关键词:

股骨转子间骨折, 内固定失效, 股骨颈前倾角, 机器学习算法

Abstract:

Objective　To analyze the correlation between femoral neck anteversion angle and internal fixation failure of intertrochanteric fracture, and to construct a prediction model of internal fixation failure of intertrochanteric fracture based on machine learning algorithm. Methods　A total of 206 patients with intertrochanteric fracture who received internal fixation surgery in Shangluo Central Hospital from January 2022 to September 2023 were retrospectively selected. Among them, there were 103 males and 103 females, aged (69.75±6.90) years, with a body mass index of (22.63±2.31) kg/m², 152 cases of stable fracture and 54 cases of unstable fracture. According to the follow-up results 12 months after surgery, the patients were divided into two groups: an internal fixation failure group (22 cases) and a normal group (184 cases). The clinical data of the patients were collected, factors of internal fixation failure in patients were analyzed by univariate analysis (independent sample t test and χ² test), and machine learning algorithms including logistic regression, categorical regression tree (CRT), and backpropagation neural network (BPNN) were used to construct a prediction model of internal fixation failure of intertrochanteric fracture. The receiver operating characteristic curve (ROC) was used to compare the predictive values of internal fixation failure models constructed by the three methods. Results　Univariate analysis showed that there were statistically significant differences in the body mass index, osteoporosis, fracture reduction quality, lateral wall thickness, and femoral neck anteversion angle between the two groups (t=3.624, P<0.001; χ²=5.016, P=0.025; χ²=7.529, P=0.023; t=4.464, P<0.001; t=7.602, P<0.001). Multivariate logistic regression analysis showed that body mass index, poor fracture reduction quality, lateral wall thickness, and femoral neck anteversion angle were independent influencing factors for internal fixation failure (OR=1.570, P=0.002; OR=22.315, P=0.009; OR=0.589, P=0.001; OR=2.378, P<0.001). The prediction model constructed by CRT method showed that femoral neck anteversion angle, lateral wall thickness, fracture reduction quality, and body mass index were all classification factors for internal fixation failure. The results of BPNN model showed that the most important influencing factors were femoral neck anteversion angle, lateral wall thickness, body mass index, fracture reduction quality, and osteoporosis. The areas under the curves (AUCs) of logistic regression model, CRT model, and BPNN model were 0.952, 0.919, and 0.950, respectively; the results of non-parametric DeLong test showed that there was no statistically significant difference in the prediction performance among the three models (P_{logistic regression model-CRT model} =0.158, P_{logistic regression model-BPNN model} =0.782, P_{CRT model-BPNN model} =0.219). Conclusions　The internal fixation failure of intertrochanteric fracture is correlated with femoral neck anteversion angle, lateral wall thickness, body mass index, and fracture reduction quality, etc. The three prediction models constructed in this study all show good prediction ability and can be further applied.

Key words:

 , Intertrochanteric fracture of femur, Failure of internal fixation, Anteversion angle of femoral neck, Machine learning algorithm