Aug 2026· Frontiers in Medicine· Vol 13· 0 citations· 40 references
Medicine
TL;DR
To develop and validate machine learning models and an individualized nomogram for predicting pedicle screw loosening after posterior lumbar interbody fusion in osteoporotic patients using preoperative clinical, imaging, and bone metabolism-related medication profiles, these complementary tools offer a potentially useful framework for preoperative risk stratification and personalized management of osteoporotic patients undergoing PLIF.
Abstract
Objective To develop and validate machine learning models and an individualized nomogram for predicting pedicle screw loosening after posterior lumbar interbody fusion (PLIF) in osteoporotic patients using preoperative clinical, imaging, and bone metabolism-related medication profiles. Methods A retrospective analysis was conducted on 630 osteoporotic patients who underwent PLIF at three spine surgery centers. Patients were divided into a non-loosening group (n = 450) and a loosening group (n = 180) according to the presence of implant loosening on imaging within 12 months postoperatively. Univariate analysis was used to screen candidate variables, and LASSO–logistic regression with 10-fold cross–validation was applied to extract independent predictors. Four models (naïve Bayes, logistic regression, linear discriminant analysis, and decision tree) were constructed based on the selected features and evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). The structure of the optimal model (decision tree) was visualized, and a nomogram was built using multivariable logistic regression. Results Univariate analysis showed significant differences between the two groups in age, bone mineral density T-score, pelvic incidence, lumbar lordosis, sex, hypertension, diabetes mellitus, foraminal morphology, history of glucocorticoid use, calcium supplementation, and vitamin D supplementation (all P < 0.05). LASSO regression identified 11 independent predictors (λ.min = 0.0325). Among the four models, the decision tree showed the highest discrimination, achieving an AUC of 0.975 (95% CI 0.961–0.989) in the training set and 0.971 (95% CI 0.955–0.987) in the test set, with good calibration (Hosmer-Lemeshow test P = 0.418). DCA demonstrated a significant net benefit across a clinically relevant threshold probability range (0–50%). The decision tree identified the bone mineral density T-score as the root splitting variable; a T-score < –3.6 classified patients as being at extremely high risk for loosening. Among those with T-score ≥ –3.6 who did not take calcium, lack of vitamin D supplementation was associated with a very high risk. Among those with T-score ≥ –3.6 who took calcium, female sex combined with lumbar lordosis ≥ 51° indicated an elevated risk. The nomogram integrated the 11 factors and yielded a C-index of 0.928 (95% CI 0.903–0.953) with good calibration (Hosmer-Lemeshow test P = 0.372). Conclusion The decision tree model demonstrated favorable predictive performance for pedicle screw loosening after PLIF. Its interpretable classification rules facilitate rapid screening of high-risk patients, while the nomogram enables individualized probability estimation for surgical planning. Together, these complementary tools offer a potentially useful framework for preoperative risk stratification and personalized management of osteoporotic patients undergoing PLIF
The random forest model demonstrated strong discriminatory ability and calibration in predicting fixation failure in this single-center retrospective cohort, and shows promise for perioperative risk stratification.
Purpose Postoperative cubitus varus is a disabling complication of pediatric lateral humeral condyle fractures that impairs long-term elbow function. This study aimed to develop and validate the first Improved LangEvin Equation-based Evolutionary (ILEE) automated machine learning (AutoML) model and visual decision support system for preoperative prediction of this complication, to enable personalized risk stratification and targeted intervention. Methods We conducted a retrospective cohort study of 330 children with lateral humeral condyle fractures treated between January 2005 and June 2022. An ILEE-optimized AutoML model was constructed and compared with the original LEE algorithm and 6 conventional machine learning models. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, and specificity. Key predictors were identified and interpreted via SHapley Additive exPlanations (SHAP) analysis, and a clinical decision support system was developed using MATLAB App Designer. Results The ILEE algorithm outperformed all comparators in optimization stability and convergence speed. The AutoML model identified five key features for predicting cubitus varus and exhibited better prediction calibration performance compared to other models (ROC-AUC = 0.9557). SHAP analysis ranked the importance of these features as follows: obesity degree, preoperative timing, internal fixation time, fracture type, and external fixation time. The decision support system can generate real-time risk levels, predicted probabilities, and personalized clinical recommendations within 1 s. Conclusion This study establishes the first ILEE-based AutoML model for predicting postoperative cubitus varus in pediatric lateral humeral condyle fractures, demonstrating competitive predictive performance. The user-friendly visual system enables rapid preoperative risk assessment, assisting clinicians in identify high-risk patients, optimize treatment plans, and potentially improving pediatric orthopedic outcomes.
Yunfeng Wan, Kunjie Deng, Yang Yuan et al.· Frontiers in Surgery· 0 citations
Post-fracture intercostal neuralgia in osteoporotic thoracic vertebrae is multifactorial, and middle thoracic vertebra fractures, injury of the thoracolumbar fascia, decreased bone density, intravertebral vacuum fissure, and depressive state are independent risk factors.
Dongliang Xiao, Yong-Guang Xu· International Journal of Gen...· 0 citations
RF-based models can accurately and equitably predict perioperative complications in diverse spine surgery contexts, supporting personalized counseling, targeted monitoring, and optimized resource allocation.
Andrea Campagner, Francesco Langella, P. Bellosta-López et al.· European spine journal· 0 citations
This study aimed to investigate the risk factors associated with 1-year postoperative mortality in patients with lumbar compression fractures and to construct and validate a predictive nomogram model. Clinical data of patients admitted between January 2021 and December 2024 were retrospectively analyzed. Independent predictors of 1-year mortality were identified using univariate and multivariate logistic regression analyses. A nomogram was constructed based on the final model. Model discrimination was evaluated using the receiver operating characteristic curve and the area under the curve. Calibration, Bootstrap resampling, and 10-fold cross-validation were used for internal validation. A total of 378 patients were included, of whom 21 (5.56%) died within 1 year postoperatively. Five independent predictors were identified: bone mineral density ≤ −2.5, multiple segmental fractures, age > 70 years, albumin ≤ 40 g/L, and neutrophil-to-lymphocyte ratio > 4. The nomogram showed good discriminative performance, with an area under the curve of 0.826 in the training cohort and 0.813 in the validation cohort. Calibration curves demonstrated good agreement between predicted and observed outcomes. One-year postoperative mortality in lumbar compression fracture patients is influenced by multiple clinical and inflammatory factors. The proposed nomogram demonstrates good discriminative ability and may help clinicians identify high-risk patients for early intervention.
Objective To investigate the clinical and radiographic factors associated with surgeon selection of the direct anterior approach (DAA) versus the posterolateral approach (PLA) in total hip arthroplasty (THA), and to explore whether machine learning methods can characterize historical surgical selection patterns. Methods A retrospective analysis was performed on 98 patients who underwent primary THA at two institutions, including 61 patients in the PLA group and 37 patients in the DAA group. Patient demographics and preoperative radiographic parameters were collected. Baseline comparisons and univariate logistic regression analyses were conducted using SPSS. A machine learning classification model reflecting historical surgeon-selected approaches was constructed using the XGBoost algorithm, and its performance was evaluated via receiver operating characteristic (ROC) curves, five-fold cross-validation, SHAP analysis, and calibration curves. Results Patients in the DAA group exhibited significantly higher age, soft tissue thickness, neck-shaft angle (NSA), and femoral offset compared to those in the PLA group (all P < 0.05). Univariate logistic regression revealed that age, NSA, and femoral offset were significantly associated with DAA selection. The XGBoost model achieved an area under the curve (AUC) of 0.938 and an accuracy of 90.0% on the test set, with a mean AUC of 0.897 via five-fold cross-validation. SHAP analysis identified osteoporosis, Dorr classification, age, and NSA as key contributors to model predictions. The model exhibited good calibration, as indicated by a Brier score of 0.0898. Conclusion The present study identified several clinical and radiographic factors associated with historical surgeon selection of DAA versus PLA in THA. The machine learning model demonstrated the ability to characterize real-world surgical selection patterns, although it should not be interpreted as recommending the optimal surgical approach for individual patients.
Meng Li, Yuanye Ge, Dalin Wang et al.· Frontiers in Surgery· 0 citations
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