scholarly journals Comparison of the Effects of Sacco and START Triage Methods in the Death Risk Assessment of Mass Trauma Patients after Earthquake

2019 ◽  
Vol 34 (s1) ◽  
pp. s109-s110
Author(s):  
Wang Aoyu ◽  
Lin Run ◽  
Chen Yaqi ◽  
Tao Mengjiao ◽  
Hu Hai
Keyword(s):  
Risk Assessment ◽  
Emergency Department ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Trauma Patients ◽  
Receiver Operator Curve ◽  
Mass Trauma ◽  
Death Risk ◽  
The One ◽  
Operator Curve

Introduction:Compared with traditional START Triage Method, the Sacco Triage Method is a new way to access death risk in disaster scenes. However, due to the difficulties in disaster medical research, there is still no evidence to prove which one is more effective.Aim:To assess and compare the value of START Triage Method and Sacco Triage Method in the death risk assessment of transport and the one-month death risk assessment of the earthquake mass trauma patients.Methods:A retrospective analysis was conducted on 1,612 patients who were transferred to the West China Hospital by assigning to different triage levels by Sacco Triage Method and START Triage Method respectively. Both of the triage methods were evaluated based on death cases on either during transport or in the emergency department, using the area under the receiver-operator curve.Results:For death during the transport and in the emergency department, the receiver-operator curve of two groups reflected as 0.721 and 0.649. For death in a consequence, the receiver-operator curve of the two groups was revealed as 0.667 and 0.519.Discussion:As an accurate triage method, the Sacco Triage Method may be used in a mass casualty incident. It is a more effective way than the START Triage Method for the evaluation of death risk assessment of the mass trauma patients.

The effect of training and experience on mass casualty incident triage performance: Evidence from emergency personnel in a high complexity university hospital

2019 ◽  
Vol 14 (2) ◽  
pp. 113-119 ◽  
Author(s):  
Juan P. Vargas, MD, MSc ◽  
Ives Hubloue, MD, PhD ◽  
Jazmín J. Pinzón, MD ◽  
Alejandra Caycedo Duque, MD
Keyword(s):  
Emergency Department ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
University Hospital ◽  
Trauma Patients ◽  
Post Intervention ◽  
Cross Sectional ◽  
Emergency Department Staff ◽  
Department Staff ◽  
Before And After

Mass casualty incident (MCI) can occur at any time and place and health care institutions must be prepared to deal with these incidents. Emergency department staff rarely learn how to triage MCI patients during their medical or nurse degrees, or through on-the-job training. This study aims to evaluate the effect of training and experience on the MCI triage performance of emergency personnel.Methodology: This was a cross-sectional prospective study that analyzed the performance of 94 emergency department staff on the triage classifications of 50 trauma patients, before and after a short training in MCI triage, while taking into account their academic background and work experience.Results: The participants were assigned initially to one of two groups: low experience if they had less than 5 years of practice, and high experience if they had more than 5 years of practice. In the low experience group, the initial accuracy was 45.76 percent, over triage 45.84 percent, and subtriage 8.38 percent. In the high experience group, the initial accuracy was 53.80 percent, over triage 37.66 percent, and sub triage 8.57 percent.Postintervention Results: In the low experience group, the post intervention accuracy was 63.57 percent, over triage 21.15 percent, and subtriage 15.30 percentage. In the high experience group, the post-intervention accuracy was 67.66 percentage, over triage 15.19 percentage, and subtriage 17.14 percentage.  Conclusion: Upon completion of this study, it can be concluded that MCI triage training significantly improved the performance of all those involved in the workshop and that experience plays an important role in MCI triage performance.

scholarly journals Comparing the Predictive Power of Preoperative Risk Assessment Tools to Best Predict Major Adverse Cardiac Events in Kidney Transplant Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Colin P. Dunn ◽  
Emmanuel U. Emeasoba ◽  
Ari J. Holtzman ◽  
Michael Hung ◽  
Joshua Kaminetsky ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Assessment Tools ◽  
Major Adverse Cardiac Events ◽  
Receiver Operator Curve ◽  
Cardiac Events ◽  
Preoperative Risk ◽  
Preoperative Risk Assessment ◽  
Transplant Patients ◽  
Adverse Cardiac Events ◽  
Operator Curve

Background. Patients undergoing kidney transplantation have increased risk of adverse cardiovascular events due to histories of hypertension, end-stage renal disease, and dialysis. As such, they are especially in need of accurate preoperative risk assessment. Methods. We compared three different risk assessment models for their ability to predict major adverse cardiac events at 30 days and 1 year after transplant. These were the PORT model, the RCRI model, and the Gupta model. We used a method based on generalized U-statistics to determine statistically significant improvements in the area under the receiver operator curve (AUC), based on a common major adverse cardiac event (MACE) definition. For the top-performing model, we added new covariates into multivariable logistic regression in an attempt to create further improvement in the AUC. Results. The AUCs for MACE at 30 days and 1 year were 0.645 and 0.650 (PORT), 0.633 and 0.661 (RCRI), and finally 0.489 and 0.557 (Gupta), respectively. The PORT model performed significantly better than the Gupta model at 1 year (p=0.039). When the sensitivity was set to 95%, PORT had a significantly higher specificity of 0.227 compared to RCRI’s 0.071 (p=0.009) and Gupta’s 0.08 (p=0.017). Our additional covariates increased the receiver operator curve from 0.664 to 0.703, but this did not reach statistical significance (p=0.278). Conclusions. Of the three calculators, PORT performed best when the sensitivity was set at a clinically relevant level. This is likely due to the unique variables the PORT model uses, which are specific to transplant patients.

scholarly journals A Surprise Mass Casualty Incident Simulation: Does It Improve Knowledge or Is It Just a Bit of Fun?

2019 ◽  
Vol 34 (s1) ◽  
pp. s18-s19
Author(s):  
Brad Mitchell ◽  
Karen Hammad ◽  
Dana Aldwin
Keyword(s):  
Emergency Department ◽  
Low Cost ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Major Incident ◽  
Likert Scales ◽  
The Past ◽  
Emergency Department Nurses ◽  
Major Incidents ◽  
Simulation Scenario

Introduction:We opened a national conference in Australia with a surprise mass casualty simulation scenario of a van versus multiple persons outside the conference venue. The purpose of this exercise was to increase awareness of, and preparation for, mass casualty incident (MCI) events for the conference delegates who were paramedics, emergency department nurses, and doctors.Aim:The aim of the research is to understand whether a surprise MCI simulation is a useful way to increase knowledge and motivate preparedness.Methods:A survey hosted on Qualtrics was circulated to delegates via email. The survey was designed by the research team and had 38 questions about demographics and respondents’ experience with MCIs, as well as their perceptions of the simulation exercise. The questions were a mixture of 5-point Likert scales, multiple choice, and short answers.Results:The majority of respondents were clinicians (n = 66, 76%) and those who worked in emergency departments or the prehospital setting (n = 75, 86%). While the majority had not responded to an MCI in the past 5 years (n = 67, 77%), more than half (n = 50, 57%) had undertaken MCI training during this time. Overall, a vast majority of respondents found the simulation to be a worthwhile exercise that increased knowledge and preparedness. An overwhelming majority also reported that the simulation was relevant to practice, of high quality, and a useful way to teach about major incidents.Discussion:Our surprise major incident simulation was a fun and effective way to raise awareness and increase knowledge in prehospital and emergency department clinicians about MCI response. This approach to simulation can be easily replicated at relatively low cost and is, therefore, a useful solution to training a group of multidisciplinary health professionals outside of the workplace.

scholarly journals Westgate Shootings: An Emergency Department Approach to a Mass-casualty Incident

2014 ◽  
Vol 29 (5) ◽  
pp. 538-541 ◽  
Author(s):  
Benjamin W. Wachira ◽  
Ramadhani O. Abdalla ◽  
Lee A. Wallis
Keyword(s):  
Emergency Department ◽  
Emergency Preparedness ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
University Hospital ◽  
Shopping Mall ◽  
Major Incident ◽  
Local Health ◽  
Publication Type ◽  
Communication Assessment

AbstractAt approximately 12:30 pm on Saturday September 21, 2013, armed assailants attacked the upscale Westgate shopping mall in the Westlands area of Nairobi, Kenya. Using the seven key Major Incident Medical Management and Support (MIMMS) principles, command, safety, communication, assessment, triage, treatment, and transport, the Aga Khan University Hospital, Nairobi (AKUH,N) emergency department (ED) successfully coordinated the reception and care of all the casualties brought to the hospital.This report describes the AKUH,N ED response to the first civilian mass-casualty shooting incident in Kenya, with the hope of informing the development and implementation of mass-casualty emergency preparedness plans by other EDs and hospitals in Kenya, appropriate for the local health care system.WachiraBW, AbdallaRO, WallisLA. Westgate shootings: an emergency department approach to a mass-casualty incident. Prehosp Disaster Med. 2014;29(5):1-4.

scholarly journals Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions

2021 ◽  
pp. 1106-1126
Author(s):  
Dylan J. Peterson ◽  
Nicolai P. Ostberg ◽  
Douglas W. Blayney ◽  
James D. Brooks ◽  
Tina Hernandez-Boussard
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Emergency Department ◽  
Emergency Department Visits ◽  
Test Model ◽  
Receiver Operator Curve ◽  
Patients At Risk ◽  
Electronic Health ◽  
Chemotherapy Patients ◽  
Operator Curve

PURPOSE Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data. METHODS Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve. RESULTS Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients. CONCLUSION Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.

Managing a Mass CO Poisoning: Critical Issues and Solutions From the Field to the Hyperbaric Chamber

2016 ◽  
Vol 11 (2) ◽  
pp. 251-255 ◽  
Author(s):  
Adriano Valerio ◽  
Matteo Verzè ◽  
Francesco Marchiori ◽  
Igor Rucci ◽  
Lucia De Santis ◽  
...  
Keyword(s):  
Emergency Department ◽  
Carbon Monoxide ◽  
Emergency Services ◽  
Carbon Monoxide Poisoning ◽  
Heating System ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Accidental Poisoning ◽  
Complex Situation ◽  
First Case

AbstractCarbon monoxide acute intoxication is a common cause of accidental poisoning in industrialized countries and sometimes it produces a real mass casualty incident. The incident described here occurred in a church in the province of Verona, when a group of people was exposed to carbon monoxide due to a heating system malfunction. Fifty-seven people went to the Emergency Department. The mean carboxyhemoglobin (COHb) level was 10.1±5.7% (range: 3-25%). The clinicians, after medical examination, decided to move 37 patients to hyperbaric chambers for hyperbaric oxygen (HBO) therapy. This is the first case report that highlights and analyses the logistic difficulties of managing a mass carbon monoxide poisoning in different health care settings, with a high influx of patients in an Emergency Department and a complex liaison between emergency services. This article shows how it is possible to manage a complex situation with good outcome. (Disaster Med Public Health Preparedness. 2017;11:251–255)

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 The Experience of a Mass Casualty Incident Call in a Tertiary Hospital after the 2018 Hualien Earthquake

2019 ◽  
Vol 34 (s1) ◽  
pp. s131-s131
Author(s):  
Hsing Chia Cheng ◽  
Kuang Yu Niu ◽  
Ming Han Ho
Keyword(s):  
Emergency Department ◽  
Healthcare Providers ◽  
Task Assignment ◽  
Tertiary Hospital ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Incident Command System ◽  
Incident Command ◽  
Efficiency And Effectiveness ◽  
Training Courses

Introduction:After a 6.0 magnitude earthquake struck Hualien on February 6, 2018, over one hundred and fifty patients crammed into the emergency department of a nearby tertiary hospital within two hours. The mass casualty incident (MCI) call was activated, and over 300 related personnel responded to the call and engaged with the MCI management.Aim:This research aimed to analyze the practice of an MCI call and to form the strategies to improve its efficiency and effectiveness.Methods:The research was conducted in a tertiary hospital in Hualien, Taiwan. Questionnaires regarding the practice of the MCI call were sent out to the healthcare providers in the emergency department who responded to that MCI operation.Results:Thirty-seven responders in the emergency department were involved in this study. 78% had participated in training courses for hospital incident command system (HICS) or MCI management before this event. On arrival at the emergency department, 69.4% of the responders were aware of the check-in station and received a clear task assignment and briefing. During the operation, 25.7% reported the lack of confidence carrying out the assigned tasks and 54.1% of the participants experienced great stress (stress score over 7 out of 10).Discussion:MCI is an uncommon event for hospital management. It is universally challenging owing to its unpredictable and time-sensitive nature. Furthermore, the administration could be further complicated by the associated disasters. Despite regular exercises and drills, there are still a significant number of participants experiencing stress and confusion during the operation. The chaotic situation may further compromise the performance of the participants. This study showed that optimizing task briefing and on-site directions may improve the performance of the MCI participants.

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