Aims. Patient dissatisfaction following primary total knee arthroplasty (TKA) with manual jig-based instruments has been reported to be as high as 30%. Robotic-assisted total knee arthroplasty (RA-TKA) has been increasingly used in an effort to improve patient outcomes, however there is a paucity of literature examining patient satisfaction after RA-TKA. This study aims to identify the incidence of patients who were not satisfied following RA-TKA and to determine factors associated with higher levels of dissatisfaction. Methods. This was a retrospective review of 674 patients who underwent primary TKA between October 2016 and September 2020 with a minimum two-year follow-up. A five-point Likert satisfaction score was used to place patients into two groups: Group A were those who were very dissatisfied, dissatisfied, or neutral (Likert score 1 to 3) and Group B were those who were satisfied or very satisfied (Likert score 4 to 5). Patient demographic data, as well as preoperative and postoperative patient-reported outcome measures, were compared between groups. Results. Overall, 45 patients (6.7%) were in Group A and 629 (93.3%) were in Group B. Group A (vs Group B) had a higher proportion of male sex (p = 0.008), preoperative chronic opioid use (p < 0.001), preoperative psychotropic medication use (p = 0.01), prior anterior cruciate ligament (ACL) reconstruction (p < 0.001), and preoperative symptomatic lumbar spine disease (p = 0.004). Group A was also younger (p = 0.023). Multivariate analysis revealed preoperative opioid use (p = 0.012), prior ACL reconstruction (p = 0.038), male sex (p = 0.006), and preoperative psychotropic medication use (p = 0.001) as independent predictive factors of patient dissatisfaction. Conclusion. The use of RA-TKA demonstrated a high rate of patient satisfaction (629 of 674, 93.3%). Demographics for patients not satisfied following RA-TKA included: male sex, chronic opioid use, chronic psychotropic medication use, and prior ACL reconstruction. Patients in these groups should be identified preoperatively and educated on realistic expectations given their comorbid conditions. Cite this article: Bone Jt Open 2024;5(9):758–765

Aims

To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA.