Update of a prediction model for postoperative shoulder stiffness after arthroscopic rotator cuff repair

Abstract

Background Arthroscopic rotator cuff repair (ARCR) is a common procedure, and postoperative shoulder stiffness (POSS) is one of its most frequent adverse events, potentially necessitating individualized therapy. Our objectives were to update and internally validate a model predicting the occurrence of POSS for patients undergoing an ARCR. Methods We prospectively enrolled 973 patients undergoing primary ARCR included in the ARCR_Pred dataset. A two-round Delphi survey with 53 surgeons established a consensus definition of POSS within 6 months postoperatively and a ranking of candidate prognostic factors. Treating surgeons estimated POSS risk immediately after surgery. We externally validated an existing POSS model and developed updated multivariable logistic regression models using complete-case and multiple imputed datasets. Results We achieved a high consensus (88%) on the POSS definition among 44 responding shoulder surgeons, who also ranked the prognostic relevance of 71 factors for the prediction of POSS. The newly developed ARCR_Pred-POSS included 7 factors (age, acromiohumeral distance, symptom duration, baseline external rotation, active baseline abduction, baseline Oxford Shoulder Score, and surgery duration) and demonstrated superior discrimination (AUC = 0.735) and calibration (slope = 1.022) compared to the original POSS model (AUC = 0.581, slope = 0.508). Surgeons tended to overestimate the risk of POSS in their patients (AUC = 0.563, slope = 1.241). Conclusions These findings support the continued development of prediction models and provide valuable outputs for optimizing surgical timing, indications, and personalized rehabilitation. Plain language summary One of the most common shoulder surgeries is called arthroscopic rotator cuff repair. It helps many people recover from shoulder injuries and improves shoulder function. However, about 1 in 10 patients may experience shoulder stiffness after the surgery, which can make recovery more difficult. This study looked at ways to predict which patients are more likely to have this problem. By using data from past patients, researchers created a tool that helps doctors identify individuals at higher risk. This tool can guide decisions about when to perform surgery, who might benefit most, and how to personalize recovery plans. The results showed that these prediction tools are reliable and can help doctors make better clinical decisions, ultimately leading to improved outcomes for patients.