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1.
Prehosp Disaster Med ; 37(3): 306-313, 2022 Jun.
Article En | MEDLINE | ID: mdl-35441588

INTRODUCTION: Many triage algorithms exist for use in mass-casualty incidents (MCIs) involving pediatric patients. Most of these algorithms have not been validated for reliability across users. STUDY OBJECTIVE: Investigators sought to compare inter-rater reliability (IRR) and agreement among five MCI algorithms used in the pediatric population. METHODS: A dataset of 253 pediatric (<14 years of age) trauma activations from a Level I trauma center was used to obtain prehospital information and demographics. Three raters were trained on five MCI triage algorithms: Simple Triage and Rapid Treatment (START) and JumpSTART, as appropriate for age (combined as J-START); Sort Assess Life-Saving Intervention Treatment (SALT); Pediatric Triage Tape (PTT); CareFlight (CF); and Sacco Triage Method (STM). Patient outcomes were collected but not available to raters. Each rater triaged the full set of patients into Green, Yellow, Red, or Black categories with each of the five MCI algorithms. The IRR was reported as weighted kappa scores with 95% confidence intervals (CI). Descriptive statistics were used to describe inter-rater and inter-MCI algorithm agreement. RESULTS: Of the 253 patients, 247 had complete triage assignments among the five algorithms and were included in the study. The IRR was excellent for a majority of the algorithms; however, J-START and CF had the highest reliability with a kappa 0.94 or higher (0.9-1.0, 95% CI for overall weighted kappa). The greatest variability was in SALT among Green and Yellow patients. Overall, J-START and CF had the highest inter-rater and inter-MCI algorithm agreements. CONCLUSION: The IRR was excellent for a majority of the algorithms. The SALT algorithm, which contains subjective components, had the lowest IRR when applied to this dataset of pediatric trauma patients. Both J-START and CF demonstrated the best overall reliability and agreement.


Mass Casualty Incidents , Algorithms , Child , Humans , Pilot Projects , Reproducibility of Results , Triage/methods
2.
Prehosp Disaster Med ; 35(2): 165-169, 2020 Apr.
Article En | MEDLINE | ID: mdl-32054549

INTRODUCTION: 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.


Disaster Planning , Mass Casualty Incidents/statistics & numerical data , Triage , Adolescent , Algorithms , Child , Child, Preschool , Computer Simulation , Humans , Infant , Infant, Newborn , Los Angeles , Prospective Studies , Reproducibility of Results
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