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Early prediction of posttraumatic in-hospital mortality in pediatric patients
Xan F Courville1, Kenneth J Koval, Brian T Carney
1Dartmouth Hitchcock Medical Center, Department of Orthopaedics, Lebanon, NH 03756, USA. Xan.Courville@hitchcock.org
Insights
A new tool can predict pediatric in-hospital mortality using emergency department (ED) data. This helps quickly triage injured children to trauma centers for better care.
Area of Science:
- Pediatric Emergency Medicine
- Trauma Surgery
- Biostatistics
Background:
- Pediatric in-hospital mortality prediction is crucial for timely intervention.
- Existing tools may not fully utilize early emergency department (ED) data.
- Developing an accurate triaging tool is essential for improving outcomes in critically injured children.
Purpose of the Study:
- To develop and validate a predictive tool for pediatric in-hospital mortality.
- To identify key variables available early in the emergency department (ED) that indicate mortality risk.
- To create a data-driven approach for triaging critically injured pediatric patients.
Main Methods:
- Utilized the National Trauma Data Bank, including 224,628 pediatric patients (<18 years).
- Employed Chi-square-assisted interaction detection (CHAID) to analyze demographics, Glasgow Coma Scale (GCS), ED vital signs, and injury mechanisms.
- Randomly divided the cohort into training and testing sets for model development and validation.
Main Results:
- The study identified 16 of 19 potential variables associated with increased mortality risk.
- Low Glasgow Coma Scale (GCS) scores and systolic blood pressure in the ED were the most powerful predictors.
- The CHAID model demonstrated superior relative risk, sensitivity, and positive predictive value compared to GCS and systolic blood pressure alone.
Conclusions:
- A predictive tool using early ED data can identify children at risk of in-hospital mortality.
- This tool can aid frontline providers in rapid triage to pediatric trauma centers.
- The findings support improved resuscitation and educational strategies for pediatric trauma care.
Background:
The purpose of this study was to develop a triaging tool to predict pediatric in-hospital mortality from data available soon after emergency department (ED) presentation.
Methods:
The study group consisted of patients of less than 18 years of age from the National Trauma Data Bank with a reported in-hospital mortality status. Variables analyzed were (1) patient demographics, (2) Glasgow Coma Scale (GCS) values, (3) ED vital signs, (4) injury mechanism, and (5) number of days from trauma until admission. Chi-square-assisted interaction detection (CHAID) profiled patient subgroups. The final cohort was randomly divided into 2 equal sets: a training set to subgroup patients and a testing set to validate the prediction accuracy.
Results:
The cohort consisted of 224,628 patients with 2.29% in-hospital mortality. Sixteen of 19 potential variables were associated with increased risk of in-hospital mortality. The relative risk of dying was 61.7 times greater (95% confidence interval 57.5-66.1) when CHAID predicted mortality relative to when the model predicted survival (P<0.0001). The most powerful variables of the CHAID model were low total GCS scores and systolic blood pressure in the ED. The CHAID model had an improved relative risk and a better combination of sensitivity and positive predictive value compared with GCS and systolic blood pressure in predicting mortality.
Conclusions:
The risk of in-hospital mortality for injured children may be identified soon after arrival in the ED. This information may be used by frontline providers to appropriately triage patients to pediatric trauma centers quickly, to guide resuscitation, and for teaching purposes.