Accuracy of the Critical Shoulder Angle for Predicting Rotator Cuff Tears in Patients With Nontraumatic Shoulder Pain. Issue 5 (13th May 2020) - Lin, Che-Li; Chen, Yi-Wen; Lin, Li-Fong; Chen, Cho-Pang; Liou, Tsan-Hon; Huang, Shih-Wei
Background: The critical shoulder angle (CSA) is the angle between the superior and inferior bony margins of the glenoid and the most lateral border of the acromion. Although studies have reported that the CSA is associated with rotator cuff tears (RCTs), few studies have examined the accuracy of the CSA for predicting RCTs in patients with shoulder pain. Purpose: To investigate the accuracy of the CSA for predicting RCTs among patients with nontraumatic shoulder pain. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Data were retrospectively collected from 301 patients who had RCTs and underwent arthroscopic rotator cuff repair between January 2014 and December 2018 (RCT group). During that same period, we also included 300 patients with shoulder pain but without RCTs, confirmed through ultrasound (non-RCT group). Baseline demographic data, the CSA, and the acromion index (AI) were compared using an independent t test. Categorical variables were analyzed using the chi-square test. Receiver operating characteristic (ROC) curve analysis was performed to investigate the accuracy of the CSA and AI for predicting RCTs, and the optimal cutoff point was determined using the Youden index. Multiple stepwise and binary logistic regressions were used to determine the predictors of RCTs. Results: A total of 301 patients (123 males, 178 females) and 300 patients (116 males, 184 females) were included in the RCT and non-RCT groups, respectively. The RCT group had a higher CSA ( P < .001) than the non-RCT group. The area under the ROC curve (AUC) was 70.5% ( P < .001) for the CSA, but there was no significance for the AI, with an AUC of 47.7% for predicting RCTs in patients. Stepwise logistic regression revealed the CSA as an independent predictor of RCTs, with an adjusted odds ratio of 1.295 (95% CI, 1.019-1.571; P = .006). For patients with a CSA greater than 37.52°, binary logistic regression revealed an adjusted odds ratio of 3.92 (95% CI, 2.79-5.51; P < .001) for the presence of an RCT. Conclusion: The CSA was an objective assessment tool to identify patients with shoulder pain who may have RCTs. Our study indicated that the CSA predicted RCTs more accurately than did the AI for patients with shoulder pain.