Objectives. Static radiostereometric analysis (RSA) using implanted markers is considered the most accurate system for the evaluation of prosthesis migration. By using CT bone models instead of markers, combined with a dynamic RSA system, a non-invasive measurement of joint movement is enabled. This method is more accurate than current 3D skin marker-based tracking systems. The purpose of this study was to evaluate the accuracy of the CT model method for measuring knee joint kinematics in static and dynamic RSA using the marker method as the benchmark. Methods. Bone models were created from CT scans, and tantalum beads were implanted into the tibia and femur of eight human cadaver knees. Each specimen was secured in a fixture, static and dynamic stereoradiographs were recorded, and the bone models and marker models were fitted to the stereoradiographs. Results. Results showed a mean difference between the two methods in all six degrees of freedom for static RSA to be within -0.10 mm/° and 0.08 mm/° with a 95% limit of agreement (LoA) ranging from ± 0.49 to 1.26. Dynamic RSA had a slightly larger range in mean difference of -0.23 mm/° to 0.16 mm/° with LoA ranging from ± 0.75 to 1.50. Conclusions. In a laboratory-controlled setting, the CT model method combined with dynamic RSA may be an alternative to previous marker-based methods for kinematic analyses. Cite this article: K. Stentz-Olesen, E. T. Nielsen, S. De Raedt, P. B. Jørgensen, O. G. Sørensen, B. L. Kaptein, M. S. Andersen, M. Stilling.
The Forgotten Joint Score (FJS) is a 12-item patient reported outcome measurement instrument. It was developed with classical test theory, without testing assumptions such as unidimensionality (all items reflect one underlying factor), appropriate weighting of each item, no differential item function (DIF, different groups answer the same way), and monotonicity (people with higher function have higher score). We applied item response theory (IRT) to improve the validity of FJS to contemporary standards to optimise it for ongoing use. Does the FJS reflect one latent trait? Can an IRT model be fitted to the FJS to provide interval-scaled measurement?Abstract
Background
Research Questions
The Coronal Plane Alignment of the Knee (CPAK) classification is a simple and comprehensive system for predicting pre-arthritic knee alignment. However, when the CPAK classification is applied in the Asian population, which is characterized by more varus and wider distribution in lower limb alignment, modifications in the boundaries of arithmetic hip-knee-ankle angle (aHKA) and joint line obliquity (JLO) should be considered. The purposes of this study were as follows: first, to propose a modified CPAK classification based on the actual joint line obliquity (aJLO) and wider range of aHKA in the Asian population; second, to test this classification in a cohort of Asians with healthy knees; third, to propose individualized alignment targets for different CPAK types in kinematically aligned (KA) total knee arthroplasty (TKA). The CPAK classification was modified by changing the neutral boundaries of aHKA to 0° ± 3° and using aJLO as a new variable. Radiological analysis of 214 healthy knees in 214 Asian individuals was used to assess the distribution and mean value of alignment angles of each phenotype among different classifications based on the coronal plane. Individualized alignment targets were set according to the mean lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) of different knee types.Aims
Methods
To estimate the measurement properties for the Oxford Knee Score (OKS) in patients undergoing revision knee arthroplasty (responsiveness, minimal detectable change (MDC-90), minimal important change (MIC), minimal important difference (MID), internal consistency, construct validity, and interpretability). Secondary data analysis was performed for 10,727 patients undergoing revision knee arthroplasty between 2013 to 2019 using a UK national patient-reported outcome measure (PROM) dataset. Outcome data were collected before revision and at six months postoperatively, using the OKS and EuroQol five-dimension score (EQ-5D). Measurement properties were assessed according to COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) guidelines.Aims
Methods
Wear of polyethylene inserts plays an important role in failure
of total knee replacement and can be monitored Before revision, the minimum joint space width values and their
locations on the insert were measured in 15 fully weight-bearing
radiographs. These measurements were compared with the actual minimum
thickness values and locations of the retrieved tibial inserts after
revision. Introduction
Method
Tibial component malrotation is associated with pain, stiffness and altered patellofemoral kinematics in total knee arthroplasty (TKA). However, accurately measuring tibial component rotation following TKA is difficult. Proposed protocols utilizing computed tomography (CT) are not well validated and can be time consuming. This study aimed to; 1) Validate and compare the reproducibility of the Berger (2D-CT) and Mayo (3D-CT) protocols; 2) Validate a simple, and potentially rapid screening measurement using an anatomical distance on 2D axial CT- the Centre of Tibial Tray to Tibial Tubercle (CTTT) distance. Rotational alignment of 70 TKA patients were evaluated by 3 independent observers using the Berger, and Mayo protocols, which have been previously described, and a new CTTT protocol (Figure 1). The inter and intra-rater interclass correlation coefficients (ICC's), mean difference between measurements and the mean measurement times were calculated. Linear regression analysis was performed to give a coefficient of determination (R2).Introduction
Methods
This study validates the short-form WOMAC function scale for assessment of conservative treatment of osteoarthritis of the knee. Data were collected before treatment and six and nine months later, from 100 patients with osteoarthritis of the knee to determine the validity, internal consistency, test-retest reliability, floor and ceiling effects, and responsiveness of the short-form WOMAC function scale. The scale showed high correlation with the traditional WOMAC and other measures. The internal consistency was good (Cronbach α: 0.88 to 0.95) and an excellent test-retest reliability was found (Lin’s concordance correlation coefficient (ρc): 0.85 to 0.94). The responsiveness was adequate and comparable to that of the traditional WOMAC (standardised response mean 0.56 to 0.44 and effect size 0.64 to 0.57) and appeared not to be significantly affected by floor or ceiling effects (0% and 7%, respectively). The short-form WOMAC function scale is a valid, reliable and responsive alternative to the traditional WOMAC in the evaluation of patients with osteoarthritis of the knee managed conservatively. It is simple to use in daily practice and is therefore less of a burden for patients in clinical trials.
This study demonstrates a significant correlation
between the American Knee Society (AKS) Clinical Rating System and
the Oxford Knee Score (OKS) and provides a validated prediction
tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed
five years after TKR and completed AKS assessments and an OKS questionnaire.
Multivariate regression analysis demonstrated significant correlations between
OKS and the AKS knee and function scores but a stronger correlation
(r = 0.68, p <
0.001) when using the sum of the AKS knee and
function scores. Addition of body mass index and age (other statistically
significant predictors of OKS) to the algorithm did not significantly
increase the predictive value. The simple regression model was used to predict the OKS in a
group of 236 patients who were clinically assessed nine to ten years
after TKR using the AKS system. The predicted OKS was compared with
actual OKS in the second group. Intra-class correlation demonstrated
excellent reliability (r = 0.81, 95% confidence intervals 0.75 to
0.85) for the combined knee and function score when used to predict
OKS. Our findings will facilitate comparison of outcome data from
studies and registries using either the OKS or the AKS scores and
may also be of value for those undertaking meta-analyses and systematic
reviews. Cite this article:
Aims. The aim of the British Association for Surgery of the Knee (BASK) Meniscal Consensus Project was to develop an evidence-based treatment guideline for patients with meniscal lesions of the knee. Materials and Methods. A formal consensus process was undertaken applying nominal group, Delphi, and appropriateness methods. Consensus was first reached on the terminology relating to the definition, investigation, and classification of meniscal lesions. A series of simulated clinical scenarios was then created and the appropriateness of arthroscopic meniscal surgery or nonoperative treatment in each scenario was rated by the group. The process was informed throughout by the latest published, and previously unpublished, clinical and epidemiological evidence. Scenarios were then grouped together based upon the similarity of clinical features and ratings to form the guideline for treatment. Feedback on the draft guideline was sought from the entire membership of BASK before final revisions and approval by the consensus group. Results. A total of 45 simulated clinical scenarios were refined to five common clinical presentations and six corresponding treatment recommendations. The final guideline stratifies patients based upon a new, standardized classification of symptoms, signs, radiological findings, duration of symptoms, and previous treatment. Conclusion. The 2018 BASK Arthroscopic Meniscal Surgery Treatment Guidance will facilitate the consistent identification and treatment of patients with meniscal lesions. It is hoped that this guidance will be adopted nationally by surgeons and help inform healthcare commissioning guidance.
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. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models.Aims
Methods
This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.Aims
Methods
The aims of this study were to assess mapping models to predict the three-level version of EuroQoL five-dimension utility index (EQ-5D-3L) from the Oxford Knee Score (OKS) and validate these before and after total knee arthroplasty (TKA). A retrospective cohort of 5,857 patients was used to create the prediction models, and a second cohort of 721 patients from a different centre was used to validate the models, all of whom underwent TKA. Patient characteristics, BMI, OKS, and EQ-5D-3L were collected preoperatively and one year postoperatively. Generalized linear regression was used to formulate the prediction models.Aims
Methods
The aim of this study was to screen the entire genome for genetic markers associated with risk for anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injury. Genome-wide association (GWA) analyses were performed using data from the Kaiser Permanente Research Board (KPRB) and the UK Biobank. ACL and PCL injury cases were identified based on electronic health records from KPRB and the UK Biobank. GWA analyses from both cohorts were tested for ACL and PCL injury using a logistic regression model adjusting for sex, height, weight, age at enrolment, and race/ethnicity using allele counts for single nucleotide polymorphisms (SNPs). The data from the two GWA studies were combined in a meta-analysis. Candidate genes previously reported to show an association with ACL injury in athletes were also tested for association from the meta-analysis data from the KPRB and the UK Biobank GWA studies.Aims
Methods
The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.Aims
Methods
A comprehensive classification for coronal lower limb alignment with predictive capabilities for knee balance would be beneficial in total knee arthroplasty (TKA). This paper describes the Coronal Plane Alignment of the Knee (CPAK) classification and examines its utility in preoperative soft tissue balance prediction, comparing kinematic alignment (KA) to mechanical alignment (MA). A radiological analysis of 500 healthy and 500 osteoarthritic (OA) knees was used to assess the applicability of the CPAK classification. CPAK comprises nine phenotypes based on the arithmetic HKA (aHKA) that estimates constitutional limb alignment and joint line obliquity (JLO). Intraoperative balance was compared within each phenotype in a cohort of 138 computer-assisted TKAs randomized to KA or MA. Primary outcomes included descriptive analyses of healthy and OA groups per CPAK type, and comparison of balance at 10° of flexion within each type. Secondary outcomes assessed balance at 45° and 90° and bone recuts required to achieve final knee balance within each CPAK type.Aims
Methods
Unicompartmental knee arthroplasty (UKA) has become a popular method of treating knee localized osteoarthritis (OA). Additionally, the posterior cruciate ligament (PCL) is essential to maintaining the physiological kinematics and functions of the knee joint. Considering these factors, the purpose of this study was to investigate the biomechanical effects on PCL-deficient knees in medial UKA. Computational simulations of five subject-specific models were performed for intact and PCL-deficient UKA with tibial slopes. Anteroposterior (AP) kinematics and contact stresses of the patellofemoral (PF) joint and the articular cartilage were evaluated under the deep-knee-bend condition.Aims
Methods
The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset.Aims
Methods
Posterior condylar offset (PCO) and posterior tibial slope (PTS) are critical factors in total knee arthroplasty (TKA). A computational simulation was performed to evaluate the biomechanical effect of PCO and PTS on cruciate retaining TKA. We generated a subject-specific computational model followed by the development of ± 1 mm, ± 2 mm and ± 3 mm PCO models in the posterior direction, and -3°, 0°, 3° and 6° PTS models with each of the PCO models. Using a validated finite element (FE) model, we investigated the influence of the changes in PCO and PTS on the contact stress in the patellar button and the forces on the posterior cruciate ligament (PCL), patellar tendon and quadriceps muscles under the deep knee-bend loading conditions.Objectives
Methods
An important measure for the diagnosis and monitoring of knee osteoarthritis is the minimum joint space width (mJSW). This requires accurate alignment of the x-ray beam with the tibial plateau, which may not be accomplished in practice. We investigate the feasibility of a new mJSW measurement method from stereo radiographs using 3D statistical shape models (SSM) and evaluate its sensitivity to changes in the mJSW and its robustness to variations in patient positioning and bone geometry. A validation study was performed using five cadaver specimens. The actual mJSW was varied and images were acquired with variation in the cadaver positioning. For comparison purposes, the mJSW was also assessed from plain radiographs. To study the influence of SSM model accuracy, the 3D mJSW measurement was repeated with models from the actual bones, obtained from CT scans.Objectives
Materials and Methods
An evidence-based radiographic Decision Aid for meniscal-bearing
unicompartmental knee arthroplasty (UKA) has been developed and
this study investigates its performance at an independent centre. Pre-operative radiographs, including stress views, from a consecutive
cohort of 550 knees undergoing arthroplasty (UKA or total knee arthroplasty;
TKA) by a single-surgeon were assessed. Suitability for UKA was
determined using the Decision Aid, with the assessor blinded to
treatment received, and compared with actual treatment received, which
was determined by an experienced UKA surgeon based on history, examination,
radiographic assessment including stress radiographs, and intra-operative
assessment in line with the recommended indications as described
in the literature.Aims
Patients and Methods
