ArticleOrthopaedic journal of sports medicine2025
Machine Learning Prediction of Poor Flexion-Extension Outcome After Open Elbow Arthrolysis: Identifying Individual Predisposition Using Clinical and Laboratory Indicators.
Article in Orthopaedic journal of sports medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Elbow stiffness is a condition that causes mobility dysfunction and severe suffering in patients. Patients with posttraumatic elbow stiffness who undergo open elbow arthrolysis are at risk for poor postoperative flexion-extension outcome, which leads us to suspect that these patients possess a unique "elbow stiffness predisposition." Purpose: To develop an innovative predictive model that combines clinical and laboratory indicators to forecast poor flexion-extension outcomes after open elbow arthrolysis, thereby interpreting the "elbow stiffness predisposition." Study Design: Cohort study; Level of evidence, 3. Methods: Patients who underwent open elbow arthrolysis between 2019 and 2022 were selected for model training and validation (n = 254), while those who underwent open elbow arthrolysis between 2016 and 2017 served as a test set (n = 35). The study assessed 19 clinical features and 58 laboratory parameters. A comparative analysis of several machine learning models-logistic regression, Naive Bayes, decision trees, random forest, gradient boosting, and XGBoost-was performed to identify the most effective approach. SHapley Additive exPlanations (SHAP) were employed to prioritize the key factors. Results: Using univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression, 14 key variables were selected for inclusion in the model. The XGBoost model demonstrated superior performance, reaching an area under the curve of 0.909 on the test dataset. The most important indicator identified by LASSO was alkaline phosphatase. Indicators ranked by SHAP were lipoprotein(a), alkaline phosphatase, visual analog scale score, serum calcium, basophil count, serum sodium, previous arthrolysis, alanine aminotransferase, blood glucose, preoperative elbow range of motion, Cystatin C, uric acid, tobacco use, and serum cholinesterase. Conclusion: We have successfully developed a machine learning model using 14 key indicators to predict poor flexion-extension outcomes, which preliminarily explains the "elbow stiffness predisposition."
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