Robotic TKA allows for quantifiable precision performing bone resections for implant realignment within acceptable final component and limb alignments. One of the early steps in this robotic technique is after initial exposure and removal of medial and lateral osteophytes, a “pose-capture” is performed with varus and valgus stress applied to the knee in near full extension and 90° of flexion to assess gaps. Component alignment adjustments can be made on the preoperative plan to balance the gaps. At this point in the procedure any posterior osteophytes will still be present, which could after removal change the flexion and extension gaps by 1–3mm. This must be taken into consideration, or changes in component alignment could result in over-correction of gaps can occur. The purpose of this study was to identify what effect the posterior osteophyte's size and location and their removal had on gap measurements between pose-capture and after bone cuts are made and gaps assessed during implant trialing.Introduction
Objective
In total knee arthroplasty (TKA), component realignment with bone-based surgical correction (BBSC) can provide soft tissue balance and avoid the unpredictability of soft tissue releases (STR) and potential for more post-operative pain. Robotic-assisted TKA enhances the ability to accurately control bone resection and implant position. The purpose of this study was to identify preoperative and intraoperative predictors for soft tissue release where maximum use of component realignment was desired. This was a retrospective, single center study comparing 125 robotic-assisted TKAs quantitatively balanced using load-sensing tibial trial components with BBSC and/or STR. A surgical algorithm favoring BBSC with a desired final mechanical alignment of between 3° varus and 2° valgus was utilized. Component realignment adjustments were made during preoperative planning, after varus/valgus stress gaps were assessed after removal of medial and lateral osteophytes (pose capture), and after trialing. STR was performed when a BBSC would not result in knee balance within acceptable alignment parameters. The predictability for STR was assessed at four steps of the procedure: Preoperatively with radiographic analysis, and after assessing static alignment after medial and lateral osteophyte removal, pose capture, and trialing. Cutoff values predictive of release were obtained using receiver operative curve analysis.Introduction
Methods
Not all patients receive enhanced mobility and return to comfortable, independent living after Total Hip Arthroplasty (THA). It would be beneficial to both surgeons and patients to be able to predict short term outcomes for THA. The purpose of this study was to investigate factors affecting the short term outcome of primary THA and develop a multivariate regression model that can predict such outcomes. This was a prospective study of 101 patients, who underwent primary THA. All patients were followed for a minimum of 1 year. 12 independent variables, including age, gender, diagnosis, presence of preoperative comorbidities, BMI, preoperative WOMAC physical component (PC) score, type of anesthesia, type of fixation, surgical time, estimated blood loss, use of a postoperative drain, and length of stay were analyzed using correlation and multivariate regression analyses. Multivariate regression models were validated using an independent cohort. Correlation analyses showed three variables significantly influence short term THA outcome. These include preoperative WOMAC PC score (PC) (p<
0.01), gender (G) (p= 0.01) and the presence of preoperative comorbidities (CMB) (p= 0.02). By multivariate regression analysis, the following regression model was obtained: Outcome = PC*0.45 −G*9 + CMB*8 + 62. This model exhibited positive correlation (R2=.25) when compared to a separate cohort of 27 patients undergoing THA not included in the original equation derivation. Our multivariate regression analysis has yielded statistical, multivariate confirmation or non-confirmation of common, predictive THA factors that have previously been reported in the literature. This study provides a concrete, statistically significant measure indicating that preoperative WOMAC PC score, gender, and the presence of preoperative comorbidities are predictive factors for short term primary THA outcome. Finally, our multivariate regression equation can be used to predict the general short term patient outcome following primary THA.
