Development and validation of a model predicting patient-reported shoulder function after arthroscopic rotator cuff repair in a Swiss setting

Background . Prediction models for outcomes after orthopedic surgery provide patients with evidence-based post-operative outcome expectations. Our objectives were (1) to identify prognostic factors associated with the post-operative shoulder function outcome of the Oxford Shoulder Score (OSS) and (2) to develop and validate a prediction model for post-operative OSS. Methods. Patients undergoing arthroscopic rotator cuff repair (ARCR) were prospectively documented at a Swiss orthopedic tertiary care center. The first primary ARCR in adult patients with a partial or complete rotator cuff tear were included between October 2013 and June 2021. Twenty-two potential prognostic factors were used for prediction model development. Three sets of factors were compared in terms of overall performance (using Akaike’s Information Criterion and model bias) and discriminative ability (using R squared and Root Mean Squared Error). Results. A complete-case analysis of 1,310 patients was performed. Set 3 retained the following factors: age, sex, American Society of Anesthesiologists (ASA) classification, number of pre-operative steroid infiltrations, baseline OSS, baseline level of depression and anxiety, infraspinatus tear, tendon delamination and acromioclavicular joint resection. Linear regression and Tobit models had similar performance on both the training (R squared = 0.201 and 0.201, respectively) and test sets (R squared = 0.119 and 0.123, respectively). The Tobit model was slightly more consistent than the linear regression model after bootstrap validation (R squared = 0.189 compared to 0.161). Conclusion. A prediction model for patients undergoing ARCR was developed to provide patients and surgeons with individualized expectations for post-operative shoulder function outcomes.