Construction and evaluation of prediction model for postoperative re-fractures in elderly patients with hip fractures.

Abstract

The aim of study was to construct a postoperative re-fracture prediction model for elderly hip fracture patients using an automated machine learning algorithm to provide a basis for early identification of patients with high risk of re-fracture occurrence. Clinical data were collected and subjected to univariate and multivariate analyses to determine the independent risk factors affecting postoperative re-fracture of hip fracture in the elderly. The collected data were divided into training and validation sets in a ratio of 7:3, AutoGluon was applied to construct LightGBMXT, LightGBM, RandomForestGini, RandomForestEntr, CatBoost, NeuralNetFastAI, XGBoost, NeuralNetTorch, LightGBMLarge and WeightedEnsemble_L2 prediction models, and the constructed models were evaluated using evaluation indicators. The models were externally validated and the model with the best prediction performance was selected. The incidence of postoperative re-fracture was about 11.7%, and age, comorbid diabetes mellitus, comorbid osteoporosis, rehabilitation exercise status, and preoperative total protein level were considered as independent risk factors. The top three models in terms of AUC values in the training set were WeightedEnsemble_L2 (0.9671), XGBoost (0.9636), and LightGBM (0.9626), the WeightedEnsemble_L2 (0.9759) was best in the external validation. Based on the AUC and other evaluation indicators, WeightedEnsemble_L2 was considered the model with the best prediction performance. The constructed model is highly generalizable and applicable, and can be used as an effective tool for healthcare professionals to assess and manage patients' risk of re-fracture.