Emergency Medical Services personnel are often the first to intervene in the care of critically ill children. Airway management is a fundamental step in prehospital resuscitation, yet there is significant variation in current prehospital airway management practices. Our objective is to present a methodologic approach to determine the optimal strategy for prehospital pediatric airway management. We describe the conceptual premise for the Pediatric Prehospital Airway Resuscitation Trial, a novel Bayesian adaptive sequential platform trial. We developed an innovative design to enable comparison of the three predominant prehospital pediatric airway techniques (bag-mask-ventilation, supraglottic airway insertion, and endotracheal intubation) in three distinct disease groups (cardiac arrest, major trauma, and other respiratory failure). We used a Bayesian statistical approach to provide flexible modeling that can adapt based on prespecified rules according to accumulating trial data with patient enrollment continuing until stopping rules are met. The approach also allows the comparison of multiple interventions in sequence across the different disease states. This Bayesian hierarchical model will be the primary analysis method for the Pediatric Prehospital Airway Resuscitation Trial. The model integrates information across subgroups, a technique known as “borrowing” to generate accurate global and subgroup-specific estimates of treatment effects and enables comparisons of airway intervention arms within the overarching trial. We will use this Bayesian hierarchical linear model that adjusts for subgroup to estimate treatment effects within each subgroup. The model will predict a patient-centered score of 30-day intensive care unit-free survival using arm, subgroup, and emergency medical services agency as predictors. The novel approach of Pediatric Prehospital Airway Resuscitation Trial will provide a feasible method to determine the optimal strategy for prehospital pediatric airway management and may transform the design of future prehospital resuscitation trials.
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