Purpose: To propose a modified Patte classification (evaluating tendon retraction on 2 coronal sections) and analyze whether this classification was better at predicting irreparability and retear of large to massive rotator cuff tears (RCTs).
Methods: A retrospective study was performed. Imaging evaluation including tendon retraction, fatty infiltration, the acromiohumeral distance (AHD), and the tangent sign was performed using magnetic resonance imaging. The modified Patte classification was used to assess tendon retraction. Intraobserver and interobserver reliability was analyzed by calculating intraclass correlation coefficients. Factors affecting irreparability and retear were analyzed using both univariate and multivariate analyses. Sensitivity and specificity of tendon retraction to predict irreparability and retear were calculated.
Results: A total of 121 shoulders with large to massive RCTs underwent arthroscopic rotator cuff repairs. The modified Patte classification system had excellent interobserver and intraobserver reliability. Several factors were associated with reparability and retear in the univariate analysis. However, in binary logistic regression analysis, the only factors affecting reparability were AHD less than 0.4 cm (P = .007) and modified Patte stage III tendon retraction (P = .023). Low-grade repair quality (P = .001) and modified Patte stage III tendon retraction (P = .031) were independent factors for retear. Modified Patte stage III had a high specificity for predicting irreparability (93.58%) and retear (98.78%), whereas the specificity of original Patte stage III was 76.15% and 84.15%, respectively.
Conclusions: For large to massive RCT repairs, modified Patte stage III tendon retraction with evaluation of 2 coronal cuts reveals higher specificity on predicting tendon irreparability and postoperative retear. An AHD less than 0.4 cm on magnetic resonance imaging and modified Patte stage III tendon retraction were independent risk factors for irreparability. Low-grade repair quality and modified Patte stage III tendon retraction were independent risk factors for postoperative retear.
Level Of Evidence: Level III, case-control study.
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http://dx.doi.org/10.1016/j.arthro.2020.06.023 | DOI Listing |
Rev Bras Ortop (Sao Paulo)
November 2024
Instituto de Ortopedia e Traumatologia/Hospital São Vicente de Paulo, Passo Fundo, RS, Brasil.
Rupture of the pectoralis major muscle is extremely rare in adolescents. The current literature contains only 5 reports of this condition in patients under 20 years old, with 2 reports in subjects under 16. In the present article, we report the case of a 15-year-old volleyball player who suffered a traumatic rupture of the pectoralis major in a match during the serve movement.
View Article and Find Full Text PDFAsia Pac J Sports Med Arthrosc Rehabil Technol
January 2025
Sports Medicine Institute of Fudan University, Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, PR China.
Purpose: To use a finite element method to construct a patch-bridge repair model for massive rotator cuff tears (MRCTs) and investigate the effects of different suture methods and knot numbers on postoperative biomechanics.
Methods: A finite element model based on intact glenohumeral joint data was used for a biomechanical study. A full-thickness defect and retraction model of the supraspinatus tendon simulated MRCTs.
JBJS Essent Surg Tech
December 2024
Department of Orthopedics, OhioHealth Health System, Columbus, Ohio.
Mol Syndromol
December 2024
Research team in genomics and molecular epidemiology of genetic diseases, Genomics Center of Human Pathologies, Faculty of Medicine and Pharmacy, University Mohammed V of Rabat, Rabat, Morocco.
J Shoulder Elbow Surg
December 2024
School for Biomedical and Precision Engineering, Personalised Medicine Research, University of Bern, Bern, Switzerland.
Background: Tear size and shape are known to prognosticate the efficacy of surgical rotator cuff (RC) repair however, current manual measurements on magnetic resonance images (MRI), exhibit high interobserver variabilities and exclude three-dimensional (3D) morphological information. This study aimed to develop algorithms for automatic 3D analyses of posterosuperior full-thickness RC tear to enable efficient and precise tear evaluation and 3D tear visualization.
Methods: - A deep-learning network for automatic segmentation of the tear region in coronal and sagittal multicenter MRI was trained with manually segmented (consensus of 3 experts) pd- and T2 weighted MRI of shoulders with full-thickness posterosuperior tears (n=200).
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