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Predicting hospital mortality among injured children using a national trauma database
Randall S Burd1, Tai S Jang, Satish S Nair
1Department of Surgery, Division of Pediatric Surgery, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA. burdrs@umdnj.edu
Insights
A new model accurately predicts pediatric trauma mortality using vital signs and Glasgow Coma Scale (GCS) scores, even with missing data. This approach improves upon existing trauma scores for better clinical application.
Area of Science:
- Pediatric Emergency Medicine
- Trauma Surgery
- Biostatistics
Background:
- Predicting mortality in injured children is crucial for acute care.
- Existing trauma scoring systems often lack generalizability due to missing data in national databases.
- Developing robust predictive models requires addressing data imputation challenges.
Purpose of the Study:
- To develop a generalizable model for predicting pediatric trauma mortality using initial patient evaluation components.
- To address limitations of missing data in national trauma registries for predictive modeling.
- To create a statistically reliable model applicable in diverse acute care settings.
Main Methods:
- Logistic regression analysis of pediatric patient data from the National Pediatric Trauma Registry (NPTR) (1996-1999).
- Utilized vital signs, Glasgow Coma Scale (GCS) score, and intubation status as predictors.
- Applied multiple imputation techniques to handle missing data, with validation on independent NPTR and National Trauma Data Bank (NTDB) datasets.
Main Results:
- Complete case analysis identified only GCS-eye and intubation status as significant predictors.
- Multiple imputation identified additional predictors (systolic blood pressure, pulse, GCS-motor), significantly improving model discrimination (c-index 0.947-0.973).
- The developed model demonstrated favorable comparison to the Revised Trauma Score (RTS) and Pediatric Trauma Score in terms of false-negative rates.
Conclusions:
- A pediatric trauma mortality prediction model developed using multiple imputation for missing data shows improved performance over existing scores.
- The proposed methodology offers a more statistically reliable and clinically applicable approach to trauma prediction.
- Handling missing data effectively is key to developing accurate and generalizable predictive models in pediatric trauma care.
Purpose:
The purpose of this study was to develop a model that accurately predicts mortality among injured children based on components of the initial patient evaluation and that is generalizable to diverse acute care settings. Important predictive variables obtained in an emergency setting are frequently missing in even large national databases, limiting their effectiveness for developing predictions. In this study, a model predicting pediatric trauma mortality was developed using a national database and methods to handle missing data that may avoid biases that can occur restricting analyses to complete cases.
Methods:
Records of pediatric patients included in the National Pediatric Trauma Registry (NPTR) between 1996 and 1999 were used as a training set in a logistic regression model to predict hospital mortality using vital signs, Glasgow Coma Scale (GCS) score, and intubation status. Multiple imputation was applied to handle missing data. The model was tested using independent data from the NPTR and National Trauma Data Bank (NTDB).
Results:
Complete case analysis identified only GCS-eye and intubation status as predictors of mortality. A model based on complete case analysis had good discrimination (c-index = 0.784) and excellent calibration (Hosmer-Lemeshow c-statistic, 6.8) (p > 0.05). Using multiple imputation, three additional predictors of mortality (systolic blood pressure, pulse, and GCS-motor) were identified and improved model performance was observed. The model developed using multiple imputation had excellent discrimination (c-index, 0.947-0.973) in both test datasets. Calibration was better in the NPTR testing set than in the NTDB (Hosmer-Lemeshow c-statistic, 9.2 for NPTR [p > 0.05] and 258.2 for NTDB [p < 0.05]). At a probability cutoff that minimized misclassification in the training set, the false-negative and false-negative rates of the model were better than those obtained with either the Revised Trauma Score (RTS) or Pediatric Trauma Score using data from the NPTR testing set. Although the false-positive rates were lower with the RTS using data from the NTDB, the false-negative rates of the proposed model and the RTS were similar in this test dataset.
Conclusions:
Using multiple imputation to handle missing data, a model predicting pediatric trauma mortality was developed that compared favorably with existing trauma scores. Application of these methods may produce predictive trauma models that are more statistically reliable and applicable in clinical practice.