Total knee arthroplasty (TKA) reliably improves pain and function in patients with knee osteoarthritis (OA), though a substantial percentage of patients remain unsatisfied. Reasons include the presence of complications, persistent pain, and unmet expectations. The aim of this study was to determine whether the sequential addition of accelerometer-based navigation of the distal femoral cut and sensor-assisted soft tissue balancing changed complication rates, radiographic alignment, or patient-reported outcomes (PROs) compared to TKA performed with conventional instrumentation. This retrospective cohort study included 371 TKAs in 319 patients. All surgeries were performed by a single surgeon in sequential fashion using a measured resection technique with a goal of mechanical alignment. The historical control group, utilizing intramedullary guides for distal femoral resection and surgeon-guided soft tissue balancing, was compared to group 1 (accelerometer-based navigation for distal femoral resection, surgeon-guided balancing) and group 2 (navigated femoral resection, sensor-guided balancing). Primary outcome measures were PROMIS scores including physical function computerized adaptive test (PF CAT), and the Global 10 health assessment (including physical, mental, and pain scores), and Knee Injury Osteoarthritis and Outcome Score (KOOS), measured preoperatively and at 6 weeks and 12 months postoperatively. Radiographic measurements included component position and overall mechanical alignment of the limb and were made at 6 weeks by a single examiner from hip to ankle standing films. Charts were reviewed for pre- and postoperative ROM at 6 weeks, polyethylene insert morphology, and postoperative hematocrit change. Complications were recorded, including manipulation under anesthesia and reoperation. Our study was powered to detect a difference of 1 standard deviation in PF CAT score with 100 patients. Statistical analysis was performed by a statistician including t-tests, multivariate regression, and time series plot analyses.Introduction
Methods
Recent studies of novel healthcare episode payment models, such as the Bundled Payments for Care Improvement (BPCI) initiative, have demonstrated pathways for improving value. However, these models may not provide appropriate payments for patients with significant medical comorbidities or complications. The objective of this study was to identify risk factors for exceeding our institution's target payment, the so-called “bundle busters.” After receiving an exemption from the Institutional Review Board, we queried our institutional data warehouse for all patients (n=412) that underwent total joint arthroplasty (TJA) of the hip (n=192), knee (n=207), or ankle (n=13), and qualified for our institution's bundled payments model during the study time period (July 2015 – May 2017). Patients with medical conditions that were not well controlled or were potentially optimizable were all sent for preoperative medical optimization prior to surgery. For each 90-day episode, patient characteristics, medical comorbidities, perioperative data, and payments from the Centers for Medicare and Medicaid Services (CMS) were obtained. Episodes where Medicare payments exceeded the target payment were considered “busters”. The busters were older, and had higher comorbidity scores (all, p<0.01). Variables were summarized using descriptive statistics and risk ratios were calculated using a modified Poisson regression analysis.Introduction
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