Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

The June 2024 Hip & Pelvis Roundup³⁶⁰ looks at: Machine learning did not outperform conventional competing risk modelling to predict revision arthroplasty; Unravelling the risks: incidence and reoperation rates for femoral fractures post-total hip arthroplasty; Spinal versus general anaesthesia for hip arthroscopy: a COVID-19 pandemic- and opioid epidemic-driven study; Development and validation of a deep-learning model to predict total hip arthroplasty on radiographs; Ambulatory centres lead in same-day hip and knee arthroplasty success; Exploring the impact of smokeless tobacco on total hip arthroplasty outcomes: a deeper dive into postoperative complications.

Aims. Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results. The algorithm measured 1,078 radiographs at a rate of 12.6 s/image (2,156 unique measurements in 3.8 hours). There was no significant difference or bias between reader and algorithm measurements for the FMAA (p = 0.130 to 0.563). The FMAA was 6.3° (SD 1.0°; 25% outside range of 5.0° (SD 2.0°)) using definition one and 4.6° (SD 1.3°; 13% outside range of 5.0° (SD 2.0°)) using definition two. Differences between males and females were observed using definition two (males more valgus; p < 0.001). Conclusion. We developed a rapid and accurate DL tool to quantify the FMAA. Considerable variation with different measurement approaches for the FMAA supports that patient-specific anatomy and surgeon-dependent technique must be accounted for when correcting for the FMAA using an intramedullary guide. The angle between the mechanical and anatomical axes of the femur fell outside the range of 5.0° (SD 2.0°) for nearly a quarter of patients. Cite this article: Bone Jt Open 2024;5(2):101–108

Knee osteoarthritis (KOA) diagnosis is based on symptoms, assessed through questionnaires such as the WOMAC. However, the inconsistency of pain recording and the discrepancy between joint phenotype and symptoms highlight the need for objective biomarkers in KOA diagnosis. To this end, we study relationships among clinical and molecular data in a cohort of women (n=51) with Kellgren-Lawrence grade 2–3 KOA through Support Vector Machine (SVM) and a regulation network model (RNM). Clinical descriptors (i.e., pain catastrophism (CA); depression (DE); functionality (FU); joint pain (JP); rigidity (RI); sensitization (SE); synovitis (SY)) are used to classify patients. A Youden's test is performed for each classifier to determine optimal binarization thresholds for the descriptors. Thresholds are tested against patient stratification according to baseline WOMAC data from the Osteoarthritis Initiative, and the mean accuracy is 0.97. For our cohort, the data used as SVM inputs are KOA descriptors, synovial fluid (SL) proteomic measurements (n=25), and transcription factors (TF) activation obtained from RNM [2] stimulated with the SL measurements. The relative weights after classification reflect input importance. The performance of each classifier is evaluated through AUC-ROC analysis. The best classifier with clinical data is CA (AUC = 0.9), highly influenced by FU and SE, suggesting that kinesophobia is involved in pain perception. With SL input, leptin strongly influences every classifier, suggesting the importance of low-grade inflammation. When TF are used, the mean AUC is limited to 0.608, which can be related to the pleomorphic behaviour of osteoarthritic chondrocytes. Nevertheless, FU has an AUC of 0.7 with strong importance of FOXO downregulation. Though larger and longitudinal cohorts are needed, this unique combination of SVM and RNM shall help to map objectively KOA descriptors. Acknowledgements: Catalan & Spanish governments 2020FI_b00680; STRATO-PID2021126469ob-C21-2, European Commission (MSCA-TN-ETN-2020-Disc4All-955735, ERC-2021-CoG-O-Health-101044828). ICREA Academia

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

Aims. Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli. Results. A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34. o. (SD 2.4. o. ) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65. o. (SD 0.55. o. ) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre. Conclusion. The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning. Cite this article: Bone Jt Open 2022;3(10):767–776

Abstract. Objectives. Knee alignment affects both the development and surgical treatment of knee osteoarthritis. Automating femorotibial angle (FTA) and hip-knee-ankle angle (HKA) measurement from radiographs could improve reliability and save time. Further, if the gold-standard HKA from full-limb radiographs could be accurately predicted from knee-only radiographs then the need for more expensive equipment and radiation exposure could be reduced. The aim of this research is to assess if deep learning methods can predict FTA and HKA angle from posteroanterior (PA) knee radiographs. Methods. Convolutional neural networks with densely connected final layers were trained to analyse PA knee radiographs from the Osteoarthritis Initiative (OAI) database with corresponding angle measurements. The FTA dataset with 6149 radiographs and HKA dataset with 2351 radiographs were split into training, validation and test datasets in a 70:15:15 ratio. Separate models were learnt for the prediction of FTA and HKA, which were trained using mean squared error as a loss function. Heat maps were used to identify the anatomical features within each image that most contributed to the predicted angles. Results. FTA could be predicted with errors less than 3° for 99.8% of images, and less than 1° for 89.5%. HKA prediction was less accurate than FTA but still high: 95.7% within 3°, and 68.0 % within 1°. Heat maps for both models were generally concentrated on the knee anatomy and could prove a valuable tool for assessing prediction reliability in clinical application. Conclusions. Deep learning techniques could enable fast, reliable and accurate predictions of both FTA and HKA from plain knee radiographs. This could lead to cost savings for healthcare providers and reduced radiation exposure for patients

Aims. To explore the clinical relevance of joint space width (JSW) narrowing on standardized-flexion (SF) radiographs in the assessment of cartilage degeneration in specific subregions seen on MRI sequences in knee osteoarthritis (OA) with neutral, valgus, and varus alignments, and potential planning of partial knee arthroplasty. Methods. We retrospectively reviewed 639 subjects, aged 45 to 79 years, in the Osteoarthritis Initiative (OAI) study, who had symptomatic knees with Kellgren and Lawrence grade 2 to 4. Knees were categorized as neutral, valgus, and varus knees by measuring hip-knee-angles on hip-knee-ankle radiographs. Femorotibial JSW was measured on posteroanterior SF radiographs using a special software. The femorotibial compartment was divided into 16 subregions, and MR-tomographic measurements of cartilage volume, thickness, and subchondral bone area were documented. Linear regression with adjustment for age, sex, body mass index, and Kellgren and Lawrence grade was used. Results. We studied 345 neutral, 87 valgus, and 207 varus knees. Radiological JSW narrowing was significantly (p < 0.01) associated with cartilage volume and thickness in medial femorotibial compartment in neutral (r = 0.78, odds ratio (OR) 2.33) and varus knees (r = 0.86, OR 1.92), and in lateral tibial subregions in valgus knees (r = 0.87, OR 3.71). A significant negative correlation was found between JSW narrowing and area of subchondral bone in external lateral tibial subregion in valgus knees (r = −0.65, p < 0.01) and in external medial tibial subregion in varus knees (r = −0.77, p < 0.01). No statistically significant correlation was found in anterior and posterior subregions. Conclusion. SF radiographs can be potentially used for initial detection of cartilage degeneration as assessed by MRI in medial and lateral but not in anterior or posterior subregions. Cite this article: Bone Joint Res 2021;10(3):173–187

Background. Artificial total knee designs have revolutionized over time, yet 20% of the population still report dissatisfaction. The standard implants fail to replicate native knee kinematic functionality due to mismatch of condylar surfaces and non-anatomically placed implantation. (Daggett et al 2016; Saigo et al 2017). It is essential that the implant surface matches the native knee to prevent Instability and soft tissue impingement. Our goal is to use computational modeling to determine the ideal shapes and orientations of anatomically-shaped components and test the accuracy of fit of component surfaces. Methods. One hundred MRI scans of knees with early osteoarthritis were obtained from the NIH Osteoarthritis Initiative, converted into 3D meshes, and aligned via an anatomic coordinate system algorithm. Geomagic Design X software was used to determine the average anterior-posterior (AP) length. Each knee was then scaled in three dimensions to match the average AP length. Geomagic's least-squares algorithm was used to create an average surface model. This method was validated by generating a statistical shaped model using principal component analysis (PCA) to compare to the least square's method. The averaged knee surface was used to design component system sizing schemes of 1, 3, 5, and 7 (fig 1). A further fifty arthritic knees were modeled to test the accuracy of fit for all component sizing schemes. Standard deviation maps were created using Geomagic to analyze the error of fit of the implant surface compared to the native femur surface. Results. The average shape model derived from Principal Component Analysis had a discrepancy of 0.01mm and a standard deviation of 0.05mm when compared to Geomagic least squares. The bearing surfaces showed a very close fit within both models with minimal errors at the sides of the epicondylar line (fig 2). The surface components were lined up posteriorly and distally on the 50 femurs. Statistical Analysis of the mesh deviation maps between the femoral condylar surface and the components showed a decrease in deviation with a larger number of sizes reducing from 1.5 mm for a 1-size system to 0.88 mm for a 7-size system (table 1). The femoral components of a 5 or 7-size system showed the best fit less than 1mm. The main mismatch was on the superior patella flange, with maximum projection or undercut of 2 millimeters. Discussion and Conclusion. The study showed an approach to total knee design and technique for a more accurate reproduction of a normal knee. A 5 to 7 size system was sufficient, but with two widths for each size to avoid overhang. Components based on the average anatomic shapes were an accurate fit on the bearing surfaces, but surgery to 1-millimeter accuracy was needed. The results showed that an accurate match of the femoral bearing surfaces could be achieved to better than 1 millimeter if the component geometry was based on that of the average femur. For any figures or tables, please contact the authors directly

Aims. Recent studies have suggested that corticosteroid injections into the knee may harm the joint resulting in cartilage loss and possibly accelerating the progression of osteoarthritis (OA). The aim of this study was to assess whether patients with, or at risk of developing, symptomatic osteoarthritis of the knee who receive intra-articular corticosteroid injections have an increased risk of requiring arthroplasty. Methods. We used data from the Osteoarthritis Initiative (OAI), a multicentre observational cohort study that followed 4,796 patients with, or at risk of developing, osteoarthritis of the knee on an annual basis with follow-up available up to nine years. Increased risk for symptomatic OA was defined as frequent knee symptoms (pain, aching, or stiffness) without radiological evidence of OA and two or more risk factors, while OA was defined by the presence of both femoral osteophytes and frequent symptoms in one or both knees. Missing data were imputed with multiple imputations using chained equations. Time-dependent propensity score matching was performed to match patients at the time of receving their first injection with controls. The effect of corticosteroid injections on the rate of subsequent (total and partial) knee arthroplasty was estimated using Cox proportional-hazards survival analyses. Results. After removing patients lost to follow-up, 3,822 patients remained in the study. A total of 249 (31.3%) of the 796 patients who received corticosteroid injections, and 152 (5.0%) of the 3,026 who did not, had knee arthroplasty. In the matched cohort, Cox proportional-hazards regression resulted in a hazard ratio of 1.57 (95% confidence interval (CI) 1.37 to 1.81; p < 0.001) and each injection increased the absolute risk of arthroplasty by 9.4% at nine years’ follow-up compared with those who did not receive injections. Conclusion. Corticosteroid injections seem to be associated with an increased risk of knee arthroplasty in patients with, or at risk of developing, symptomatic OA of the knee. These findings suggest that a conservative approach regarding the treatment of these patients with corticosteroid injections should be recommended. Cite this article: Bone Joint J 2020;102-B(5):586–592

An increased prevalence of osteoarthritis (OA) in post-menopausal women has led to the suggestion that hormonal factors may play a role in the pathogenesis. This study aims to examine if undergoing a hysterectomy, both with retention and removal of ovaries, predisposes women to OA and secondly if the development is influenced by hormone replacement therapy (HRT). Statistical shape modelling (SSM) is a method of image analysis allowing for detection of subtle shape variation described by landmark points. Through the generation of linearly independent modes of variation, each image can be described in terms of numerical scores. 149 radiographs from female participants of the Osteoarthritis Initiative (OAI) were examined to compare hip morphology in those who had undergone hysterectomies compared to controls. No differences were observed in BMI, age, height or weight between groups. ANOVA and Games-Howell post-hoc analysis showed that modes 3 and 5 were statistically significant. Lower mode 3 scores were associated with hysterectomy (p=0.019), with narrowing of the femoral neck and increased acetabular coverage. Lower mode 5 scores were associated with hysterectomy and oophorectomy (p=0.049), displaying reduced coverage of the femoral head, superolateral migration of the femoral head and larger greater trochanter. No associations were observed between HRT use and OA. The subtle morphologic features of hip OA present in only hysterectomised women suggests undergoing a hysterectomy may be a predisposing factor and a clinical consideration. The use of HRT was not observed to influence the development of OA and thus cannot be suggested as a protective measure

Introduction

This analysis determined whether the type and level of physical activity changes during the initial 24 months post-total hip (THR) or total knee replacement (TKR) compared to pre-operative levels, and how this change compares to people without arthroplasty or osteoarthritis.

Patellofemoral joint (PFJ) arthroplasty is traditionally performed using mechanical jigs to align the components, and it is hard to fine tune implant placement for the individual patient. These replacements have not had the same success rate as other forms of total or partial knee replacement surgery1. Our team have developed a computer assisted planning tool that allows alignment of the implant based on measurements of the patient's anatomy from MRI data with the aim of improving the success of patellofemoral joint arthroplasty. When planning a patellofemoral joint arthroplasty, one must start from the premise that the original joint is either damaged as a result of osteoarthritis, or is dysplastic in some way, deviating from a normal joint. The research aimed to plan PFJ arthroplasty using knowledge of the relationship between a normal PFJ (trochlear groove, trochlea axis and articular surfaces) and other aspects of the knee2, allowing the plan to be estimated from unaffected bone surfaces, within the constraints of the available trochlea. In order to establish a patient specific trochlea model a method was developed to automatically compute an average shape of the distal femur from normal distal femur STL files (Fig.1). For that MRI scans of 50 normal knees from osteoarthritis initiative (OAI) study were used. Mimics and 3-matic software (Materialise) packages were used for segmentation and analysis of 3D models. Spheres were fitted to the medial and lateral flexion facets for both average knee model and patient knee model. The average knee was rescaled and registered in order to match flexion facet axis (FFA) distance and FFA midpoint of the patient (Fig.2). The difference between the patient surface and the average knee surface allow to plan the patella groove alteration. The Patella cut is planned parallel to the plane fitted to the anterior surface of the patella. The patella width/thickness ratio (W/T=2) is used to predict the post reconstruction thickness3. The position of the patella component (and its orientation if a component with a median ridge is used) is also planned. The plan is next fine-tuned to achieve satisfactory PFJ kinematics4 (Fig.3). This will be complemented by intraoperative PFJ tracking which assists with soft tissue releases. PFJ kinematics is evaluated in terms of patella shift, tilt and deviation from the previously described circular path of the centre of the patella. The effect of preoperative planning on PFJ tracking and soft tissue releases is being examined. Additional study is needed to evaluate whether planning and intraoperative kinematic measurements improve the clinical outcome of PFJ arthroplasty

Introduction. Despite generally excellent patient outcomes for Total Knee Arthroplasty (TKA), there remains a contingent of patients, up to 20%, who are not satisfied with the outcome of their procedure. (Beswick, 2012) There has been a large amount of research into identifying the factors driving these poor patient outcomes, with increasing recognition of the role of non-surgical factors in predicting achieved outcomes. However, most of this research has been based on single database or registry sources and so has inherited the limitations of its source data. The aim of this work is to develop a predictive model that uses expert knowledge modelling in conjunction with data sources to build a predictive model of TKR patient outcomes. Method. The preliminary Bayesian Belief Network (BBN) developed and presented here uses data from the Osteoarthritis Initiative, a National Institute of Health funded observational study targeting improved diagnosis and monitoring of osteoarthritis. From this data set, a pared down subset of patient outcome relevant preoperative questionnaire sets has been extracted. The BBN structure provides a flexible platform that handles missing data and varying data collection preferences between surgeons, in addition to temporally updating its predictions as the patient progresses through pre and postoperative milestones in their recovery. In addition, data collected using wearable activity monitoring devices has been integrated. An expert knowledge modelling process relying on the experience of the practicing surgical authors has been used to handle missing cross-correlation observations between the two sources of data. Results. The model presented here has been internally cross validated and has some interesting facets, including the strongest single predictive question of bad outcome for the patient being the presence of lower back pain. Clinical implementation and long term predictive accuracy result collection is ongoing. Discussion. Unsatisfied patients represent a significant minority of TKR recipients, with multiple, multifaceted causal factors both in surgery and out implicated. Historically, focus has been on the role of management and improvement of the surgical factors, which is linked to the fact that surgical factors can often lead to far more disastrous consequences for the patient and the basic principle that “you only improve what you measure.” Growing collection of Patient Reported Outcome Measures by registries around the world has exposed the fact that management of patient factors has lagged behind. (Judge, 2012) Increasingly, the pivotal role of unmet expectations in determining patient satisfaction (Noble, 2006) and the “expectation gap” (Ghomrawi, 2012) between surgeons and patients has been exposed as an opportunity to improve patient outcomes. By developing a model that uses existing surgical expert knowledge to integrate research identified preoperative factors that can be accurately and practically gathered in a clinical setting, a workflow that manages patient expectations in order to optimize outcomes could reduce dissatisfaction rates in TKR recipients. Future work should focus on improving clinical integration and, in the absence of sufficiently wide, deep and complete patient response and predictor datasets, ways of harnessing existing expert knowledge into an evolving predictive tool of patient outcomes

Introduction. Pain related to knee osteoarthritis (OA) is a complex phenomenon that cannot be fully explained by radiographic disease severity. We hypothesized that pain phenotypes are likely to be derived from a confluence of factors across multiple domains: knee OA pathology, psychology, and neurophysiological pain processing. The purpose of this study was to identify distinct phenotypes of knee OA, using measures from the proposed domains. Methods. Data from 3494 subjects participating in the Osteoarthritis Initiative (OAI) study was analyzed. Variables analyzed included: radiographic OA severity (Kellgren-Lawrence grade), isometric quadriceps strength, Body Mass Index (BMI), comorbidities, CES-D Depression subscale score, Coping Strategies Questionnaire Catastrophizing subscale score, number of pain sites, and knee tenderness on physical examination. Variables used for comparison across classes included pain severity, WOMAC disability score, sex and age. Latent Class Analysis was performed. Model solutions were evaluated using the Bayesian Information Criterion. One-way ANOVAs and post hoc least significance difference tests were used for comparison of classes. Results. A four-class model was identified. Class 1 (57% of study population) had lesser radiographic OA, little psychological involvement, greater strength, and less pain sensitivity. Class 2 (28%) had higher rates of knee joint tenderness. Class 3 (10%) had greater psychological distress and more bodily pain sites. Class 4 (4%) had more comorbidities. Additionally, Class 1 was the youngest, had the lowest disability, and least pain. Class 4 was the oldest. Class 2 had a higher proportion of females. Class 3 had the worst disability and most pain. Conclusions. Four distinct pain phenotypes for knee OA were identified. Psychological factors, knee tenderness, and comorbidities appear to be important in defining phenotypes of OA-related pain. Therapies in knee OA should take a multicomponent approach, recognizing the factors most relevant to an individual's experience of pain

Purpose. It is well known that meniscus extrusion is associated with structural progression of knee OA. However, it is unknown whether medial meniscus extrusion promotes cartilage loss in specific femorotibial subregions, or whether it is associated with a increase in cartilage thickness loss throughout the entire femorotibial compartment. We applied quantitative MRI-based measurements of subregional cartilage thickness (change) and meniscus position, to address the above question in knees with and without radiographic joint space narrowing (JSN). Methods. 60 participants with unilateral medial OARSI JSN grade 1–3, and contralateral knee OARSI JSN grade 0 were drawn from the Osteoarthritis Initiative. Manual segmentation of the medial tibial and weight-bearing medial femoral cartilage was performed, using baseline and 1-year follow-up sagittal double echo steady-state (DESS) MRI, and proprietary software (Chondrometrics GmbH, Ainring, Germany). Segmentation of the entire medial meniscus was performed with the same software, using baseline coronal DESS images. Longitudinal cartilage loss was computed for 5 tibial (central, external, internal, anterior, posterior) and 3 femoral (central, external, internal) subregions. Meniscus position was determined as the % area of the entire meniscus extruding the tibial plateau medially and the distance between the external meniscus border and the tibial cartilage in an image located 4mm posterior to the central image (a location commonly used for semi-quantitative meniscus scoring). The relationship between meniscus position and cartilage loss was assessed using Pearson (r) correlation coefficients, for knees with JSN and without JSN. Results. The percentage of knees showing a quantitative value of >3mm medial meniscus extrusion was 50% in JSN knees, and only 12% in noJSN knees. The 1-year cartilage loss in the medial femorotibial compartment was 74±182µm (2.0%) in JSN knees, and 26±120µm (0.8%) in noJSN knees. There was a significant correlation between cartilage loss throughout the entire femorotibial compartment (MFTC) and extrusion area in JSN knees but not for noJSN knees. Also, the extrusion distance measured 4mm posterior to the central slice was not significantly correlated with MFTC cartilage loss. The strongest (negative) correlation between meniscus position and subregional femorotibial cartilage loss (r=−0.36) was observed for the external medial tibia. In contrast, no significant relationship was seen in the central tibia. No significant relationship was found in other tibial subregions, except for the anterior medial tibia, but only in JSN knees (r=−0.27). Correlation coefficients for the femoral subregions were generally smaller than those for tibial subregions, with only the internal medial weight-bearing femur attaining statistical significance (r =−0.26). Conclusions. The current results show that the relationship between meniscus extrusion and cartilage loss differs substantially between femorotibial subregions. The correlation was strongest for the external medial tibia, a region that is physiologically covered by the medial meniscus. It was less for other tibial and femoral subregions, including the central medial tibia, a region that exhibited similar rates of cartilage loss as the external subregion. The findings suggest that external tibia may be particularly vulnerable to cartilage tissue loss once the meniscus extrudes and the surface is “exposed” to direct, non-physiological, cartilage-cartilage contact

We studied whether the presence of lateral osteophytes on plain radiographs was a predictor for the quality of cartilage in the lateral compartment of patients with varus osteoarthritic of the knee (Kellgren and Lawrence grade 2 to 3).

The baseline MRIs of 344 patients from the Osteoarthritis Initiative (OAI) who had varus osteoarthritis (OA) of the knee on hip-knee-ankle radiographs were reviewed. Patients were categorised using the Osteoarthritis Research Society International (OARSI) osteophyte grading system into 174 patients with grade 0 (no osteophytes), 128 grade 1 (mild osteophytes), 28 grade 2 (moderate osteophytes) and 14 grade 3 (severe osteophytes) in the lateral compartment (tibia). All patients had Kellgren and Lawrence grade 2 or 3 arthritis of the medial compartment. The thickness and volume of the lateral cartilage and the percentage of full-thickness cartilage defects in the lateral compartment was analysed.

There was no difference in the cartilage thickness or cartilage volume between knees with osteophyte grades 0 to 3. The percentage of full-thickness cartilage defects on the tibial side increased from < 2% for grade 0 and 1 to 10% for grade 3.

The lateral compartment cartilage volume and thickness is not influenced by the presence of lateral compartment osteophytes in patients with varus OA of the knee. Large lateral compartment osteophytes (grade 3) increase the likelihood of full-thickness cartilage defects in the lateral compartment.

Cite this article: Bone Joint J 2015;97-B:1634–9.