Development and Validation of a Nomogram Model for Predicting Dislocation of the Long

Purpose This study aimed to explore the risk factors for dislocation of the long head of the biceps tendon (LHBT) and to establish and validate a nomogram prediction model. Methods A retrospective analysis was performed on the clinical data of 193 patients with rotator cuff tears who underwent MRI and CT examinations in the Department of Orthopaedics IV, The Third Hospital of Hebei Medical University, from November 2023 to November 2025. Preoperative morphological parameters of the bicipital groove, including width, length, medial wall angle, and total opening angle, were measured on CT. All data analyses were conducted using R software (version 4.5.2), with key statistical packages including glmnet (for Lasso regression analysis), rms (for nomogram construction), pROC (for ROC curve analysis), and caret (for logistic regression analysis) along with Zstats v1.0. The significance level was set at p < 0.05. To construct the LHBT dislocation prediction model and evaluate the correlation between clinical factors and postoperative recovery, multiple statistical methods were employed, including univariate analysis, Lasso regression, logistic regression, and calibration curve analysis. Results A nomogram prediction model was successfully constructed. Univariate analysis and Lasso regression identified gender, smoking history, medial wall angle of the bicipital groove, total opening angle, and width and length of the bicipital groove as key influencing factors for dislocation. Further logistic regression analysis confirmed that the medial wall angle of the bicipital groove was a significant independent predictor. Conclusions Gender, medial wall angle of the bicipital groove, total opening angle, width, and length of the bicipital groove are risk factors for LHBT dislocation, with gender and medial wall angle showing significant statistical relevance. The calibration curves for the training and validation sets confirmed that the nomogram model for predicting LHBT dislocation possesses good predictive performance and offers reference value in clinical practice.