Validation of a venous thromboembolism prediction algorithm for pediatric trauma: A National Trauma Data Bank (NTDB) analysis
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In brief
In brief
This study validates a predictive algorithm for venous thromboembolism (VTE) risk in pediatric trauma patients using National Trauma Data Bank data from 2013-2016. The model demonstrated strong discriminatory ability (AUC >0.93) across multiple cohorts, supporting its potential use in identifying high-risk children who may benefit from VTE prophylaxis.
Written by the GCMD Library team from the article.
Purpose
We sought to validate a risk model to predict venous thromboembolism (VTE) in pediatric trauma through an analysis of a contemporary cohort in the National Trauma Data Bank (NTDB).
Study design
Prospective internal validation was performed in 10 randomly stratified samples of children (age 0–17 years) from the NTDB 2013–2016. Model discrimination was determined by calculation of the c-statistic (AUC), and calibration was evaluated through analysis of observed to expected (O:E) ratio. Recalibration was performed with application of a mixed-effects logistic regression. Model parameters were reestimated based on recalibration.
Results
Retrospective review identified 481,485 pediatric trauma patients with 729 (0.2%) episodes of VTE. Discriminatory ability of the model in all random cohorts was significant with AUC > 0.93 (p