Evaluation of Missing Prehospital Physiological Values in Injured Children and Adolescents.

INTRODUCTION: Prehospital vital signs and the Glasgow Coma Scale score are often missing in clinical practice and not recorded in trauma databases. Our study aimed to identify factors associated with missing prehospital physiological values, including systolic blood pressure, heart rate, respiratory rate, peripheral oxygen saturation, and Glasgow Coma Scale. METHODS: We used our hospital trauma registry to obtain patient, injury, resuscitation, and transportation characteristics for injured children and adolescents (age <15 y). We evaluated the association of missing documentation of prehospital values with other patient, injury, transportation, and resuscitation characteristics using multivariable regression. We standardized vital sign values using age-adjusted z-scores. RESULTS: The odds of a missing physiological value decreased with age (odds ratio [OR] = 0.9, 95% confidence interval [CI] = 0.9, 0.9) and were higher when prehospital cardiopulmonary resuscitation was required (OR = 3.3, 95% CI = 1.9, 5.7). Among the physiological values considered, we observed the highest odds of missingness of systolic blood pressure, respiratory rate, and oxygen saturation. The odds of observing normal emergency department physiological values were lower when prehospital physiological values were missing (OR = 0.9, 95% CI = 0.9, 1.0; P = 0.04). CONCLUSIONS: Missing prehospital physiological values were associated with younger age and cardiopulmonary resuscitation among the injured children treated at our hospital. Measurement and documentation of physiological variables of patients with these characteristics should be targeted.

Discovering interpretable medical process models: A case study in trauma resuscitation.

Understanding the actual work (i.e., "work-as-done") rather than theorized work (i.e., "work-as-imagined") during complex medical processes is critical for developing approaches that improve patient outcomes. Although process mining has been used to discover process models from medical activity logs, it often omits critical steps or produces cluttered and unreadable models. In this paper, we introduce a TraceAlignment-based ProcessDiscovery method called TAD Miner to build interpretable process models for complex medical processes. TAD Miner creates simple linear process models using a threshold metric that optimizes the consensus sequence to represent the backbone process, and then identifies both concurrent activities and uncommon-but-critical activities to represent the side branches. TAD Miner also identifies the locations of repeated activities, an essential feature for representing medical treatment steps. We conducted a study using activity logs of 308 pediatric trauma resuscitations to develop and evaluate TAD Miner. TAD Miner was used to discover process models for five resuscitation goals, including establishing intravenous (IV) access, administering non-invasive oxygenation, performing back assessment, administering blood transfusion, and performing intubation. We quantitively evaluated the process models with several complexity and accuracy metrics, and performed qualitative evaluation with four medical experts to assess the accuracy and interpretability of the discovered models. Through these evaluations, we compared the performance of our method to that of two state-of-the-art process discovery algorithms: Inductive Miner and Split Miner. The process models discovered by TAD Miner had lower complexity and better interpretability than the state-of-the-art methods, and the fitness and precision of the models were comparable. We used the TAD process models to identify (1) the errors and (2)the best locations for the tentative steps in knowledge-driven expert models. The knowledge-driven models were revised based on the modifications suggested by the discovered models. The improved modeling using TAD Miner may enhance understanding of complex medical processes.

Transitioning Cognitive Aids into Decision Support Platforms: Requirements and Design Guidelines.

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.

Development and validation of a Bayesian belief network predicting the probability of blood transfusion after pediatric injury.

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. LEVEL OF EVIDENCE: Prognostic and Epidemiologic; Level III.

Reducing the Time to Blood Administration after Pediatric Injury: A Quality Improvement Initiative.

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 (ß = -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.

Metrics of shock in pediatric trauma patients: A systematic search and review.

INTRODUCTION: Shock-index (SI) and systolic blood pressure (SBP) are metrics for identifying children and adults with hemodynamic instability following injury. The purpose of this systematic review was to assess the quality of these metrics as predictors of outcomes following pediatric injury. MATERIALS AND METHODS: We conducted a literature search in Pubmed, SCOPUS, and CINAHL to identify studies describing the association between shock metrics on the morbidity and mortality of injured children and adolescents. We used the data presented in the studies to calculate the sensitivity and specificity for each metric. This study was registered with Prospero, protocol CRD42020162971. RESULTS: Fifteen articles met the inclusion criteria. seven studies evaluated SI or SIPA score, an age-corrected version of SI, as predictors of outcomes following pediatric trauma, with one study comparing SIPA score and SBP and one study comparing SI and SBP. The remaining eight studies evaluated SBP as the primary indicator of shock. The median sensitivity for predicting mortality and need for blood transfusion was highest for SI, followed by SIPA, and then SBP. The median specificity for predicting these outcomes was highest for SBP, followed by SIPA, and then SI. CONCLUSIONS: Common conclusions were that high SIPA scores were more specific than SI and more sensitive than SBP. SIPA score had better discrimination for severely injured children compared to SI and SBP. An elevated SIPA was associated with a greater need for blood transfusion and higher in-hospital mortality. SIPA is specific enough to exclude most patients who do not require a blood transfusion.