Patients
This study was approved by our institutional review board (2021BJYYEC-225-01). The requirement for patient consent was waived by the review board because of the retrospective nature of the study. We retrospectively reviewed patients with shoulder arthroscopic surgery between January 2017, to December 2020. The inclusion criteria were patients who underwent shoulder arthroscopic procedures with preoperative shoulder MRI. Participants combined with fractures, tumors, immunologically related diseases, rheumatic immune diseases, and revision surgery of the shoulder were excluded.
Characteristics of patients included age, gender, cause of injury, injury side (i.e., left or right side). A total of 460 patients with 150 males and 310 females enrolled in the study. According to the Lafosse classification, we evaluated the severity of subscapularis tendon tears under arthroscopy[16]. Patients with arthroscopically determined subscapularis tendon tears were included in the SSC tear group, while others with intact subscapularis tendon were enrolled in the non-SSC tear group. Ultimately, 184 patients and 276 patients were included in the SSC tear group and non-SSC tear group respectively. The mean age of both groups was 62.55 ± 9.03 and 59.76 ± 9.42 years, respectively. Eighty-eight (47.8%) patients in the SSC tear group underwent arthroscopic surgery for trauma, compared to 139 (50.4%) in non-SSC tear group. A detailed description of characteristics was presented in table 1.
Imaging characteristics
All patients enrolled in the study received the identical imaging protocol. T1-T2-weighted and fat-suppressed T2-weighted images were performed underwent 3.0-T MRI with the arm in a neutral position. According to previous reports[14, 17-21], clinical experience, and clinical importance, seventeen imaging features were measured for further evaluation by two trained executors. The consensus was reached after deliberation and the average value of variables was obtained with multiple measurements.
Coracohumeral distance (CHD) was measured from the humeral cortex to the coracoid process cortex[17]. According to different measurement planes, we evaluated CHD on the oblique sagittal plane and axial plane, respectively (Figure.1). The coracoid overlap (CO) was defined as the distance between the glenoid and the tip of the coracoid process, which was measured on the axial plane[17] (Figure. 1). According to the previous study[18], the relative ratio of the coracoid length and humeral head diameter measured on the axial plane was defined as coracohumeral index (CHI) (Figure.1). To evaluate subscapularis tendon tears, Shim et al introduced two selected oblique sagittal planes (the en-face and Y-face)[20]. The en-face plane was the image in which the glenoid was the largest observed and the base of the coracoid process was in contact with the glenoid, and the Y-view was the first image medial to the glenoid where the scapular spine was in contact with the scapular body (Figure. 2). We evaluated subscapular muscle atrophy and fluid accumulation on these two planes (Figure 2). In the en-face and the Y-face, subscapular muscle atrophy was classified as grades I, II, and III according to the degree of atrophy. In en-face, the atrophy of subscapularis was evaluated according to the base-to-tip line (BTL) introduced by Shim et al[20]. In Y -face, the atrophy of subscapularis was graded based on the tangent line and its parallel line. A detailed description of classification in subscapular muscle atrophy was presented in figure 2. To further evaluate fluid accumulation on en-face and Y-face, we introduced a new index, namely fluid area ratio. According to the base-to-tip line (BTL), the fluid area ratio was defined as the ratio of the effusion area to the area surrounded by the coracoid process, glenoid, and BTL in the en-face (Figure.2). To facilitate the measurement of the fluid area ratio (en-face) on MRI, this indicator was graded as ratio>0.5 and ratio<0.5.
The posterosuperior (PS, supraspinatus, infraspinatus, teres minor) rotator cuff is anatomically adjacent to the subscapularis tendon. The retraction and severity of PS rotator cuff tears may affect the development and progression of subscapularis tendon lesions. To further investigate their relationship, the retraction of PS rotator cuff tears was classified according to the Patte classification (grade I~III)[20, 22]. Furthermore, the severity of PS rotator cuff tears was classified on the basis of tearing thickness. Similarly, when a subscapularis tendon tear occurs due to the proximity of the anatomy, it is sometimes accompanied by a lesion of long head of the biceps (LHB)[14]. The malposition of LHB might be associated with subscapularis tendon tears. We selected LHB subluxation/dislocation as a potential risk factor with evaluation on the axial plane of MRI[23](Figure. 1). Turkmen et al suggested that the prevalence of subscapularis tendon tears was higher in patients with subcoracoid cyst[22] (Figure.1). In this study, the subcoracoid cyst was measured on axial plane with fat-suppressed T2-weighted MRI. Mostly, the greater tubercle cysts were considered to be associated with supraspinatus tenon tears[24, 25], clinicians speculated that the lesser tuberosity cysts (LTC) maybe be related to subscapularis tendon tears[15, 21]. We evaluated the presence of lesser tuberosity cysts and measured their maximum diameter on the fat-suppressed T2-weighted axial plane.
Statistical Analysis
Categorical variables were described as the whole number, and continuous variables were expressed as means ± standard deviation. The significance of each variable in the cohort was assessed by univariable logistic regression analysis firstly. Variables significantly associated with subscapularis tendon tears were further assessed in multivariable logistic regression (forward stepwise likelihood ratio method) to select the final independent risk factors.
Continuous variables (ie, age and CHD) were directly analyzed in the logistic regression, while for ordered categorical variables, we converted them into dichotomous variables with optimal scale regression before entering logistic regression. P-value < 0.05 was considered to be statistically significant.
According to the results of multivariable logistic regression, the rms package of R, version 4.0 (http://www.r-project.org/) was used to establish a nomogram. The nomogram was based on proportionally converting each regression coefficient in multivariate logistic regression to a 0- to 100-point scale. The total points were derived from the sum of the points for each independent variable and converted into predicted probability. The predictive performance of the nomogram is evaluated by concordance index (C index), decision curve analysis (DCA), and calibration with 1000 bootstrap samples. The performance of the predictive model was evaluated by the sensitivity, specificity, predictive values, and likelihood ratios.