OBJECTIVES: Real-world data (RWD) are recognized to complement findings from clinical trials. However, poor quality and missingness often constitute limitations related to the use of RWD and particularly in orthopedic research. This study aimed to assess the risk factors of rotator cuff surgery failure including orthopedic data not usually available in common real-world databases.

METHODS: A monocentric retrospective study was conducted among 2720 patients who underwent rotator cuff surgery at the Nice Hospital from 2008 to 2021. Data were collected through Ortho+ software, already structured and ready for use, facilitating a complete descriptive analysis of the population. A Kaplan-Meier analysis was built to estimate the survival probability and Cox proportional-hazards regression models were performed to identify potential risk factors associated with surgery failure. Models’ covariates included both cardiovascular and medical history data (osteoporosis, arthritis, fat infiltration, dominant side, type of onset, work accident and amyotrophy).

RESULTS: Among all patients, 49 (1.8%) experienced failures of their initial surgery. The Kaplan-Meier analysis for time to reintervention shows a high survival probability, remaining above 0.95 over a 24-month period. Cox models demonstrated significant associations between the preoperative work accident status and the risk of failure (HR= 0.00; p=0.042). However, sex and cardiovascular-related covariates were not significantly associated with orthopedic event risk.

CONCLUSIONS: Collected RWD from the Nice Hospital Ortho+ software offers potentials to assess orthopedic patient outcomes following rotator cuff surgery. Ortho+ includes patient-reported outcome measures such as satisfaction scores, quality of life, and assessment scores used by healthcare professionals. The high-quality of the Ortho+ database allowed to address common gaps associated with real-world evidence and may be enriched with the French National Health Data System through linkage. Overall, this study emphasizes the importance of data-driven research in orthopedic surgery, providing insights that can be applied to improve patient outcomes and treatment strategies.