Derivation and Internal Validation of a Prediction Model for Pediatric Hand Fracture Triage

Abstract

Background: Pediatric hand fractures are common, and most can be managed by a period of immobilization. However, it remains challenging to identify those more complex fractures requiring the expertise of a hand surgeon to ensure a good outcome. The purpose of this study was to develop a prediction model for identification of complex pediatric hand fractures requiring care by a hand surgeon.

Methods: A 2-year retrospective cohort study of consecutively referred pediatric (<18 years) hand fracture patients was used to derive and internally validate a prediction model for identification of complex fractures requiring the expertise of a hand surgeon. These complex fractures were defined as those that required surgery, closed reduction, or four or more appointments with a hand surgeon. The model, derived by multivariable logistic regression analysis, was internally validated using bootstrapping and then translated into a risk index.

Results: Of 1170 fractures, 416 (35.6%) met criteria for a complex fracture. Multivariable regression analysis identified six significant predictors of complex fracture: open fracture, rotational deformity, angulation, condylar involvement, dislocation or subluxation, and displacement. Internal validation demonstrated good performance of the model (C-statistic = 0.88, calibration curve p = 0.935). A threshold of ≥1 point (ie, any one of the predictors) resulted in a simple, easy-to-use tool with 96.4% sensitivity and 45.5% specificity.

Conclusions: A high-performing and clinically useful decision support tool was developed for emergency and urgent care physicians providing initial assessment for children with hand fractures. This tool will provide the basis for development of a clinical care pathway for pediatric hand fractures.

Fig. 1.

Prediction model with regression coefficients and model intercept for complex fracture.

Fig. 2.

Performance of the prediction model for complex fracture: A, Area under the receiver operating curve for the prediction model. B, Calibration curve for the prediction model.

Fig. 3.

Risk index figure depicting total number of pediatric hand fractures by points score and the number of complex and simple fractures within each point score category.

Fig. 4.

Example of a clinical tool adapted from the prediction model for complex fractures with a threshold of ≥1 point and how to apply this clinical tool using a sample radiograph.