The combined utilization of predictors seems more suitable to diagnose and predict rotator cuff tears

Morphological markers presenting the lateral extension of acromion and the greater tuberosity of humerus were proposed to diagnose and predict rotator cuff tears (RCTs) in recent years, but few studies have addressed the combined performance when using two predictors together. As a presence of a RCT may be associated with the impingement caused by both acromion and the greater tuberosity, we believe a combined utilization of predictors could result in a better diagnostic and predictive performance than using a single predictor. The aim of this study is to (i) explore whether the combination is more efficient to predict and diagnose RCTs; (ii) find out which combination is the most superior screening approach for RCTs.

This was a retrospective study and patients who visited our hospital and were diagnosed with or without partial-thickness or full-thickness RCTs via magnetic resonance imaging from January 2018 to April 2022 were enrolled and classified into two groups respectively. Four predictors, the critical shoulder angle (CSA), the acromion index (AI), the greater tuberosity angle (GTA) and the double-circle radius ratio (DRR) were picked to participate in the present study. Quantitative variables were compared by independent samples t tests and qualitative variables were compared by chi-square tests. Binary logistic regression analysis was used to construct discriminating combined models to further diagnose and predict RCTs. Receiver operating characteristic (ROC) curves were pictured to determine the overall diagnostic performance of the involved predictors and the combined models.

One hundred and thirty-nine shoulders with RCTs and 57 shoulders without RCTs were included. The mean values of CSA (35.36 ± 4.57 versus 31.41 ± 4.09°, P < 0.001), AI (0.69 ± 0.08 versus 0.63 ± 0.08, P < 0.001), DRR (1.43 ± 0.10 versus 1.31 ± 0.08, P < 0.001) and GTA (70.15 ± 7.38 versus 64.75 ± 7.91°, P < 0.001) were significantly higher in the RCT group than for controls. Via ROC curves, we found the combined model always showed a better diagnostic performance than either of its contributors. Via logistic regression analysis, we found the values of both predictors over their cutoff values resulted in an increasement (20.169-161.214 folds) in the risk of having a RCT, which is more than that by using a single predictor only (2.815 -11.191 folds).

The combined utilization of predictors is a better approach to diagnose and predict RCTs than using a single predictor, and CSA together with DRR present the strongest detectability for a presence of RCTs.