Development and Validation of a Predictive Model for the Prognosis of Complications of Supracondylar Fractures of The Humerus in Children
Abstract
Objective: Informing patient consultations and healthcare choices, clinical predictive models can offer patients tailored projections of the outcome. The most frequent elbow fractures in children are supracondylar humerus fractures, and clinical prediction models were still largely underutilized in these cases. By developing and verifying a prediction model to lower the risk of postoperative problems in children with supracondylar humerus fractures, this research sought to evaluate independent risk variables connected with the incidence of complications of supracondylar humerus fractures in children. Methods: We retrospectively studied 411 children with supracondylar humerus fractures treated surgically at our hospital from 2015 to 2019, and explored the independent risk factors affecting the prognosis of supracondylar humerus fractures in children in the study group using univariate and multifactorial Cox regression analysis, respectively. In addition, a prediction model based on the independent factors was constructed, a nomogram was made and data from the two cohorts were used to verify the feasibility and reliability of the model and visualize the data. Results: Height, older than eight years, weight, nerve damage, fracture type and with joystick technology of the child as independent risk factors influenced the prognosis of pediatric supracondylar humerus fractures in the modeling constructed by the training cohort, respectively. The results of the validation cohort were further screened for older than eight years, nerve injury and fracture type as independent prognostic factors. Conclusions: We were able to construct a predictive model based on a large genuine data sample, and clinical characteristics in this model could be used as independent predictors for reducing the occurrence of postoperative complications in supracondylar fractures. Combining basic vital signs and clinical risk factors into a simple and clear nomogram was more likely to result in the best treatment plan.
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