The learning curve for anatomic and reverse total shoulder arthroplasty: a systematic review.

Background: Despite the rising incidence of anatomic total shoulder arthroplasty (ATSA) and reverse total shoulder arthroplasty (RTSA) among surgeons, little is known about the learning curve associated with these procedures. The purpose of this systematic review was to (1) identify the learning curves associated with ATSA and RTSA, (2) evaluate the effect of the learning curves on clinical outcomes, and (3) determine the number of cases needed to achieve proficiency. Methods: Four online databases [PubMed (NLM), MEDLINE (OVID), Cochrane Library (Wiley), and Scopus (Elsevier)] were systematically searched and screened according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. The search included results from the inception of each database to May 18, 2022. Data regarding study characteristics, patient demographics, learning curve analyses, patient reported outcome measures, range of motion, complication rates, and reoperation rates were collected. A quality assessment for each article was performed according to the Methodological Index for Nonrandomized Studies criteria. Results: A total of 13 studies of fair to good quality were included for analysis (one of level II evidence, five of level III, and seven of level IV) with the majority originating from the United States [n = 8, 61.5%]. Overall, there were a total of 3381 cases (1861 RTSA and 1520 ATSA), with a mean patient age of 72.6 years [range: 45-92 years]. From the studies analyzed in this systematic review, for RTSA, the approximate average number of cases surgeons need to perform to move to an acceptable position on the RTSA learning curve is 25 cases. For ATSA, a wider range of 16-86 cases was derived as only two studies reported on ATSA. Conclusion: Progression along the learning curve for RTSA and ATSA results in decreased operative times, improved patient-reported outcomes, and fewer complications. However, a true learning curve is difficult to quantify given the heterogeneity of reported outcome measures, individual surgeon experience at the time of data collection, and statistical analyses used across studies.