Jul 2026· European spine journal· 0 citations· 12 references
Medicine
TL;DR
RF-based models can accurately and equitably predict perioperative complications in diverse spine surgery contexts, supporting personalized counseling, targeted monitoring, and optimized resource allocation.
An interpretable gradient-boosting model may support risk-stratified perioperative assessment for elderly patients undergoing abdominal surgery and Prospective multicenter validation is required before routine clinical implementation.
Qiang Zhong, Guiming Huang, Wen Zhou et al.· Frontiers in Surgery· 0 citations
The machine learning model developed in this study demonstrates strong capability in stratifying anesthetic risk for patients with lumbar spinal stenosis, providing valuable reference for selecting surgical and anesthetic approaches.
Jitao Yang, Yixi Wang, Qihao Chen et al.· Frontiers in Medicine· 0 citations
This interpretable 11-variable model enables early POP risk stratification after brain tumor surgery and may support timely preventive intervention in neurosurgical care.
H. Mao, Fengchun Mu, Xinyu Wang et al.· Frontiers in Cellular and In...· 0 citations
This study aimed to develop and validate an interpretable machine learning model incorporating clinical and radiological features to predict the risk of adverse outcomes in patients with lumbar disc herniation (LDH) following unilateral biportal endoscopic (UBE) surgery. The model was designed to facilitate preoperative risk stratification and assist individualized clinical decision-making.
A retrospective cohort study was conducted involving 418 patients with LDH who underwent UBE surgery at our institution between January 2022 and January 2025. Patients were randomly allocated to the training and validation cohorts in a 7:3 ratio. The collected variables comprised demographic characteristics, perioperative parameters, and radiological features. Four complementary feature selection approaches, including the least absolute shrinkage and selection operator (LASSO), Boruta, minimum redundancy maximum relevance (mRMR), and recursive feature elimination (RFE), were applied. Six machine learning algorithms were developed and optimized using five-fold cross-validation within the training cohort. The predictive performance of each model was subsequently evaluated in the validation cohort, and the area under the receiver operating characteristic curve (AUC) was used to identify the optimal model. The best-performing model was further evaluated using multiple performance metrics. SHapley additive explanation (SHAP) analysis was performed to interpret model predictions and enhance transparency in the decision-making process. Finally, an online risk prediction calculator was developed based on the optimal model to facilitate clinical application.
Modic changes, Pfirrmann grade, APHC, and age were identified as important predictors of adverse outcomes following UBE surgery. Among the six evaluated machine learning models, the ExtraTrees model demonstrated the highest predictive performance. In the training cohort, the model achieved an AUC of 0.933 (95% CI, 0.901–0.960), with an accuracy, precision, sensitivity, and specificity of 0.850, 0.867, 0.703, and 0.934, respectively. In the validation cohort, the model maintained satisfactory performance, achieving an AUC of 0.884 (95% CI, 0.818–0.941), with an accuracy, precision, sensitivity, and specificity of 0.818, 0.811, 0.652, and 0.913, respectively, suggesting good generalizability. Decision curve analysis demonstrated that the ExtraTrees model achieved greater net clinical benefit than the other models across a wide range of threshold probabilities. SHAP-based global importance plots, dependence plots, and individual-level waterfall and force plots demonstrated that higher Modic grades, higher Pfirrmann grades, increased APHC values, and older age were associated with an increased risk of adverse outcomes. The direction and magnitude of these associations were largely consistent with clinical expectations, thereby enhancing model interpretability and clinical acceptance. Furthermore, a web-based calculator (
https://mtt123456.shinyapps.io/dynnomapp/
) was developed to facilitate the practical application of the prediction model.
The proposed prediction model demonstrated favorable predictive performance in the internal validation cohort and may serve as a practical tool for preoperative risk assessment of adverse outcomes following UBE surgery for LDH. This model may assist in identifying high-risk patients and provide evidence to support preoperative planning, patient counseling, and individualized treatment optimization. However, because validation was limited to a randomly divided single-center dataset, further external validation involving multicenter cohorts, different temporal periods, and surgeons with varying levels of experience is required to determine the model's robustness and generalizability.
Yue-Long Tan, Yu Xi, Nai-Yan Hu et al.· Frontiers in Surgery· 0 citations
Objective To construct and validate a risk prediction model of in-hospital mortality using machine learning (ML) algorithm in a retrospective cohort of acute type A aortic dissection (ATAAD) patients undergoing surgical treatment. Methods Patients with ATAAD undergoing surgical treatment between January 2014 and December 2022 were enrolled to predict in-hospital mortality. To address class imbalance and overfitting, we developed a robust Random Forest (RF)-based classification framework using a nested stratified 5-fold cross-validation (NCV). This was a single-center, retrospective study with internal validation only; no external validation was performed. Performance was evaluated via ROC-AUC, Precision-Recall Area Under the Curve (PR-AUC), sensitivity, brier score and calibration metrics, with Shapley Additive exPlanations (SHAP) utilized for feature interpretation. Results A total of 639 ATAAD patients were included in the analytical cohort, with an in-hospital mortality rate of 5.6% (36/639). The calibrated full RF model (50 preoperative clinical variables) achieved an ROC-AUC of 0.666, PR-AUC of 0.145, brier score of 0.051, and calibration slope of 0.836, with a sensitivity of 0.694 at an optimized threshold. A parsimonious 15-feature model maintained robust performance (ROC-AUC: 0.752, PR-AUC: 0.207, brier score: 0.050 and calibration slope: 0.934). SHAP analysis identified Creatine Kinase-MB, Myoglobin, and Fibrinogen Concentration as the top mortality predictors. Conclusion We developed and internally validated an explainable RF model to predict in-hospital mortality after ATAAD surgery. Given the low positive predictive value and high negative predictive value, the model is best regarded as a promising preliminary rule-out/triage tool that requires multicenter external validation before clinical use.
Ke-Yan Liu, Sili Shan, Hao-Long Zeng et al.· Frontiers in Medicine· 0 citations
BACKGROUND
Older adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admission-time risk stratification tools are lacking. We aimed to characterize HACs and develop both an ensemble prediction model and a simplified bedside risk score.
METHODS
This retrospective cohort study used the Japanese Diagnosis Procedure Combination database provided by JMDC Inc. Patients aged ≥ 65 years emergently admitted for tibial fracture (ICD-10: S82, 2014-2025) who underwent surgery within 5 days, with stay > 5 days, were included. The primary outcome was composite HACs during index hospitalization. Missing data were handled with multiple imputation. A Super Learner ensemble was developed and evaluated on held-out test data, and a simplified scorecard was derived using analysis of variance (ANOVA)-based feature selection and Weight-of-Evidence transformation.
RESULTS
Among 53,186 admissions, 5,193 met eligibility criteria. HACs occurred in 851 patients (16.4%), most commonly delirium (7.5%) and falls/trauma (5.7%). The Super Learner achieved a test-set area under the receiver operating characteristic curve (AUC) of 0.740 (95% CI 0.707-0.772), higher than conventional linear logistic regression (0.724). The most influential predictors were Hospital Frailty Risk Score, dementia, Barthel Index, comorbidity burden, and days to surgery. The simplified 7-variable scorecard achieved a test-set AUC of 0.736 (95% CI 0.703-0.769), stratifying patients into five risk groups (HAC rate: 3.45%-38.64%).
CONCLUSIONS
HAC risk after emergency tibial fracture surgery in older adults was driven primarily by geriatric vulnerability rather than fracture-specific factors. A simplified admission-time score may support targeted prevention, pending external validation.
Akio Shimizu, Ryota Sakamoto, H. Nagayama et al.· Aging Clinical and Experimen...· 0 citations
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