Dataset related to article "Intraoperative Predictors of Postoperative Stiffness Requiring Manipulation Under Anesthesia After Robotic-Assisted Total Knee Arthroplasty: A Matched Case–Control Study"

This record contains raw data related to article "Intraoperative Predictors of Postoperative Stiffness Requiring Manipulation Under Anesthesia After Robotic-Assisted Total Knee Arthroplasty: A Matched Case–Control Study" Abstract Background: Postoperative stiffness after total knee arthroplasty (TKA) often necessitates manipulation under anesthesia (MUA). Robotic-assisted TKA (RA-TKA) improves alignment and balancing accuracy, but the impact of newer workflows with digital ligament tensioning on stiffness remains unclear. Methods: We conducted a retrospective matched case–control study of 2,241 consecutive RA-TKA procedures performed by a single surgeon (April 2020–December 2024). Seventy patients who required MUA were matched 1:1 with controls for age, sex, BMI, and ASA class. Intraoperative predictors of stiffness were analyzed using logistic regression. A secondary cohort analysis compared MUA incidence between procedures performed with the original MAKO workflow and the updated MAKO 2.0 platform. Results: Flexion contracture (4.3° vs 1.0°, p=0.006) and extension gap imbalance (–0.37 vs 0.10 mm, p=0.0018) were significantly greater in patients requiring MUA. Postoperative reductions in patellar height indices were greater among MUA patients but not independently predictive. Across the full cohort, MUA incidence was lower with MAKO 2.0 compared with the original workflow (1.6% vs 4.1%; OR 0.39, 95% CI 0.22–0.69; p=0.0008). Conclusion: Preoperative flexion contracture and extension gap imbalance independently predict stiffness requiring MUA after RA-TKA. Adoption of the MAKO 2.0 workflow was associated with a significantly reduced MUA incidence, supporting the role of precise balancing and controlled resections in minimizing postoperative stiffness.