An Explainable Prediction Model for Outcomes After Anterior Subcutaneous Ulnar Nerve Transposition in Idiopathic Cubital Tunnel Syndrome.

PURPOSE: To identify preoperative clinical and electrodiagnostic predictors of outcome after anterior subcutaneous ulnar nerve transposition for idiopathic cubital tunnel syndrome and to develop an explainable model for individualized prognosis. METHODS: This retrospective cohort study included 119 patients who underwent anterior subcutaneous ulnar nerve transposition for idiopathic cubital tunnel syndrome between 2005 and 2022 with at least 1 year of follow-up. Candidate predictors included demographic factors, occupational category score, elbow carrying angle, modified McGowan grade, and electrodiagnostic parameters. Outcomes were graded using Messina's criteria and dichotomized as excellent/good versus fair/poor for prediction modeling. Conventional regression and six machine-learning algorithms were evaluated, with the machine-learning models internally validated using nested stratified 5-fold cross-validation repeated 50 times. Model performance was assessed using the area under the receiver operating characteristic curve, F1 score, and Brier score. Interpretation relied on feature importance and local interpretable model-agnostic explanations. RESULTS: Sixty-eight patients (57.1%) had an excellent/good outcome, whereas 51 patients (42.9%) had a fair/poor outcome. The extremely randomized trees model achieved the highest mean area under the receiver operating characteristic curve and the lowest Brier score and was selected for model interpretation. Wrist-level compound muscle action potential amplitude and modified McGowan grade were the dominant prognostic features. Exploratory model interpretation supported wrist-level compound muscle action potential amplitude ≥6.6 mV and modified McGowan grade ≤IIA as reference values for an excellent/good outcome. CONCLUSIONS: Wrist-level compound muscle action potential amplitude and preoperative clinical severity were the key predictors of recovery after anterior subcutaneous transposition for idiopathic cubital tunnel syndrome. Among the evaluated algorithms, the extremely randomized trees model achieved the highest mean area under the receiver operating characteristic curve and the lowest mean Brier score, reinforcing the prognostic importance of these variables. Explainable prediction modeling may support individualized preoperative counseling. TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic III.