There is little overall evidence behind clinical practice guidelines for diagnosis and treatment of rotator cuff repair. The purpose of this study was to compare the performance of different machine learning models that use pre-operative data from an international and multicentric database to predict if a patient that underwent rotator cuff repair could achieve the minimal important change (MIC) for single assessment numeric evaluation (SANE) at one year follow-up.

Inclusion Criteria:

• Primarily treated for rotator cuff tears by partial or complete surgical repair with a planned arthroscopic procedure
• Reparable tears
• No language barrier hindering questionnaire completion or legal incompetence were not included

Exclusion Criteria:

• missing pre- or post-operative single-assessment numeric evaluation (SANE)

All adult patients who were primarily treated for rotator cuff tears by partial or complete surgical repair with a planned arthroscopic procedure were included in study.