An interpretable multimodal biomechanical-radiological model for predicting fixation failure in osteoporotic hip fractures: A retrospective cohort study
Abstract
This study aimed to develop and validate a multimodal prediction model integrating biomechanical and radiological variables to predict internal fixation failure in osteoporotic hip fractures, enabling individualized risk assessment and perioperative decision-making. Patients with osteoporotic hip fractures undergoing internal fixation between March 2019 and February 2024 were retrospectively enrolled and randomly divided into a training set (n = 249) and a validation set (n = 107) at a 7:3 ratio. The primary outcome was implant-related failure within 12 months post-surgery. In the training set, univariate analysis and multivariate logistic regression were performed to screen associated factors. Using independent predictors, 3 machine learning models (random forest, support vector machine, and K-nearest neighbors) were developed and compared. The model with the best discriminative ability, assessed by the area under the receiver operating characteristic curve (AUC) with internal validation (bootstrapping), calibration curves, and decision curve analysis, was selected to construct a nomogram. No significant differences in baseline characteristics were observed between the training and validation sets (P > .05). Multivariate logistic regression identified that bone mineral density, maximum fracture end displacement, peak stress distribution of the implant, fracture reduction alignment deviation, and implant insertion depth were significantly associated with fixation failure (P…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 249 participants or records.
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