Advanced technologies, like robotics, provide enhanced precision for implanting total knee arthroplasty (TKA) components; however, optimal component position and limb alignment remain unknown. This study purpose was to identify the ideal target sagittal component position and coronal limb alignment that produce optimal clinical outcomes. A retrospective review of 1,091 consecutive TKAs was performed. All TKAs were PCL retaining or sacrificing with anterior lipped (49.4%) or conforming bearings (50.6%) performed with modern perioperative protocols. Posterior tibial slope, femoral flexion, and tibiofemoral limb alignment were measured with a standardized protocols. Patients were grouped by the ‘how often does your knee feel normal?’ outcome score at latest follow-up. Machine learning algorithms were used to identify optimal alignment zones which predicted improved outcomes scores.Background
Methods
t is accepted dogma in total knee arthroplasty (TKA) that resecting the posterior cruciate ligament (PCL) increases the flexion space by approximately 4mm, which significantly affects intra-operative decisions and surgical techniques. Unfortunately, this doctrine is based on historical cadaveric studies of limited size. This study purpose was to more accurately determine the effect of PCL resection on the tibiofemoral flexion gap dimension Tibiofemoral joint space measurements were made during 127 standardized TKAs by two arthroplasty surgeons. A medial parapatellar approach, computer navigation and provisional tibial and femoral bone cuts were performed in all cases with particular attention to preserving PCL integrity. Cases with an incompetent or damaged PCL were excluded. The tibiofemoral gap dimension was measured with a calibrated tension device at full extension, 45-degrees, and 90-degrees before and after complete PCL resection.Introduction
Methods
Dual-mobility (DM) articulations may be useful for patients at increased risk for instability in primary and revision THA. While DM articulations are becoming increasingly popular, its routine use in primary THA is more uncertain. Even less is known about femoral head penetration in DM designs manufactured with highly cross-linked polyethylene infused with Vitamin E (E-HXLPE). The purpose of this study was to evaluate the early clinical results and femoral head penetration rates of primary THA implanted with DM E-HXLPE. Between 2012 and 2017, 105 primary DM THAs were performed using a one-piece acetabular shell, 28mm ceramic head, coupled with an E-HXLPE outer bearing via a standard posterior approach. Three patients refused follow-up after six months. 102 hips (92 patients) were available for review. The diagnosis was 99% OA. Average age was 65.7 years (33–90 years). 56% of patients were female. The most common femoral head size was 50mm (range, 44–60mm). The average thickness of the E-HXLPE outer bearing was 22.7mm (range, 16–32mm). Patients were followed at two months (baseline radiograph), six months, one, three, five, and seven years. Harris hip scores (HHS), UCLA activity score, and femoral head penetration (Martell method) were obtained at each visit beyond two months. Follow-up averaged 3 years (range, 1–7 years).Introduction
Methods
Existing studies report more accurate implant placement with robotic-assisted unicompartmental knee arthroplasty (UKA); however, surgeon experience has not always been accounted for. The purpose of this study was to compare the accuracy of an experienced, high-volume surgeon to published data on robotic-assisted UKA tibial component alignment. One hundred thirty-one consecutive manual UKAs performed by a single surgeon using a cemented, fixed bearing implant were radiographically reviewed by an independent reviewer to avoid surgeon bias. Native and tibial implant slope and coronal alignment were measured on pre- and postoperative lateral and anteroposterior radiographs, respectively. Manual targets were set within 2° of native tibial slope and 0 to 2° varus tibial component alignment. Deviations from target were calculated as root mean square (RMS) errors and were compared to robotic-assisted UKA data.Introduction
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