Screening risk factors for the occurrence of wedge effects in intramedullary nail fixation for intertrochanteric fractures in older people via machine learning and constructing a prediction model: a retrospective study.

purposeThe wedge effect (V-effect) is a common complication in intramedullary nailing surgery for intertrochanteric fractures and can significantly affect postoperative outcomes. The purpose of this study was to screen risk factors for the intraoperative V-effect in intertrochanteric fractures and to develop a clinical prediction model.

methodsA total of 319 patients (77 patients who developed V-effects) from China were randomly divided into a training set (n = 223) and a validation set (n = 96) at a ratio of 7:3. The variables were screened via 3 machine learning methods, including least absolute shrinkage and selection operator (LASSO) regression, the Boruta algorithm, and recursive feature elimination (RFE). Variables that appeared in the three machine learning methods were included in multivariate logistic regression to construct predictive models. Spearman correlation analysis was used to exclude covariance between variables. Restricted cubic splines (RCSs) were used to analyze the relationships among femoral lateral wall thickness, BMI, and the V effect. The differentiation, calibration and clinical applicability of the model were assessed, and the reasonability of the model was analyzed.

resultsMachine learning identified 8 variables that appeared in these 3 machine learning methods, and the covariance between these 8 variables was excluded (r < 0.6). BMI, surgical experience, a lesser trochanteric fracture, the thickness of the lateral wall, the insertion point, bone density, fracture classification, and holiday surgery were found to be risk factors for the occurrence of the V-effect via multivariate logistic regression. The RCS analysis revealed that the lateral wall thickness, BMI, and occurrence of the V effect were linearly related. The final predictive model had good differentiation, calibration and clinical applicability, and it had better predictive efficacy than the other models did.

conclusionThis study employed three machine learning variable selection methods-the LASSO, RFE, and Boruta algorithms-to construct a V-effect predictive model. The model enables orthopedic surgeons to better understand the risk factors associated with the V-effect and provides a reference for surgeons to implement appropriate measures to reduce the incidence of the V-effect.