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Original Investigation | Pacific Coast Surgical Association

A Clinical Tool for the Prediction of Venous Thromboembolism in Pediatric Trauma Patients

Christopher R. Connelly, MD1; Amy Laird, PhD2; Jeffrey S. Barton, MD3,4; Peter E. Fischer, MD5; Sanjay Krishnaswami, MD6; Martin A. Schreiber, MD1; David H. Zonies, MD1; Jennifer M. Watters, MD1
[+] Author Affiliations
1Division of Trauma, Critical Care, and Acute Care Surgery, Department of Surgery, Oregon Health & Science University, Portland
2Division of Biostatistics, Department of Public Health & Preventive Medicine, Oregon Health & Science University, Portland
3Colon and Rectal Clinic of Houston, Department of Surgery, University of Texas, Houston
4currently with the Division of Colon and Rectal Surgery, Department of Surgery, Louisiana State University, New Orleans
5Division of Trauma, Surgical Critical Care and Acute Care Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
6Division of Pediatric Surgery, Department of Surgery, Oregon Health & Science University, Portland
JAMA Surg. 2016;151(1):50-57. doi:10.1001/jamasurg.2015.2670.
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Importance  Although rare, the incidence of venous thromboembolism (VTE) in pediatric trauma patients is increasing, and the consequences of VTE in children are significant. Studies have demonstrated increasing VTE risk in older pediatric trauma patients and improved VTE rates with institutional interventions. While national evidence-based guidelines for VTE screening and prevention are in place for adults, none exist for pediatric patients, to our knowledge.

Objectives  To develop a risk prediction calculator for VTE in children admitted to the hospital after traumatic injury to assist efforts in developing screening and prophylaxis guidelines for this population.

Design, Setting, and Participants  Retrospective review of 536 423 pediatric patients 0 to 17 years old using the National Trauma Data Bank from January 1, 2007, to December 31, 2012. Five mixed-effects logistic regression models of varying complexity were fit on a training data set. Model validity was determined by comparison of the area under the receiver operating characteristic curve (AUROC) for the training and validation data sets from the original model fit. A clinical tool to predict the risk of VTE based on individual patient clinical characteristics was developed from the optimal model.

Main Outcome and Measure  Diagnosis of VTE during hospital admission.

Results  Venous thromboembolism was diagnosed in 1141 of 536 423 children (overall rate, 0.2%). The AUROCs in the training data set were high (range, 0.873-0.946) for each model, with minimal AUROC attenuation in the validation data set. A prediction tool was developed from a model that achieved a balance of high performance (AUROCs, 0.945 and 0.932 in the training and validation data sets, respectively; P = .048) and parsimony. Points are assigned to each variable considered (Glasgow Coma Scale score, age, sex, intensive care unit admission, intubation, transfusion of blood products, central venous catheter placement, presence of pelvic or lower extremity fractures, and major surgery), and the points total is converted to a VTE risk score. The predicted risk of VTE ranged from 0.0% to 14.4%.

Conclusions and Relevance  We developed a simple clinical tool to predict the risk of developing VTE in pediatric trauma patients. It is based on a model created using a large national database and was internally validated. The clinical tool requires external validation but provides an initial step toward the development of the specific VTE protocols for pediatric trauma patients.

Figures in this Article


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Figure 1.
Area Under the Receiver Operating Characteristic Curve (AUROC) for Model 3 on the Training and Validation Data Sets

This model, developed on the training data set, maintained its performance on the validation data set.

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Figure 2.
Calculation of a Patient’s Points Total and the Predicted Probability of Venous Thromboembolism (VTE) Given the Points Total

Averaged over facilities, model 3 scores of 0 to 523 correspond to low risk (<1%) of VTE, scores of 524 to 688 correspond to medium risk (1%-5%), and scores of 689 to 797 correspond to high risk (>5%). The predicted probability averaged over facilities (ie, zero intercept) is shown as a dark blue line. The 95% CI band, shown as a set of solid light blue lines, represents variability in the predicted probabilities over facilities. Cutoff values for risk categories averaged over facilities are shown as dashed lines. GCS indicates Glasgow Coma Scale; ICU, intensive care unit; and NA, not applicable.

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