Related Experiment Video
Updated: Mar 7, 2026
A Pediatric Concussion Model in Mice: Closed Head Injury with Long-Term Disorders (CHILD)
Published on: February 7, 2025
Pediatric Trauma Assessment and Management Database: Leveraging Existing Data Systems to Predict Mortality and
Katherine T Flynn-O'Brien1, Mary E Fallat, Tom B Rice
1Harborview Injury Prevention and Research Center, University of Washington, Seattle, WA Department of Surgery, Division of General Surgery, University of Washington, Seattle, WA Department of Pediatrics, University of Washington, Seattle, WA Hiram C Polk, Jr Department of Surgery, Division of Pediatric Surgery, University of Louisville and Norton Children's Hospital, Louisville, KY Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI Department of Surgery, Division of General Pediatric Surgery, Children's Hospital of Wisconsin, Milwaukee, WI SCL Health, Broomfield, CO Department of Surgery, Division of Pediatric General, Thoracic, and Fetal Surgery, Children's Hospital of Philadelphia, Philadelphia, PA Department of Surgery, Division of General Pediatric Surgery, Children's Hospital of Los Angeles and USC Keck School of Medicine, Los Angeles, CA Virtual Pediatric Systems, LLC, Los Angeles, CA Department of Surgery, Division of General Pediatric Surgery, Akron Children's Hospital and Pediatric Surgery Center, Akron, OH.
Insights
Merging trauma registry and Virtual Pediatric Systems data improves pediatric trauma outcome prediction. This combined approach offers better risk-adjusted models for mortality and functional status, aiding multi-institutional care evaluation.
Area of Science:
- Pediatric critical care medicine
- Trauma registry data analysis
- Health informatics
Background:
- Improving pediatric trauma outcomes requires detailed data for process assessment.
- Existing national data systems can be leveraged for cost-effective data collection.
- A novel database was created by merging two national data systems.
Purpose of the Study:
- To develop a novel database by merging trauma registry and Virtual Pediatric Systems (VPS) data.
- To provide benchmarking metrics for pediatric trauma mortality and non-mortality outcomes.
- To assess care provided throughout the continuum of care across multiple institutions.
Main Methods:
- Merged trauma registry and VPS data for children (<18 years) discharged in 2013 after traumatic injury.
- Compared risk-adjusted models for inpatient mortality using trauma registry only, VPS only, and combined data.
- Developed a de novo prediction model for functional status using the Pediatric Overall Performance Category ( a scale measuring overall disability).
Main Results:
- The combined dataset yielded the best risk-adjusted model for predicting mortality.
- The final model for functional status showed adequate discrimination (C-statistic = 0.896) and calibration.
- Significant variation in the probability of poor outcomes was observed across different pediatric trauma centers.
Conclusions:
- Merging existing data systems enhances risk-adjusted modeling for mortality and functional status in pediatric trauma.
- The integrated database facilitates multi-institutional patient evaluation across the care continuum.
- This innovative merging approach is feasible, cost-effective, and has the potential to significantly impact patient care.
Background:
Efforts to improve pediatric trauma outcomes need detailed data, optimally collected at lowest cost, to assess processes of care. We developed a novel database by merging 2 national data systems for 5 pediatric trauma centers to provide benchmarking metrics for mortality and non-mortality outcomes and to assess care provided throughout the care continuum.
Study Design:
Trauma registry and Virtual Pediatric Systems, LLC (VPS) from 5 pediatric trauma centers were merged for children younger than 18 years discharged in 2013 from a pediatric ICU after traumatic injury. For inpatient mortality, we compared risk-adjusted models for trauma registry only, VPS only, and a combination of trauma registry and VPS variables (trauma registry+VPS). To estimate risk-adjusted functional status, we created a prediction model de novo through purposeful covariate selection using dichotomized Pediatric Overall Performance Category scale.
Results:
Of 688 children included, 77.3% were discharged from the ICU with good performance or mild overall disability and 17.6% with moderate or severe overall disability or coma. Inpatient mortality was 5.1%. The combined dataset provided the best-performing risk-adjusted model for predicting mortality, as measured by the C-statistic, pseudo-R2, and Akaike Information Criterion, when compared with the trauma registry-only model. The final Pediatric Overall Performance Category model demonstrated adequate discrimination (C-statistic = 0.896) and calibration (Hosmer-Lemeshow goodness-of-fit p = 0.65). The probability of poor outcomes varied significantly by site (p < 0.0001).
Conclusions:
Merging 2 data systems allowed for improved risk-adjusted modeling for mortality and functional status. The merged database allowed for patient evaluation throughout the care continuum on a multi-institutional level. Merging existing data is feasible, innovative, and has potential to impact care with minimal new resources.
More Related Videos
07:02An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System
Published on: January 12, 2011
07:21Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
Published on: May 27, 2022