ArticleJSES international2026
Pre-operative predictors for conjoined tendon impingement following reverse total shoulder arthroplasty.
Article in JSES international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Background: Reverse total shoulder arthroplasty (rTSA) reliably relieves pain and restores function. Yet anterior shoulder pain persists in some patients, with its underlying cause remaining unclear. One proposed mechanism is altered anterior anatomy leading to impingement of the conjoined tendon. This exploratory study investigates potential anatomical and surgical planning predictors of conjoined tendon impingement following rTSA. Methods: Nine rTSA patients with intraoperatively confirmed conjoined tendon impingement against the lesser tuberosity were matched to 9 asymptomatic controls by sex, operative side, and age. Forty anatomical and planned surgical parameters were derived from pre-operative computed tomography-based 3-dimensional planning. Parameters with Results: The optimal model identified 3 potential predictors of impingement: greater retroversion of the planned humeral stem relative to native, greater anteversion of the planned glenoid baseplate, and a more inferior coracoid position relative to the glenoid center. Validation showed 79.0% overall prediction accuracy. Predicted impingement likelihood was 76.1 ± 30.9% in symptomatic patients vs. and 22.2 ± 16.2% in asymptomatic patients ( Conclusion: Anatomical and surgical planning parameters can increase the risk of conjoined tendon impingement through mechanisms proposed to include altered humeral rotation, tuberosity positioning, and reduced joint distalization. These results provide early insight into potential predictors of impingement, offering a foundation for future research. A model based on anatomical and surgical planning parameters demonstrated feasibility for identifying intraoperatively observed conjoined tendon impingement, achieving a classification accuracy of 79%. Integrating a model derived from these findings into pre-operative planning may help inform implant selection and positioning, with the potential to reduce the risk of post-operative anterior shoulder pain and improve functional outcomes.
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