Editorial commentary: predictors for rotator cuff re-tear : are there any crystal balls?
Rotator cuff tears are a common cause of shoulder pain and dysfunction, affecting up to 33% of the population, and approximately 250,000 arthroscopic rotator cuff repairs are performed each year in the United States. Arthroscopic rotator cuff repair is the gold standard for surgical management of fu...
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| Main Authors: | , , |
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| Format: | Article (Journal) Editorial |
| Language: | English |
| Published: |
[9 May 2025]
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| In: |
Arthroscopy
Year: 2025, Volume: 41, Issue: 9, Pages: 3523-3525 |
| ISSN: | 1526-3231 |
| DOI: | 10.1016/j.arthro.2025.04.057 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.arthro.2025.04.057 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0749806325003548 
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| Author Notes: | Tyler J. Uppstrom, Ayham Jaber, Peter J. Millett |
MARC
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| 520 | |a Rotator cuff tears are a common cause of shoulder pain and dysfunction, affecting up to 33% of the population, and approximately 250,000 arthroscopic rotator cuff repairs are performed each year in the United States. Arthroscopic rotator cuff repair is the gold standard for surgical management of full-thickness rotator cuff tears and is associated with postoperative improvements in pain and function. However, reported retear rates based on postoperative magnetic resonance imaging vary from 7% to 90% following arthroscopic rotator cuff repair. Despite variations in repair techniques, implant technology, biologic and patch augmentation, and postoperative rehabilitation algorithms, retear rates following rotator cuff repair have remained high over the past several decades. As a result, there remains significant interest in identifying predictive factors of retear after rotator cuff repair. That said, numerous well-designed studies have demonstrated a poor correlation between postoperative magnetic resonance imaging findings and functional outcomes. Regardless, intraoperative evaluation of tendon quality, tear pattern, and tissue mobility is essential to predicting the likelihood of successful rotator cuff repair, although at the current time, this evaluation is largely subjective, and few validated assessment tools exist. As such, future objective research is required to improve our assessment and documentation of these intraoperative factors, with artificial intelligence and machine learning models possibly serving as useful tools for identifying meaningful intraoperative patterns predictive of postoperative outcomes in the future. Perhaps we will have more tools like this that will serve as “crystal balls” to help us better predict outcomes for our patients. | ||
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