Large-Scale Airmedical Transport from a Peripheral Hospital to Level-1 Trauma Centers after Remote Mass-Casualty Incidents in Israel

2009 ◽  
Vol 24 (6) ◽  
pp. 549-555 ◽  
Author(s):  
Ophir Lavon ◽  
Dan Hershko ◽  
Erez Barenboim
Keyword(s):  
Large Scale ◽  
Medical Personnel ◽  
Mass Casualty ◽  
Mass Casualty Incidents ◽  
Trauma Patients ◽  
Trauma Centers ◽  
Preparedness Plan ◽  
Peripheral Hospital ◽  
Number Of Patients ◽  
Level 1

AbstractIntroduction:Mass-casualty incidents (MCIs) result in the evacuation of many patients to the nearest medical facility. However, an overwhelming number of patients and the type and severity of injuries may demand rapid, mass airmedical transport to more advanced medical centers. This task may be challenging, particularly after a MCI in a neighboring country. The Israeli Air Force Rescue and Airmedical Evacuation Unit (RAEU) is the main executor of airmedical transport in Israel, including MCIs.Problem:The available data on airmedical transport from remote MCIs are limited. The objective of this study was to evaluate the airmedical transport from a rural hospital after two remote MCIs.Methods:The study was retrospective and reviewed descriptive records of airmedical transports.Results:The RAEU was involved in airmedical transports from a peripheral hospital shortly after two remote MCIs that occurred in the Sinai desert near the Egyptian-Israeli border. Nineteen (22.4%) and 25 (100%) of the treated trauma patients from each event were airmedically transported to Level-1 Trauma Centers in Israel within hours of the dispatch. The rapid dispatch and accumulation of medical personnel and equipment was remarkable. The airmedical surge capacity was broad and sufficient. Cooperation with local authorities and a tailored boarding procedure facilitated a quality outcome.Conclusions:The incorporation of a large-scale airmedical transport program with designated multidisciplinary protocols is an essential component to a remote disaster preparedness plan.

Tabletop Application of SALT Triage to 10, 100, and 1000 Pediatric Victims

2020 ◽  
Vol 35 (2) ◽  
pp. 165-169
Author(s):  
Nicholas McGlynn ◽  
Ilene Claudius ◽  
Amy H. Kaji ◽  
Emilia H. Fisher ◽  
Alaa Shaban ◽  
...  
Keyword(s):  
Pediatric Patients ◽  
Pediatric Trauma ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Trauma Patients ◽  
Kappa Test ◽  
Life Saving ◽  
Number Of Patients ◽  
Level 1 ◽  
Level 1 Trauma Center

AbstractIntroduction:The Sort, Access, Life-saving interventions, Treatment and/or Triage (SALT) mass-casualty incident (MCI) algorithm is unique in that it includes two subjective questions during the triage process: “Is the victim likely to survive given the resources?” and “Is the injury minor?”Hypothesis/Problem:Given this subjectivity, it was hypothesized that as casualties increase, the inter-rater reliability (IRR) of the tool would decline, due to an increase in the number of patients triaged as Minor and Expectant.Methods:A pre-collected dataset of pediatric trauma patients age <14 years from a single Level 1 trauma center was used to generate “patients.” Three trained raters triaged each patient using SALT as if they were in each of the following scenarios: 10, 100, and 1,000 victim MCIs. Cohen’s kappa test was used to evaluate IRR between the raters in each of the scenarios.Results:A total of 247 patients were available for triage. The kappas were consistently “poor” to “fair:” 0.37 to 0.59 in the 10-victim scenario; 0.13 to 0.36 in the 100-victim scenario; and 0.05 to 0.36 in the 1,000-victim scenario. There was an increasing percentage of subjects triaged Minor as the number of estimated victims increased: 27.8% increase from 10- to 100-victim scenario and 7.0% increase from 100- to 1,000-victim scenario. Expectant triage categorization of patients remained stable as victim numbers increased.Conclusion:Overall, SALT demonstrated poor IRR in this study of increasing casualty counts while triaging pediatric patients. Increased casualty counts in the scenarios did lead to increased Minor but not Expectant categorizations.

scholarly journals Identifying trauma patients with benefit from direct transportation to Level-1 trauma centers

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Charlie A. Sewalt ◽  
Benjamin Y. Gravesteijn ◽  
Daan Nieboer ◽  
Ewout W. Steyerberg ◽  
Dennis Den Hartog ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Trauma Center ◽  
Trauma Patients ◽  
Trauma Centers ◽  
Absolute Benefit ◽  
Predicted Probability ◽  
Prehospital Triage ◽  
Level 1 ◽  
Level 1 Trauma Center ◽  
Level 2

Abstract Background Prehospital triage protocols typically try to select patients with Injury Severity Score (ISS) above 15 for direct transportation to a Level-1 trauma center. However, ISS does not necessarily discriminate between patients who benefit from immediate care at Level-1 trauma centers. The aim of this study was to assess which patients benefit from direct transportation to Level-1 trauma centers. Methods We used the American National Trauma Data Bank (NTDB), a retrospective observational cohort. All adult patients (ISS > 3) between 2015 and 2016 were included. Patients who were self-presenting or had isolated limb injury were excluded. We used logistic regression to assess the association of direct transportation to Level-1 trauma centers with in-hospital mortality adjusted for clinically relevant confounders. We used this model to define benefit as predicted probability of mortality associated with transportation to a non-Level-1 trauma center minus predicted probability associated with transportation to a Level-1 trauma center. We used a threshold of 1% as absolute benefit. Potential interaction terms with transportation to Level-1 trauma centers were included in a penalized logistic regression model to study which patients benefit. Results We included 388,845 trauma patients from 232 Level-1 centers and 429 Level-2/3 centers. A small beneficial effect was found for direct transportation to Level-1 trauma centers (adjusted Odds Ratio: 0.96, 95% Confidence Interval: 0.92–0.99) which disappeared when comparing Level-1 and 2 versus Level-3 trauma centers. In the risk approach, predicted benefit ranged between 0 and 1%. When allowing for interactions, 7% of the patients (n = 27,753) had more than 1% absolute benefit from direct transportation to Level-1 trauma centers. These patients had higher AIS Head and Thorax scores, lower GCS and lower SBP. A quarter of the patients with ISS > 15 were predicted to benefit from transportation to Level-1 centers (n = 26,522, 22%). Conclusions Benefit of transportation to a Level-1 trauma centers is quite heterogeneous across patients and the difference between Level-1 and Level-2 trauma centers is small. In particular, patients with head injury and signs of shock may benefit from care in a Level-1 trauma center. Future prehospital triage models should incorporate more complete risk profiles.

Using Baseline Data to Address the Lack of Hospital Beds during Mass-Casualty Incidents

2008 ◽  
Vol 23 (4) ◽  
pp. 377-379 ◽  
Author(s):  
Hysham Hadef ◽  
Jean-Claude Bartier ◽  
Herve Delplancq ◽  
Jean-Pierre Dupeyron
Keyword(s):  
Medical Personnel ◽  
Mass Casualty ◽  
Baseline Data ◽  
Mass Casualty Incidents ◽  
Free Access ◽  
Data Systems ◽  
Hospital Beds ◽  
Access Information ◽  
Data Program ◽  
Information Database

AbstractThe management of victims during mass-casualty incidents (MCIs) is improving. In many countries, physicians and paramedics are well-trained to manage these incidents. A problem that has been encountered during MCIs is the lack of adequate numbers of hospital beds to accommodate the injured. In Europe, hospitals are crowded. One solution for the lack of beds is the creation of baseline data systems that could be consulted by medical personnel in all European countries. A MCI never has occurred in northeastern Europe, but such an event remains a possibility. This paper describes how the use of SAGEC 67, a free-access, information database concerning the availability of beds should help the participating countries, initially France, Germany, and Switzerland, respond to a MCI by dispatching each patient to an appropriate hospital and informing their families and physicians using their own language.Baseline data for more than 20 countries, and for hospitals, especially those in Germany, Switzerland, and France, were collected. Information about the number of beds and their availability hour-by-hour was included. In the case of MCIs, the baseline data program is opened and automatically connects to all of the countries. In case of a necessary hospital evacuation, the required beds immediately are occupied in one of these three countries.Questions and conversations among medical staff or family members can be accomplished between hospitals through computer, secured-line chatting that automatically translates into appropriate language.During the patient evacuation phase of a MCI, respondents acknowledged that a combination of local, state, and private resources and international cooperation eventually would be needed to meet the demand. Patient evacuation is optimized through the use of SAGEC 67, a free baseline database.

Are there Field Triage Criteria that Can Predict Low-Yield Air Medical Transports?

2019 ◽  
Vol 34 (6) ◽  
pp. 596-603
Author(s):  
Hiroko Miyagi ◽  
David C. Evans ◽  
Howard A. Werman
Keyword(s):  
Trauma Center ◽  
Early Discharge ◽  
Hospital Death ◽  
Trauma Patients ◽  
Trauma Centers ◽  
Predictive Values ◽  
Field Triage ◽  
Number Of Patients ◽  
Medical Transport ◽  
The Impact

AbstractIntroduction:Air medical transport of trauma patients from the scene of injury plays a critical role in the delivery of severely injured patients to trauma centers. Over-triage of patients to trauma centers reduces the system efficiency and jeopardizes safety of air medical crews.Hypothesis:The objective of this study was to determine which triage factors utilized by Emergency Medical Services (EMS) providers are strong predictors of early discharge for trauma patients transported by helicopter to a trauma center.Methods:A retrospective chart review over a two-year period was performed for trauma patients flown from the injury site into a Level I trauma center by an air medical transport program. Demographic and clinical data were collected on each patient. Prehospital factors such as Glasgow Coma Score (GCS), Revised Trauma Score (RTS), intubation status, mechanism of injury, anatomic injuries, physiologic parameters, and any combinations of these factors were investigated to determine which triage criteria accurately predicted early discharge. Hospital factors such as Injury Severity Score (ISS), length-of-stay (LOS), survival, and emergency department disposition were also collected. Early discharge was defined as a hospital stay of less than 24 hours in a patient who survives their injuries. A more stringent definition of appropriate triage was defined as a patient with in-hospital death, an ISS >15, those taken to the operating room (OR) or intensive care unit (ICU), or those receiving blood products. Those patients who failed to meet these criteria were also used to determine over-triage rates.Results:An overall early discharge rate of 35% was found among the study population. Furthermore, when the more stringent definition was applied, over-triage rates were as high as 85%. Positive predictive values indicated that patients who met at least one anatomic and physiologic criteria were appropriately transported by helicopter as 94% of these patients had stays longer than 24 hours. No other criteria or combination of criteria had a high predictive value for early discharge.Conclusions:No individual triage criteria or combination of criteria examined demonstrated the ability to uniformly predict an early discharge. Although helicopter transport and subsequent hospital care is costly and resource consuming, it appears that a significant number of patients will be discharged within 24 hours of their transport to a trauma center. Future studies must determine the impact of eliminating “low-yield” triage criteria on under-triage of scene trauma patients.

Transfer of Trauma Patients to Level 1 and 2 Trauma Centers: Mortality Outcomes and Race – Insurance Disparities

2014 ◽  
Vol 186 (2) ◽  
pp. 511-512
Author(s):  
B. Adesibikan ◽  
S.N. Zafar ◽  
A. Obirieze ◽  
W.R. Greene ◽  
E.E. Cornwell ◽  
...  
Keyword(s):  
Trauma Patients ◽  
Trauma Centers ◽  
Level 1

scholarly journals MP26: Development and evaluation of a novel emergency physician fan-out mechanism at an urban centre for use in mass casualty incidents

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S51
Author(s):  
J. Melegrito ◽  
B. Granberg ◽  
K. Hanrahan
Keyword(s):  
Emergency Department ◽  
Emergency Physician ◽  
Large Scale ◽  
Response Rate ◽  
Text Message ◽  
Mass Casualty ◽  
Mass Casualty Incidents ◽  
Process Mapping ◽  
Emergency Physicians ◽  
Physician Response

Background: Understaffing in mass casualty incidents limits flow in the overwhelmed emergency department, which is further compounded by inefficient use of those same human resources. Process mapping analysis of a “Code Orange” exercise at a tertiary academic hospital exposed the failures of telephone-based emergency physician fan-out protocols to address these issues. As such, a quality improvement and patient safety initiative was undertaken to design, implement, and evaluate a new mass casualty incident fan-out mechanism. Aim Statement: By February 2019, emergency physician fan-out will be accomplished within 1 hour of Code Orange declaration, with a response rate greater than 20%. Measures &amp; Design: Process mapping of a Code Orange simulation highlighted telephone fan-out to be ineffective in mobilizing emergency physicians to provide care in mass casualty incidents: available staff were pulled from their usual duties to help unit clerks unsuccessfully reach off-duty physicians by telephone for hours. Stakeholders subsequently identified automation and computerization as a compelling change idea. A de-novo automated bidirectional text-messaging system was thus developed. Early trials were analyzed for process measures including fan-out speed, unit clerk involvement, and physician response rate, with further large-scale tests planned for early 2019. Evaluation/Results: Only 50% of telephone fan-out was completed after a 2-hour exercise despite 3 staff supplementing the 2 on-shift unit clerks, with a 4% physician response rate. In contrast, data from initial trials of the automated system suggest that full fan-out can be performed within 1 hour of Code Orange declaration and require only 1 unit clerk, with text-messages projected to yield higher physician response rates than telephone calls. Early findings have thus far affirmed stakeholder sentiments that automating fan-out can improve speed, unit clerk efficiency, and physician response rate. Discussion/Impact: Automated text-message systems can expedite fan-out protocol in mass casualty incidents, relieve allied health staff strain, and more reliably recruit emergency physicians. Large-scale trials of the novel system are therefore planned for early 2019, with future expansion of the protocol to other medical personnel under consideration. Thus, automated text-message systems can be implemented in urban centres to improve fan-out efficiency and aid overall emergency department flow in mass casualty incidents.

Field Triage and Patient Maldistribution in a Mass-Casualty Incident

2007 ◽  
Vol 22 (3) ◽  
pp. 224-229 ◽  
Author(s):  
Richard M. Zoraster ◽  
Cathy Chidester ◽  
William Koenig
Keyword(s):  
Los Angeles ◽  
Prehospital Care ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Trauma Centers ◽  
Community Hospitals ◽  
Center Staff ◽  
Field Triage ◽  
Number Of Patients ◽  
To Receive

AbstractIntroduction:Management of mass-casualty incidents should optimize outcomes by appropriate prehospital care, and patient triage to the most capably facilities. The number of patients, the nature of injuries, transportation needs, distances, and hospital capabilities and availabilities are all factors to be considered. Patient maldistributions such as overwhelming individual facilities, or transport to facilities incapable of providing appropriate care should be avoided. This report is a critical view of the application of the START triage nomenclature in the prehospital arena following a train crash in Los Angeles County on 26 January 2005.Methods:A scheduled debriefing was held with the major fire and emergency medical services responders, Medical Alert Center staff, and hospitals to assess and review the response to the incident. Site visits were made to all of the hospitals involved. Follow-up questions were directed to emergency department staff that were on duty during the day of the incident.Results:The five Level-I Trauma Centers responded to the poll with the capacity to receive a total of 12 “Immediate” patients, 2.4 patients per center, the eight Level-II Trauma Centers responded with capacity to receive 17 “Immediate” patients, two patients per center, while the 25 closest community hospitals offered to accept 75 “Immediate” patients, three patients per hospital. These community hospitals were typically about one-half of the size of the trauma centers (average 287 beds versus 548, average 8.7 operating rooms versus 16.6). Twenty-six patients were transported to a community hospital >15 miles from the scene, while eight closer community hospitals did not receive any patients.Conclusions:The debriefing summary of this incident concluded that there were no consistently used criteria to decide ultimate destination for “Immediates”, and that they were distributed about equally between community hospitals and trauma centers.

Role of Air-Medical Evacuation in Mass-Casualty Incidents—A Train Collision Experience

2009 ◽  
Vol 24 (3) ◽  
pp. 271-276 ◽  
Author(s):  
Amit Assa ◽  
Dan-Avi Landau ◽  
Erez Barenboim ◽  
Liav Goldstein
Keyword(s):  
Mass Casualty ◽  
Search And Rescue ◽  
Mass Casualty Incidents ◽  
Medical Decision ◽  
Trauma Centers ◽  
Medical Evacuation ◽  
Major Disaster ◽  
Trauma Victims ◽  
Medical Teams ◽  
Accident Site

AbstractBackground:On 21 June 2005, a passenger train collided with a truck near Revadim, Israel.The collision resulted in a multiple-scene mass-casualty incident in an area characterized by difficult access and a relatively long distance from trauma centers. A major disaster response was initiated by civilian and military medical forces including the Israeli Air Force (IAF) Search and Rescue teams. The air-medical evacuation from the accident site to the trauma centers, the activities of the airborne medical teams, and the lessons learned from this event are described in this report.Methods:A retrospective analysis of data gathered from relevant elements that participated in management, treatment, and evacuation from the accident site was conducted.Results:The accident resulted in 289 injured passengers and seven of the injured were killed. Six helicopters (performing nine sorties) participated. Helicopters evacuated trauma victims and aided in transporting air-medical teams to the site of the collision.Overall, 35 trauma victims (10 urgent) were evacuated by air to trauma centers. The length of time between the first helicopter landing and completion of the air evacuation was 83 minutes. The airmedical evacuation operation was controlled by the commander of the IAF Search and Rescue. Different crew compositions were set in real time.Conclusions:Air-medical evacuation during this unique event enabled prompt transportation of casualties from the scene to trauma centers and provided reasonable distribution of patients between various centers in the region.This operation highlighted the necessity for flexibility in medical decision-making and the need for non-conventional solutions regarding crew compositions during management of an airborne evacuation in similar settings. Air-medical evacuation should be considered as a part of responses to mass-casualty incidents, especially when the site is remote or characterized by accessibility difficulties.

Development of an accelerated MSCT protocol (Triage MSCT) for mass casualty incidents: comparison to MSCT for single-trauma patients

2006 ◽  
Vol 12 (5) ◽  
pp. 203-209 ◽  
Author(s):  
M. Körner ◽  
M. Krötz ◽  
K.-G. Kanz ◽  
K.-J. Pfeifer ◽  
M. Reiser ◽  
...  
Keyword(s):  
Mass Casualty ◽  
Mass Casualty Incidents ◽  
Trauma Patients

Developing a Hospital Disaster Preparedness Plan for Mass Casualty Incidents: Lessons Learned From the Downtown Beirut Bombing

2017 ◽  
Vol 12 (3) ◽  
pp. 379-385 ◽  
Author(s):  
Mazen El Sayed ◽  
Ali F. Chami ◽  
Eveline Hitti
Keyword(s):  
Disaster Preparedness ◽  
Disaster Response ◽  
Hospital Staff ◽  
Mass Casualty ◽  
Lessons Learned ◽  
Mass Casualty Incidents ◽  
Preparedness Plan ◽  
Security Issues ◽  
Response System ◽  
General Response

AbstractMass casualty incidents (MCIs) are becoming more frequent worldwide, especially in the Middle East where violence in Syria has spilled over to many neighboring countries. Lebanon lacks a coordinated prehospital response system to deal with MCIs; therefore, hospital preparedness plans are essential to deal with the surge of casualties. This report describes our experience in dealing with an MCI involving a car bomb in an urban area of downtown Beirut, Lebanon. It uses general response principles to propose a simplified response model for hospitals to use during MCIs. A summary of the debriefings following the event was developed and an analysis was performed with the aim of modifying our hospital’s existing disaster preparedness plan. Casualties’ arrival to our emergency department (ED), the performance of our hospital staff during the event, communication, and the coordination of resources, in addition to the response of the different departments, were examined. In dealing with MCIs, hospital plans should focus on triage area, patient registration and tracking, communication, resource coordination, essential staff functions, as well as on security issues and crowd control. Hospitals in other countries that lack a coordinated prehospital disaster response system can use the principles described here to improve their hospital’s resilience and response to MCIs. (Disaster Med Public Health Preparedness. 2018; 12: 379–385)

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