BMC medical informatics and decision making · 2025
Development of a prediction model for lower limb deformity in patients with hereditary multiple exostoses based on interpretable models and nomogram.
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Lu Rongbin, Ling He, Huang Zhao, Li Wencai, Liu Junjie, Lao Yonghui, Liu Qian, Ding Xiaofei
Аннотация
AIM: To investigate the impact of relevant factors on the occurrence of genu valgum and ankle valgus at admission in patients with hereditary multiple exostoses (HME) based on blood test results from the three months prior to admission, and to develop an interpretable machine learning model and nomogram to study the influence of these factors.
METHOD: This retrospective study collected blood test results and relevant clinical data from 140 HME patients in the three months prior to admission. The data were divided into training and validation sets at a 7:3 ratio. Five machine learning models were compared to select the optimal model for explaining risk factors. Multivariate regression analysis was further used to screen independent predictors, and a nomogram for predicting the probability of lower limb deformities in HME was constructed using R and JD_DCPM (V6.03, Jingding Medical Technology Co., Ltd.).
RESULT: The results showed that the Random Forest model demonstrated high stability in explaining the outcomes of genu valgum and ankle valgus in HME patients. SHAP analysis indicated that PA made a significant contribution to both outcomes. Multivariate regression analysis further identified ALB (0.037 [0.003-0.203]), GLB (0.083 [0.010-0.416]), and PA (0.025 [0.002-0.137]) as independent predictors for genu valgum. For ankle valgus, GLB (0.183 [0.053-0.571]), PA (0.162 [0.035-0.631]), and UA (7.156 [1.841-34.03]) were identified as independent predictors. The nomogram exhibited good prediction performance with moderate errors in both the training and validation groups.
CONCLUSION: ALB, GLB, and PA are independent predictors of genu valgum in HME patients three months later, while GLB, PA, and UA are independent predictors of ankle valgus. The interpretable RF model and constructed nomogram in this study enable clinicians to assess the risk of lower limb deformities in HME patients as early as possible, benefiting more patients.
METHOD: This retrospective study collected blood test results and relevant clinical data from 140 HME patients in the three months prior to admission. The data were divided into training and validation sets at a 7:3 ratio. Five machine learning models were compared to select the optimal model for explaining risk factors. Multivariate regression analysis was further used to screen independent predictors, and a nomogram for predicting the probability of lower limb deformities in HME was constructed using R and JD_DCPM (V6.03, Jingding Medical Technology Co., Ltd.).
RESULT: The results showed that the Random Forest model demonstrated high stability in explaining the outcomes of genu valgum and ankle valgus in HME patients. SHAP analysis indicated that PA made a significant contribution to both outcomes. Multivariate regression analysis further identified ALB (0.037 [0.003-0.203]), GLB (0.083 [0.010-0.416]), and PA (0.025 [0.002-0.137]) as independent predictors for genu valgum. For ankle valgus, GLB (0.183 [0.053-0.571]), PA (0.162 [0.035-0.631]), and UA (7.156 [1.841-34.03]) were identified as independent predictors. The nomogram exhibited good prediction performance with moderate errors in both the training and validation groups.
CONCLUSION: ALB, GLB, and PA are independent predictors of genu valgum in HME patients three months later, while GLB, PA, and UA are independent predictors of ankle valgus. The interpretable RF model and constructed nomogram in this study enable clinicians to assess the risk of lower limb deformities in HME patients as early as possible, benefiting more patients.