ArticleDigital health
Machine learning prediction of extended length of stay following endoscopic decompression for lumbar spinal stenosis: A retrospective cohort study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aims of this study were to develop and validate interpretable ML models for extended length of stay (eLOS) prediction following endoscopic lumbar spinal stenosis (LSS) decompression, and identify modifiable risk factors influencing healthcare costs and recovery. Methods: A prospective-retrospective cohort of 350 patients (2019-2025) undergoing single-level endoscopic decompression was analyzed. The eLOS was defined as >9 days via classification and regression tree (CART) analysis. Predictors included demographics (age, BMI), comorbidities (osteoporosis, hypertension), surgical parameters, and hospitalization costs. Seven ML models (XGBoost, Lasso Regression, CNN, etc.) were trained using stratified 70:30 splits, SMOTE balancing, and Bayesian hyperparameter tuning. Model performance was evaluated via AUC-ROC, F1-score, and SHAP interpretability. Results: The eLOS group ( Conclusion: This study establishes the first ML-driven framework for eLOS prediction in endoscopic LSS surgery, emphasizing age-related comorbidities over procedural factors. The integration of economic and clinical data enables actionable risk mitigation, supporting value-based care initiatives. Future multicenter studies should validate these models across diverse healthcare systems.
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