Introduction: Hemorrhage is the leading cause of preventable death in pediatric trauma patients. Timely blood administration is associated with improved outcomes in children and adults. This study aimed to identify delays to transfusion and improve the time to blood administration among injured children. Methods: A multidisciplinary team identified three activities associated with blood transfusion delays during the acute resuscitation of injured children. To address delays related to these activities, we relocated the storage of un-crossmatched blood to the emergency department (ED), created and disseminated an intravenous access algorithm, and established a nursing educator role for resuscitations. We performed comparative and regression analyses to identify the impact of these factors on the timeliness and likelihood of blood administration. Results: From January 2017 to June 2021, we treated 2159 injured children and adolescents in the resuscitation area, 54 (2.5%) of whom received blood products in the ED. After placing a blood storage refrigerator in the ED, we observed a centerline change that lowered the adjusted time-to-blood administration to 17 minutes (SD 11), reducing the time-to-blood administration by 11 minutes (β = −11.0, 95% CI = −22.0 to −0.9). The likelihood of blood administration was not changed after placement of the blood refrigerator. We observed no reduction in time following the implementation of the intravenous access algorithm or a nursing educator. Conclusions: Relocation of un-crossmatched blood storage to the ED decreased the time to blood transfusion. This system-based intervention should be considered a strategy for reducing delays in transfusion in time-critical settings.

BACKGROUND:Early recognition and intervention of hemorrhage are associated with decreased morbidity in children. Triage models have been developed to aid in the recognition of hemorrhagic shock after injury but require complete data and have limited accuracy. To address these limitations, we developed a Bayesian belief network, a machine learning model that represents the joint probability distribution for a set of observed or unobserved independent variables, to predict blood transfusion after injury in children and adolescents. METHODS:We abstracted patient, injury, and resuscitation characteristics of injured children and adolescents (age 1 to 18 years) from the 2017 to 2019 Trauma Quality Improvement Project database. We trained a Bayesian belief network to predict blood transfusion within 4 hours after arrival to the hospital following injury using data from 2017 and recalibrated the model using data from 2018. We validated our model on a subset of patients from the 2019 Trauma Quality Improvement Project. We evaluated model performance using the area under the receiver operating characteristic curve and calibration curves and compared performance with pediatric age-adjusted shock index (SIPA) and reverse shock index with Glasgow Coma Scale (rSIG) using sensitivity, specificity, accuracy, and Matthew's correlation coefficient (MCC). RESULTS:The final model included 14 predictor variables and had excellent discrimination and calibration. The model achieved an area under the receiver operating characteristic curve of 0.92 using emergency department data. When used as a binary predictor at an optimal threshold probability, the model had similar sensitivity, specificity, accuracy, and MCC compared with SIPA when only age, systolic blood pressure, and heart rate were observed. With the addition of the Glasgow Coma Scale score, the model has a higher accuracy and MCC than SIPA and rSIG. CONCLUSION:A Bayesian belief network predicted blood transfusion after injury in children and adolescents better than SIPA and rSIG. This probabilistic model may allow clinicians to stratify hemorrhagic control interventions based upon risk.

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Digital cognitive aids have the potential to serve as clinical decision support platforms, triggering alerts about process delays and recommending interventions. In this mixed-methods study, we examined how a digital checklist for pediatric trauma resuscitation could trigger decision support alerts and recommendations. We identified two criteria that cognitive aids must satisfy to support these alerts: (1) context information must be entered in a timely, accurate, and standardized manner, and (2) task status must be accurately documented. Using co-design sessions and near-live simulations, we created two checklist features to satisfy these criteria: a form for entering the pre-hospital information and a progress slider for documenting the progression of a multi-step task. We evaluated these two features in the wild, contributing guidelines for designing these features on cognitive aids to support alerts and recommendations in time- and safety-critical scenarios.

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