methods: We searched PubMed and EMbase to identify studies published before December 2023, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for our search strategy and scoping review. We included original articles written in English that report on the factors that influence the implementation in prehospital settings. We extracted and categorized the factors into different themes.

Results: Out of the 371 retrieved papers, we selected 19 (5%) for inclusion in this review. We extracted 46 influencing factors from the selected articles and categorized them into ten themes: (1) Outer system, (2) Inner system, (3) Practitioner characteristics, (4) Resources, (5) Communication and collaboration, (6) Patient factors, (7) Intervention characteristics, (8) De-implementation of prior practices, (9) Logistical issues, and (10) Quality improvement.

Conclusions: This study examined the literature on EMS implementation factors and proposed a 10-theme EMS model framework. Key factors include training/education, equipment/tools, communication with hospitals, and practitioners' attitudes.

Objectives: The prehospital prediction of the radiographic diagnosis of traumatic brain injury (TBI) in hemorrhagic shock patients has the potential to promote early therapeutic interventions. However, the identification of TBI is often challenging and prehospital tools remain limited. While the Glasgow Coma Scale (GCS) score is frequently used to assess the extent of impaired consciousness after injury, the utility of the GCS scores in the early prehospital phase of care to predict TBI in patients with severe injury and concomitant shock is poorly understood.

Methods: We performed a post-hoc, secondary analysis utilizing data derived from three randomized prehospital clinical trials: the Prehospital Air Medical Plasma trial (PAMPER), the Study of Tranexamic Acid During Air Medical and Ground Prehospital Transport trial (STAAMP), and the Pragmatic Prehospital Type O Whole Blood Early Resuscitation (PPOWER) trial. Patients were dichotomized into two cohorts based on the presence of TBI and then further stratified into three groups based on prehospital GCS score: GCS 3, GCS 4-12, and GCS 13-15. The association between prehospital GCS score and clinical documentation of TBI was assessed.

Results: A total of 1,490 enrolled patients were included in this analysis. The percentage of patients with documented TBI in those with a GCS 3 was 59.5, 42.4% in those with a GCS 4-12, and 11.8% in those with a GCS 13-15. The positive predictive value (PPV) of the prehospital GCS score for the diagnosis of TBI is low, with a GCS of 3 having only a 60% PPV. Hypotension and prehospital intubation are independent predictors of a low prehospital GCS. Decreasing prehospital GCS is strongly associated with higher incidence or mortality over time, irrespective of the diagnosis of TBI.

Conclusions: The ability to accurately predict the presence of TBI in the prehospital phase of care is essential. The utility of the GCS scores in the early prehospital phase of care to predict TBI in patients with severe injury and concomitant shock is limited. The use of novel scoring systems and improved technology are needed to promote the accurate early diagnosis of TBI.

Objective: Emergency Medical Services (EMS) clinicians desire performance feedback (PF) and patient outcome follow-up (POF). Within our agency, both a peer-review and feedback/outcome (PF/POF) process exist. Our objective was to determine whether receiving feedback and outcome data improved future clinical care amongst EMS, based upon peer-review scores.

Methods: This retrospective cohort study took place between 1/1/2020 and 6/7/2023 within an EMS agency site with 22,000 average annual 9-1-1 calls. Requests for PF/POF were submitted on an individual basis beginning June 2020 and completed by a dedicated EMS nurse, EMS physician, or emergency medicine (EM) resident. Peer-review of select high-acuity cases were scored by two Quality Assurance (QA) specialists within the categories of assessment, treatment, disposition/outcome and process/administrative guidelines. Association between overall peer-review score and number of PF/POF requests at time of assessment was evaluated by linear regression.

Results: A total of 378 PF/POF requests were received, with the most common patient complaints being cardiac (n = 105; 27.8%, including 49 (13.0%) out of hospital cardiac arrests), altered mental status/neurologic (n = 103; 27.2%), trauma (n = 61; 16.1%, including 2 (0.5%) traumatic arrests); and respiratory distress (n = 47; 12.4%). A total of 378 runs meeting QA criteria were peer-reviewed post-PF/POF process implementation, including 337 (89.2%) cardiac/respiratory arrests, 27 (7.1%) with difficult airway management, and 14 (3.7%) major trauma/traumatic arrests. The number of prior PF/POF requests made by the team leader was associated with higher overall peer-review scores. Team leaders with >5 prior PF/POF requests had a peer-review score 0.39 points greater (95% CI: 0.16 - 0.62, p = 0.001) than those with <5 prior requests. The number of prior PF/POF requests amongst the entire crew was also associated with higher peer-review scores. Crews that collectively had >5 prior PF/POF requests had an increase in peer-review score 0.32 points greater (95% CI: 0.14 - 0.50, p < 0.001) than those with <5 prior requests.

Conclusion: Providing performance feedback and patient outcome follow-up to EMS is associated with improved peer-review scores of clinical performance. Future studies should assess if those that are submitting cases for feedback/outcome are higher performers at baseline or if the process of receiving feedback/outcome improves their performance.