Intraoperative performance and influencing factors in computer-assisted total shoulder arthroplasty: a large-scale analysis.
Academic Article
Overview
abstract
The adoption of guidance technologies in total joint arthroplasty has expanded greatly in recent decades. This study aims to retrospectively evaluate the intra-operative performance of computer-assisted navigation (CAN) total shoulder arthroplasty (TSA) over years, analyzing temporal performance evolution and influencing factors. Over 40,000 cases utilizing a single CAN system (Exactech GPS) across an 8-year span were analyzed. We investigated the registration and navigation phases for anatomic (aTSA) and reverse (rTSA) procedures, focusing on factors influencing performance and temporal evolution. Data were stratified by surgeon volume, native anatomy, implant type, and procedural workflow. Key findings include: (1) Navigation times were longer for rTSA (mean 15:42 min) compared to aTSA (mean 10:01 min). (2) Glenoid retroversion > 20° and inferior inclination increased both registration and navigation durations for aTSA and rTSA. (3) Augmented implants extended navigation time for aTSA but not rTSA. (4) Surgeon experience significantly influenced performance; high-volume surgeons demonstrated markedly faster times, with a mean navigation time reduction of up to 7:16 min (aTSA) and 13:52 min (rTSA) compared to low-volume counterparts. (5) Registration time decreased over the years for all surgeon profiles, indicating system learning curves and increasing user familiarity. Additionally, repeated registration prolonged both registration and navigation phases, suggesting an impact on operator confidence or tracker visibility issues. Navigation performance trends over the years showed initial increases for aTSA due to added navigation steps but subsequent reductions for high- and medium-volume surgeons, underscoring the evolving efficiency of system use. The study highlights key factors influencing CAN efficiency and supports its potential to improve surgical accuracy and reduce variability, even among low-volume surgeons. This large-scale analysis underscores the utility of CAN in optimizing TSA workflows while identifying areas for potential improvement, particularly for complex anatomical cases.
