The impact of a diaphyseal femoral deformity on knee alignment varies according to its severity and localization. The aims of this study were to determine a method of assessing the impact of diaphyseal femoral deformities on knee alignment for the varus knee, and to evaluate the reliability and the reproducibility of this method in a large cohort of osteoarthritic patients. All patients who underwent a knee arthroplasty from 2019 to 2021 were included. Exclusion criteria were genu valgus, flexion contracture (> 5°), previous femoral osteotomy or fracture, total hip arthroplasty, and femoral rotational disorder. A total of 205 patients met the inclusion criteria. The mean age was 62.2 years (SD 8.4). The mean BMI was 33.1 kg/m2 (SD 5.5). The radiological measurements were performed twice by two independent reviewers, and included hip knee ankle (HKA) angle, mechanical medial distal femoral angle (mMDFA), anatomical medial distal femoral angle (aMDFA), femoral neck shaft angle (NSA), femoral bowing angle (FBow), the distance between the knee centre and the top of the FBow (DK), and the angle representing the FBow impact on the knee (C’KS angle).Aims
Methods
Classifying trochlear dysplasia (TD) is useful to determine the treatment options for patients suffering from patellofemoral instability (PFI). There is no consensus on which classification system is more reliable and reproducible for the purpose of guiding clinicians’ management of PFI. There are also concerns about the validity of the Dejour Classification (DJC), which is the most widely used classification for TD, having only a fair reliability score. The Oswestry-Bristol Classification (OBC) is a recently proposed system of classification of TD, and the authors report a fair-to-good interobserver agreement and good-to-excellent intraobserver agreement in the assessment of TD. The aim of this study was to compare the reliability and reproducibility of these two classifications. In all, six assessors (four consultants and two registrars) independently evaluated 100 axial MRIs of the patellofemoral joint (PFJ) for TD and classified them according to OBC and DJC. These assessments were again repeated by all raters after four weeks. The inter- and intraobserver reliability scores were calculated using Cohen’s kappa and Cronbach’s α.Aims
Methods
Once knee arthritis and deformity have occurred, it is currently not known how to determine a patient’s constitutional (pre-arthritic) limb alignment. The purpose of this study was to describe and validate the arithmetic hip-knee-ankle (aHKA) algorithm as a straightforward method for preoperative planning and intraoperative restoration of the constitutional limb alignment in total knee arthroplasty (TKA). A comparative cross-sectional, radiological study was undertaken of 500 normal knees and 500 arthritic knees undergoing TKA. By definition, the aHKA algorithm subtracts the lateral distal femoral angle (LDFA) from the medial proximal tibial angle (MPTA). The mechanical HKA (mHKA) of the normal group was compared to the mHKA of the arthritic group to examine the difference, specifically related to deformity in the latter. The mHKA and aHKA were then compared in the normal group to assess for differences related to joint line convergence. Lastly, the aHKA of both the normal and arthritic groups were compared to test the hypothesis that the aHKA can estimate the constitutional alignment of the limb by sharing a similar centrality and distribution with the normal population.Aims
Methods
The purpose of this study was to compare the radiological outcomes of manual versus robotic-assisted medial unicompartmental knee arthroplasty (UKA). Postoperative radiological outcomes from 86 consecutive robotic-assisted UKAs (RAUKA group) from a single academic centre were retrospectively reviewed and compared to 253 manual UKAs (MUKA group) drawn from a prior study at our institution. Femoral coronal and sagittal angles (FCA, FSA), tibial coronal and sagittal angles (TCA, TSA), and implant overhang were radiologically measured to identify outliers.Aims
Methods
