Prehospital and Disaster Medicine最新文献

Background: This study assesses the operational challenges and clinical outcomes encountered by a university-based Emergency Medical Team (EMT) during the medical search and rescue (mSAR) response to the February 2023 earthquakes in Kahramanmaraş, Turkey.

Methods: In this observational study, data were retrospectively collected from 42 individuals who received mSAR services post-earthquake. The challenges were categorized as environmental, logistical, or medical, with detailed documentation of rescue times, patient demographics, injury types, and medical interventions.

Results: In this mSAR study, 42 patients from 30 operations were analyzed and divided into environmental (26.2%), logistical (52.4%), and medical (21.4%) challenge groups. Median rescue times were 29 (IQR 28-30), 36.5 (IQR 33.75-77.75), and 30.5 (IQR 29.5-35.5) hours for each group, respectively (P = .002). Age distribution did not significantly differ across groups (P = .067). Hypothermia affected 18.2%, 45.5%, and 66.7% in the respective groups. Extremity injuries were most common in the medical group (88.9%). Intravenous access was highest in the medical group (88.9%), while splinting was more frequent in the medical (55.6%) and logistical (18.2%) groups. Hypothermia was most prevalent in the medical group (66.7%), followed by the logistical group (45.5%). Ambulance transport post-rescue was utilized for a minority in all groups.

Conclusion: The study concludes that logistical challenges, more than environmental or medical challenges, significantly prolong the duration of mSAR operations and exacerbate clinical outcomes like hypothermia, informing future enhancements in disaster response planning and execution.

This Editorial explores organizational travel risk management and advocates for a comprehensive approach to fortify health security for travelers, emphasizing proactive risk management, robust assessments, and strategic planning. Leveraging insights from very important persons (VIP) protocols, organizations can enhance duty of care and ensure personnel safety amidst global travel complexities.

Introduction: Handheld ultrasound (US) devices have become increasingly popular since the early 2000s due to their portability and affordability compared to conventional devices. The Rapid Ultrasonography for Shock and Hypotension (RUSH) protocol, introduced in 2009, has shown promising accuracy rates when performed with handheld devices. However, there are limited data on the accuracy of such examinations performed in a moving ambulance. This study aimed to assess the feasibility and accuracy of the RUSH protocol performed by paramedics using handheld US devices in a moving ambulance.

Objectives: The study aimed to examine the performability of the RUSH protocol with handheld US devices in a moving ambulance and to evaluate the accuracy of diagnostic views obtained within an appropriate time frame.

Methods: A prospective study was conducted with paramedics who underwent theoretical and practical training in the RUSH protocol. The participants performed the protocol using a handheld US device in both stationary and moving ambulances. Various cardiac and abdominal views were obtained and evaluated for accuracy. The duration of the protocol performance was recorded for each participant.

Results: Nine paramedics completed the study, with 18 performances each in both stationary and moving ambulance groups. The accuracy of diagnostic views obtained during the RUSH protocol did not significantly differ between the stationary and moving groups. However, the duration of protocol performance was significantly shorter in the moving group compared to the stationary group.

Conclusion: Paramedics demonstrated the ability to perform the RUSH protocol effectively using handheld US devices in both stationary and moving ambulances following standard theoretical and practical training. The findings suggest that ambulance movement does not significantly affect the accuracy of diagnostic views obtained during the protocol. Further studies with larger sample sizes are warranted to validate these findings and explore the potential benefits of prehospital US in dynamic environments.

Objective: The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS).

Methods: Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains.

Results: This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms.

Conclusion: Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

Introduction: Medical resuscitations in rugged prehospital settings require emergency personnel to perform high-risk procedures in low-resource conditions. Just-in-Time Guidance (JITG) utilizing augmented reality (AR) guidance may be a solution. There is little literature on the utility of AR-mediated JITG tools for facilitating the performance of emergent field care.

Study objective: The objective of this study was to investigate the feasibility and efficacy of a novel AR-mediated JITG tool for emergency field procedures.

Methods: Emergency medical technician-basic (EMT-B) and paramedic cohorts were randomized to either video training (control) or JITG-AR guidance (intervention) groups for performing bag-valve-mask (BVM) ventilation, intraosseous (IO) line placement, and needle-decompression (Needle-d) in a medium-fidelity simulation environment. For the interventional condition, subjects used an AR technology platform to perform the tasks. The primary outcome was participant task performance; the secondary outcomes were participant-reported acceptability. Participant task score, task time, and acceptability ratings were reported descriptively and compared between the control and intervention groups using chi-square analysis for binary variables and unpaired t-testing for continuous variables.

Results: Sixty participants were enrolled (mean age 34.8 years; 72% male). In the EMT-B cohort, there was no difference in average task performance score between the control and JITG groups for the BVM and IO tasks; however, the control group had higher performance scores for the Needle-d task (mean score difference 22%; P = .01). In the paramedic cohort, there was no difference in performance scores between the control and JITG group for the BVM and Needle-d tasks, but the control group had higher task scores for the IO task (mean score difference 23%; P = .01). For all task and participant types, the control group performed tasks more quickly than in the JITG group. There was no difference in participant usability or usefulness ratings between the JITG or control conditions for any of the tasks, although paramedics reported they were less likely to use the JITG equipment again (mean difference 1.96 rating points; P = .02).

Conclusions: This study demonstrated preliminary evidence that AR-mediated guidance for emergency medical procedures is feasible and acceptable. These observations, coupled with AR's promise for real-time interaction and on-going technological advancements, suggest the potential for this modality in training and practice that justifies future investigation.