Correlation between full-thickness degenerative supraspinatus tear and radiographic parameters including the acromiohumeral centre edge angle and the greater tuberosity angle

Gao

et al. 2022

Preprint

ObjectivesTo explore whether the combined utilization of two predictors is more efficient to predict and diagnose rotator cuff tears (RCTs) than using a single predictor, and to find out which combination is the best screening approach for RCTs.MethodsThis was a retrospective study and patients who visited our hospital and were diagnosed with or without 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 to find significant differences between groups. Binary logistic regression analysis was used to distinguish independent risk factors and calculate the increased odds of having a RCT, and construct discriminating combined models to further diagnose and predict RCTs. Receiver operating characteristic (ROC) curves were pictured and areas under the curve (AUC) were calculated to determine the overall diagnostic performance of the involved predictors and the combined models. For all tests a p value of < 0.05 was considered statistically significant.ResultsOne 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 = 0.000), AI (0.69 ± 0.08 versus 0.63 ± 0.08, P = 0.000), DRR (1.43 ± 0.10 versus 1.31 ± 0.08, P = 0.000) and GTA (70.15 ± 7.38 versus 64.75 ± 7.91°, P = 0.000) were significantly larger 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, improving the AUC from 0.668–0.823 to 0.751–0.883, and extending the upper boundary of diagnostic sensitivity from 71.9–81.3%, and the diagnostic specificity from 82.5–86.0%. Via logistic regression analysis, we found the values of both predictors over their cutoff values resulted in a dramatical and enormous increasement (20.169–161.214 folds) in the risk of having a RCT, which is far beyond that by using a single predictor only (2.815–11.191 folds).ConclusionThe combined utilization of predictors is a better approach to diagnose and predict RCTs than using a single predictor. With a comprehensive consideration on the diagnostic and predictive performance of the combined models, we conclude the CSA together with the DRR present the strongest detectability for a presence of RCTs.

Section: Comparison Of Predictors Between Groupssupporting

confidence: 91%

Section: Predictive Abilitymentioning

confidence: 45%

The combined utilization of predictors: a better approach to diagnose and predict rotator cuff tears than using a single predictor

Gao

et al. 2022

Preprint

“…[26][27][28][29][30] This evidence pointed out that surgical treatment such as subacromial decompression and acromioplasty may not be so effective in treating RCTs, and the intrinsic factors might be the main reason of RCTs. However, we should not deny the significant differences found in anatomic structures between the RCT group and the control group, for example, the differences in the acromion index (AI), the CSA and the GTA, which have been reported in many literatures 10,15,16,[31][32][33] and support the mechanism of extrinsic factors. In our perspective, because the DRR was found significantly larger in shoulders with RCTs, we acknowledge the extrinsic factor as a contributor to RCTs.…”

Section: Clinical Recommendationmentioning

confidence: 98%

The Double‐Circle System in the Greater Tuberosity: Using Radius to Predict Rotator Cuff Tear

Liu

et al. 2022

Orthopaedic Surgery

Objective: In this study we concerned on the morphological characteristics of the greater tuberosity of humerus and proposed the double-circle radius ratio as a new predictor for the diagnosis of rotator cuff tears. Methods:This was a retrospective study and patients who visited our hospital and were diagnosed with or without rotator cuff tears via magnetic resonance imaging from January 2018 to July 2021 were enrolled and classified into two groups respectively. In a standard anteroposterior view, the radius of the best-fit circle of humeral head and the radius of the concentric circle passing through the most lateral edge of the greater tuberosity were measured in each shoulder. The ratio of these two radiuses was named as the double-circle radius ratio. Angular parameters including the greater tuberosity angle and the critical shoulder angle were also measured in the anteroposterior view. Independent samples t tests and chi-square tests were used to find significant differences between groups. Significant associations between those measured variables and demographic characteristics were analyzed with simple linear regression analysis. Receiver operating characteristic curves were pictured to determine applied cutoff values by using Youden index. Multivariable-adjusted analysis for the occurrence of rotator cuff tears was carried out by using multiple logistic regression analysis. For all tests a p value of <0.05 was considered statistically significant.Results: One hundred and twelve shoulders with rotator cuff tears and 42 shoulders without rotator cuff tears were included. The mean value of the double-circle radius ratio was significantly larger in shoulders with rotator cuff tears (1.42 AE 0.09 vs. 1.30 AE 0.07, P = 0.000). With simple linear regression analysis, the radiuses of the humeral head and the greater tuberosity were significantly associated with heights and weights. In receiver operating characteristic curves, the largest area was found under the curve of the double-circle radius ratio as 0.846 (95% CI, 0.781-0.911; P = 0.000) with an applied cutoff value as 1.38 (sensitivity, 70.5%; specificity, 88.1%). Multivariable-adjusted analysis showed that a value of the double-circle radius ratio >1.38 resulted in 11.252-fold odds of developing rotator cuff tears (95% CI, 3.388-37.368; P = 0.000). Conclusion:The double-circle radius ratio is significantly larger in patients with rotator cuff tears and could be regarded as an eligible predictor for rotator cuff tears.

“…In Pandey et al’s research, they found a significant difference in the value of AI between the full-thickness RCT group and the control group, but the stepwise logistic regression rejected the AI as a predictor for the occurrence of a RCT [ 13 ]. A similar dilemma was observed in the GTA, as it significantly differed between groups but failed to be eligible for a predictor of full-thickness degenerative supraspinatus tear [ 18 ]. In our study, the AI and the GTA were both qualified as independent risk factors, but their predictive abilities were a little weaker, only increasing the odd of having a RCT by approximately three folds.…”

Section: Discussionmentioning

confidence: 88%

“…Unlike CSA and AI, the impact of the superolateral extension of the greater tuberosity on the development of RCT was just recognized in recent years. A classical predictor, the GTA, was created in 2018 and a mean value of > 70—72° significantly associated with developing a RCT [ 5 , 18 ]. In our study, the mean values of GTA were 70.15° in the RCT group and 64.75° for the control, comparable to the previous findings.…”

Section: Discussionmentioning

confidence: 99%

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

BMC Musculoskelet Disord

Guo

et al. 2022

Background 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. Methods 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. Results 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). Conclusion 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.