Radiologic Assessment of Knee Phenotypes Based on the Coronal Plane Alignment of the Knee Classification in a Korean Population.

Background: The Coronal Plane Alignment of the Knee (CPAK) classification system has been developed as a comprehensive system that describes 9 coronal plane phenotypes based on constitutional limb alignment and joint line obliquity (JLO). Due to the characteristics of Asian populations, which show more varus and wider distribution in lower limb alignment than other populations, modification of the boundaries of the arithmetic hip-knee-ankle angle (aHKA) and JLO should be considered. The purpose of this study was to determine the knee phenotype in a Korean population based on the original CPAK and modified CPAK classification systems. Methods: We reviewed prospectively collected data of 500 healthy and 500 osteoarthritic knees between 2021 and 2023 using radiographic analysis and divided them based on the modified CPAK classification system by widening the neutral boundaries of the aHKA to 0° ± 3° and using the actual JLO as a new variable. Using long-leg standing weight-bearing radiographs, 6 radiographic parameters were measured to evaluate the CPAK type: the mechanical HKA angle, medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), aHKA, JLO, and actual JLO. Results: From 2 cohorts of 1,000 knees, the frequency distribution representing all CPAK types was different between the healthy and arthritic groups. The most common categories were type II (38.2%) in the healthy group and type I (53.8%) in the arthritic group based on the original CPAK classification. The left and upward shift in the distribution of knee phenotypes in the original classification was corrected evenly after re-establishing the boundaries of a neutral aHKA and the actual JLO. According to the modified CPAK classification system, the most common categories were type II (35.2%) in the healthy group and type I (38.0%) in the arthritic group. Conclusions: Although the modified CPAK classification corrected the uneven distribution seen when applying the original classification system in a Korean population, the most common category was type I in Korean patients with osteoarthritic knees in both classification systems. Furthermore, there were different frequencies of knee phenotypes among healthy and arthritic knees.

Gait classification of knee osteoarthritis patients using shoe-embedded internal measurement units sensor.

BACKGROUND: Knee osteoarthritis negatively affects the gait of patients, especially that of elderly people. However, the assessment of wearable sensors in knee osteoarthritis patients has been under-researched. During clinical assessments, patients may change their gait patterns under the placebo effect, whereas wearable sensors can be used in any environment. METHODS: Sixty patients with knee osteoarthritis and 20 control subjects were included in the study. Wearing shoes with an IMU sensor embedded in the insoles, the participants were required to walk along a walkway. The sensor data were collected during the gait. To discriminate between healthy and knee osteoarthritis patients and to classify different subgroups of knee osteoarthritis patients (patients scheduled for surgery vs. patients not scheduled for surgery; bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis), we used a machine learning approach called the support vector machine. A total of 88 features were extracted and used for classification. FINDINGS: The patients vs. healthy participants were classified with 71% accuracy, 85% sensitivity, and 56% specificity. The "patients scheduled for surgery" vs. "patients not scheduled for surgery" were classified with 83% accuracy, 83% sensitivity, and 81% specificity. The bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis was classified with 81% accuracy, 75% sensitivity, and 79% specificity. INTERPRETATION: Gait analysis using wearable sensors and machine learning can discriminate between healthy and knee osteoarthritis patients and classify different subgroups with reasonable accuracy, sensitivity, and specificity. The proposed approach requires no complex gait factors and is not limited to controlled laboratory settings.

Patients with knee osteoarthritis can be divided into subgroups based on tibiofemoral joint kinematics of gait - an exploratory and dynamic radiostereometric study.

OBJECTIVE: Patients with advanced knee osteoarthritis (KOA) frequently alter their gait patterns in an attempt to alleviate symptoms. Understanding the underlying pathomechanics and identifying KOA phenotypes are essential to improve treatments. We investigated kinematics in patients with KOA to identify subgroups of homogeneous knee joint kinematics. METHOD: A total of 66 patients with symptomatic KOA scheduled for total knee arthroplasty and 15 age-matched healthy volunteers with asymptomatic, non-arthritic knees were included. We used k-means clustering to divide patients into subgroups based on dynamic radiostereometry-assessed tibiofemoral joint kinematics. Clinical characteristics such as knee ligament lesions and KOA scores were graded by magnetic resonance imaging and radiographs, respectively. RESULTS: We identified four clusters that were supported by clinical characteristics. The flexion group (n = 20) consisted primarily of patients with medial KOA. The abduction group (n = 17) consisted primarily of patients with lateral KOA. The anterior draw group (n = 10) was composed of patients with medial KOA, some degree of anterior cruciate ligament lesion and the highest KOA score. The external rotation group (n = 19) primarily included patients with medial collateral and posterior cruciate ligament lesions. CONCLUSION: Based on tibiofemoral gait patterns, patients with advanced KOA can be divided into four subgroups with specific clinical characteristics and different KOA-affected compartments. The findings add to our understanding of how knee kinematics may affect the patient's development of different types of KOA. This may inspire improved and more patient-specific treatment strategies in the future.

Early-stage symptomatic osteoarthritis of the knee - time for action.

Osteoarthritis (OA) remains the most challenging arthritic disorder, with a high burden of disease and no available disease-modifying treatments. Symptomatic early-stage OA of the knee (the focus of this Review) urgently needs to be identified and defined, as efficient early-stage case finding and diagnosis in primary care would enable health-care providers to proactively and substantially reduce the burden of disease through proper management including structured education, exercise and weight management (when needed) and addressing lifestyle-related risk factors for disease progression. Efforts to define patient populations with symptomatic early-stage knee OA on the basis of validated classification criteria are ongoing. Such criteria, as well as the identification of molecular and imaging biomarkers of disease risk and/or progression, would enable well-designed clinical studies, facilitate interventional trials, and aid the discovery and validation of cellular and molecular targets for novel therapies. Treatment strategies, relevant outcomes and ethical issues also need to be considered in the context of the cost-effective management of symptomatic early-stage knee OA. To move forwards, a multidisciplinary and sustained international effort involving all major stakeholders is required.

Study Protocol Modeling Evoked Pain in Older African Americans With Knee Osteoarthritis.

BACKGROUND: African American (AA) older adults with knee osteoarthritis experience more severe chronic pain and advanced physical disability. One of the most prominent stimuli that provokes knee pain is movement. Research suggests that, compared to Whites, AAs report significantly higher movement-evoked pain (MEP) in the knee. However, little is known about the biopsychosocial-behavioral mechanisms underlying MEP. OBJECTIVES: The aim of the study was to present a study protocol to (a) characterize the biopsychosocial-behavioral mechanisms that predict MEP in AAs with knee osteoarthritis and (b) develop a targeted, mechanism-based self-management intervention to reduce MEP and maximize movement. METHODS: An observational, mixed-methods cohort study will enroll 90 AA/Black adults (ages 55-90 years) to understand intraindividual and interindividual effects on MEP. Participants will complete assessments of MEP, function and gait, biopsychosocial-behavioral questionnaires, quantitative sensory testing, and 7-day ecological momentary assessments of pain and related symptoms. For the qualitative phase, focus groups will be conducted to co-construct a mechanism-based pain self-management intervention. RESULTS: We will develop phenotypes of MEP based on biopsychosocial-behavioral predictors and correlate measures of MEP with function. Our central hypothesis is that higher levels of MEP will predict lower self-reported function and poorer performance on functional tasks and that multiple biopsychosocial and behavioral factors will be associated with MEP and function. Predictors may serve as risk or protective factors for MEP and physical function. In targeting the biopsychosocial-behavioral mechanisms of MEP, we anticipate that older AAs may request that intervention components include culturally tailored self-management education, movement/physical activity training, treatment decision-making skills, coaching, spirituality, and social/kinship support. CONCLUSION: Osteoarthritis is now the single most common cause of disability, mobility limitations, and persistent pain in older adults-especially AA older adults. To our knowledge, this will be the first study to systematically phenotype MEP in an older racial minority population with knee osteoarthritis and will be relevant for reducing knee pain and improving function.

Validation of a second-generation appropriateness classification system for total knee arthroplasty: a prospective cohort study.

BACKGROUND: To test the validity of a second-generation appropriateness system in a cohort of patients undergoing total knee arthroplasty (TKA). METHODS: We applied the RAND/UCLA Appropriateness Method to derive our second-generation system and conducted a prospective study of patients diagnosed with knee osteoarthritis in eight public hospitals in Spain. Main outcome questionnaires were the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Short-Form-12 (SF-12), and the Knee Society Score satisfaction scale (KSS), completed before and 6 months after TKA. Baseline, changes from baseline to 6 months (journey outcome), and 6-month scores (destination outcome) were compared according to appropriateness category. Percentage of patients attaining the minimal clinically important difference (MCID) and responders according to Outcome Measures in Rheumatology-Osteoarthritis Research Society (OMERACT-OARSI) criteria were also reported. RESULTS: A total of 282 patients completed baseline and 6-month questionnaires. Of these, 142 (50.4%) were classified as Appropriate, 90 (31.9%) as Uncertain, and 50 (17.7%) as Inappropriate. Patients classified as Appropriate had worse preoperative pain, function, and satisfaction (p < 0.001) and had greater improvements (i.e., journey scores) than those classified as Inappropriate (p < 0.001). At 6 months, destination scores for pain, function, or satisfaction were not significantly different across appropriateness categories. The percentage of patients meeting responder criteria (p < 0.001) and attaining MCID was statistically higher in Appropriate versus Inappropriate groups in pain (p = 0.04) and function (p = 0.004). CONCLUSIONS: The validity of our second-generation appropriateness system was generally supported. The findings highlight a critical issue in TKA healthcare: whether TKA appropriateness should be driven by the extent of improvement, by patient final state, or by both.

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study.

OBJECTIVE: To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Classification algorithms were used to reduce the dimensionality of texture data into a likelihood of subject belonging to the reference class. METHOD: T2 relaxation time mapping with multi-slice multi-echo spin echo sequence was performed for eighty symptomatic OA patients and 63 asymptomatic controls on a 3T clinical MRI scanner. Relaxation time maps were subjected to GLCM and LBP texture analysis, and classification algorithms were deployed with an in-house developed software. Implemented algorithms were K nearest neighbors, support vector machine, and neural network classifier. RESULTS: LBP and GLCM discerned OA patients from controls with a significant difference in all studied regions. Classification models comprising GLCM and LBP showed high accuracy in classing OA patients and controls. The best performance was obtained with a multilayer perceptron type classifier with an overall accuracy of 90.2 %. CONCLUSION: LBP texture analysis complements prior results with GLCM, and together LBP and GLCM serve as significant input data for classification algorithms trained for OA assessment. Presented algorithms are adaptable to versatile OA evaluations also for future gradational or predictive approaches.

Patients with lateral compartment knee osteoarthritis during arthroscopy are at highest risk of subsequent knee arthroplasty.

BACKGROUND: Arthroscopic treatment of knee osteoarthritis has declined, in part due to concerns with conversion to arthroplasty. Some studies have investigated the demographic predictors for conversion to arthroplasty, few have assessed the risk factors within the knee itself. Our aim was to analyse the demographics and anatomical wear features of a large cohort of patients undergoing knee arthroscopy. METHODS: A retrospective analysis of 1760 cases spanning over 17 years undergoing knee arthroscopy was performed. Patients were 36 years or older at time of the index arthroscopy. Each patient received the International Cartilage Regeneration and Joint Preservation Society (ICRS) grade of all regions as well an estimate of the remaining meniscal percentage. Demographic factors as well as intraoperatively collected data were analysed as predictive variables for subsequent conversion to arthroplasty using a multi-step Cox regression analysis. RESULTS: A total of 102 patients (6.2%) were converted to arthroplasty. Age at arthroscopy (hazard ratio (HR) 1.073; 95% confidence interval (CI) 1.058-1.088) and ICRS grade of the lateral tibial plateau (HR 1.166; 95% CI 1.066-1.276) were statistically significant predicting variables for conversion to arthroplasty. CONCLUSIONS: The results of this study indicate that higher ICRS grade of the lateral tibial plateau at arthroscopy is the most significant predictor for conversion to knee arthroplasty, with a hazard equal to an increase in age. The absence of these factors does not justify arthroscopic treatment of patients with knee osteoarthritis. LEVEL OF EVIDENCE: III.

Validation of the Oxford Knee Score and Lower Extremity Functional Score questionnaires for use in Slovenia.

BACKGROUND: With the increasing prevalence of total knee replacement (TKR) due to knee osteoarthritis, the absence of patient-reported outcome measures in Slovenia must be addressed. QUESTIONS/PURPOSES: (1) We cross-culturally adapted Oxford Knee Score (OKS) and Lower Extremity Functional Scale (LEFS) questionnaires to the Slovenian-speaking community. (2) We evaluated OKS and LEFS psychometric characteristics. PATIENTS AND METHODS: In the first assessment (Time 1) Slovenian versions of both questionnaires (OKS-Slo and LEFS-Slo), knee pain, timed-up to go (TUG) and sit to stand (STS) tests were completed by 123 subjects (55% females), of which 78 were patients scheduled for TKR and 45 were healthy age-matched controls. The questionnaires were assessed one week apart (Time 2) to investigate the test-retest reliability, with 121 subjects (98.4%) completing second measurements. RESULTS: Significant differences were observed between the two groups. Where patients had greater body mass index, they were slower in TUG, weaker in STS, had greater knee pain in both knees and scored lower on both questionnaires. Additionally, correlation analysis showed that OKS-Slo and LEFS-Slo correlated almost perfectly (correlation coefficient [CC] = .968, p < 0.001). Excellent negative correlations were observed with TUG (OKS-Slo/CC = - 0.679, p < 0.001; LEFS-Slo/CC = - 0.692, p < 0.001) and STS (OKS-Slo/CC = 0.790, p < 0.001; LEFS-Slo/CC = 0.815, p < 0.001) tests, while knee pain of affected leg correlated the most (OKS-Slo/CC = - 0.923, p < 0.001; LEFS-Slo/CC = - 0.915, p < 0.001). The Cronbach's alpha coefficient for both the OKS-Slo and LEFS-Slo ranged between 0.87 and 0.99, while the interclass correlation coefficient was excellent; i.e., 0.99. Finally, both questionnaires proved to be unidimensional measures. CONCLUSION: The Slovenian version of both questionnaires is feasible, valid and reliable for use in clinical studies including the older adult population in Slovenia. LEVEL OF EVIDENCE: Level III, Diagnostic-case-control study.

Correlation between radiographic knee osteoarthritis and lifetime cigarette smoking amount in a Korean population: A cross-sectional study.

Although the inverse correlation between smoking and degenerative arthritis is controversial, quantitative analysis of the correlation between lifetime cigarette smoking amount and degenerative arthritis has not been performed. We investigated the correlation between knee radiographic osteoarthritis (ROA) and lifetime cigarette smoking amount in the general population.This cross-sectional study used the Fifth and Sixth Korean National Health and Nutrition Examination Survey (2010-2013) data. Subjects included 11,638 community-dwelling adults aged ≥50 years. Knee ROA was defined as a Kellgren/Lawrence grade ≥2 on plain radiography. Lifetime cigarette smoking amount was calculated in terms of pack-year and further divided into quartile groups. Independent correlation between smoking and knee ROA was determined using odds ratios (OR) adjusted for age, sex, obesity, physical activity, and household income on multivariate logistic regression analysis.Knee ROA prevalence was 37.3%; prevalence of lifetime cigarette smokers was 26.0%. Subjects with knee ROA had higher mean age, female sex ratio, and body mass index but lower physical activity level. The adjusted logistic regression model revealed that female sex (OR, 2.110; 95% confidence interval [CI], 1.895-2.349) was significantly associated with knee ROA. Older age, obesity, and lower household income were positively correlated with knee ROA. Second-and fourth-quartile groups of smokers had the lower ROA prevalence than never-smokers (OR, 0.800; 95% CI, 0.643-0.99; OR, 0.812; 95% CI, 0.684-0.965, respectively).An inverse correlation with knee ROA was confirmed in mid-light to heavy smokers. Prospective studies are needed to reveal whether knee ROA involves smoking.

Kellgren-Lawrence grade of osteoarthritis is associated with change in certain morphological parameters.

BACKGROUND: To compare selected morphological parameters between normal and osteoarthritic (OA) knees, as well as to evaluate differences in these parameters between Kellgren-Lawrence (K-L) grades of OA. METHODS: Knee joint morphology was evaluated using magnetic resonance (MR) images of 200 participants with knee OA (50 each of K-L grades 1-4) and 50 without knee OA, matched for age, body mass index, sex, and tibiofemoral angle. Knees with a coronal alignment within five degrees of neutral and no apparent bone loss on radiographs were included. Twenty-one morphologic parameters of the distal femur and proximal tibia were measured on MR images. Correlation between the K-L grade and measured parameters and differences in measured parameters across the K-L grades and between the OA and control groups were evaluated. RESULTS: The K-L grade was significantly correlated with multiple distal femur measurements including the posterior condylar angle (PCA), lateral epicondyle to posterior condylar cartilage (LEPC) length, medial epicondyle to posterior condylar cartilage (MEPC) length, medial epicondyle to distal cartilage (MEDC) length, medial tibial slope angle, femoral condylar cartilage height difference (FCHDc), and femoral condylar bone height difference (FCHDb) (P < 0.05). A significant difference was identified between the different K-L grades with regard to PCA, LEPC, MEPC, MEDC, and FCHDc (P < 0.05). There was no correlation between K-L grade and measured proximal tibial parameters. CONCLUSIONS: Among knees without significant angular deformity, progression of knee OA is associated with a change in the morphology of the femoral condyles but not of the proximal tibia.

Association of knee OA structural phenotypes to risk for progression: a secondary analysis from the Foundation for National Institutes of Health Osteoarthritis Biomarkers study (FNIH).

PURPOSE: Aim was to stratify the knee MRIs of the Foundation for National Institutes of Health Osteoarthritis Biomarkers Consortium (FNIH) cohort into distinct structural phenotypes based on semiquantitative assessment and to determine risk for pain and structural progression over 48 months. METHODS: The study sample from the FNIH project was selected as a nested case-control study with knees showing either 1) radiographic and pain progression (i.e., "composite" cases), 2) radiographic progression only ("JSL"), 3) pain progression only, and 4) neither radiographic nor pain progression. MRI was performed on 3T systems. MRIs were read according to the MOAKS scoring system. Knees were stratified into subchondral bone, cartilage/meniscus and inflammatory phenotypes using the baseline visits. The relation of each phenotype to risk of being in the combined JSL plus composite outcome or composite case only group compared to those not having that phenotype was determined using logistic regression. Only KL2 and 3 and those without root tears were included. RESULTS: 485 knees were included. 362 (75%) did not have any phenotype, while 95 (20%) had the bone phenotype, 22 (5%) the cartilage/meniscus phenotype and 19 (4%) the inflammatory phenotype. The bone phenotype was associated with a higher odds of the combined JSL plus composite outcome and composite outcome only (OR 1.81; [95%CI 1.14,2.85] and 1.65; 95%CI [1.04,2.61]) while the inflammatory (OR 0.96 [95%CI 0.38,2.42] and 1.25; 95%CI [0.48,3.25]) and the cartilage/meniscus phenotypes were not significantly associated with outcome (OR 1.30 95%CI [0.55,3.07] and 0.99; 95%CI [0.40,2,49]). CONCLUSIONS: The bone phenotype was associated with increased risk of having both radiographic and pain progression. Phenotypic stratification may be useful to consider when selecting patients for inclusion in clinical trials.

Correlations between radiological classification systems and confirmed cartilage loss in severe knee osteoarthritis.

AIMS: Although knee osteoarthritis (OA) is diagnosed and monitored radiologically, actual full-thickness cartilage loss (FTCL) has rarely been correlated with radiological classification. This study aims to analyze which classification system correlates best with FTCL and to assess their reliability. METHODS: A prospective study of 300 consecutive patients undergoing unilateral total knee arthroplasty (TKA) for OA (mean age 69 years (44 to 91; standard deviation (SD) 9.5), 178 (59%) female). Two blinded examiners independently graded preoperative radiographs using five common systems: Kellgren-Lawrence (KL); International Knee Documentation Committee (IKDC); Fairbank; Brandt; and Ahlbäck. Interobserver agreement was assessed using the intraclass correlation coefficient (ICC). Intraoperatively, anterior cruciate ligament (ACL) status and the presence of FTCL in 16 regions of interest were recorded. Radiological classification and FTCL were correlated using the Spearman correlation coefficient. RESULTS: Knees had a mean of 6.8 regions of FTCL (SD 3.1), most common medially. The commonest patterns of FTCL were medial ± patellofemoral (143/300, 48%) and tricompartmental (89/300, 30%). ACL status was associated with pattern of FTCL (p = 0.023). All radiological classification systems demonstrated moderate ICC, but this was highest for the IKDC: whole knee 0.68 (95% confidence interval (CI) 0.60 to 0.74); medial compartment 0.84 (95% CI 0.80 to 0.87); and lateral compartment 0.79 (95% CI 0.73 to 0.83). Correlation with actual FTCL was strongest for Ahlbäck (Spearman rho 0.27 to 0.39) and KL (0.30 to 0.33) systems, although all systems demonstrated medium correlation. The Ahlbäck score was the most discriminating in severe knee OA. Osteophyte presence in the medial compartment had high positive predictive value (PPV) for FTCL, but not in the lateral compartment. CONCLUSION: The Ahlbäck and KL systems had the highest correlation with confirmed cartilage loss at TKA. However, the IKDC system displayed the best interobserver reliability, with favourable correlation with FTCL in medial and lateral compartments, although it was less discriminating in more severe disease. Cite this article: Bone Joint J 2020;102-B(3):301-309.

Knee Osteoarthritis Grade does not Correlate with Quadriceps Muscle Strength or Bone Properties of the Calcaneus in Men Aged 80 Years or More who Can Walk independently.

Purpose: Muscle weakness and bone deterioration in the elderly are related to falls and fractures, resulting in decreased mobility. Knee osteoarthritis also may contribute to falls and fractures and thereby affect mortality rates. The Kellgren-Lawrence (KL) classification is widely used in the radiographic evaluation of knee osteoarthritis. Aims: This study aimed to evaluate the quadriceps strength and bone properties of the calcaneus for each KL grade, and to clarify the impact of knee osteoarthritis grade on quadriceps strength and bone properties. Methods: This prospective cross-sectional study included data on 108 male patients (213 knees), aged ≥80 years, who could walk independently. A handheld dynamometer was used to measure quadriceps strength. Bone properties were evaluated using broadband ultrasound attenuation with a portable bone densitometer. Weight-bearing standing knee radiographs were evaluated using KL classification. Quadriceps strength and bone properties were evaluated for each KL grade and the correlations between the grade and quadriceps strength and bone properties were assessed simultaneously. Results: The numbers of participants in KL grades I-IV were 46, 102, 45, and 20, respectively. There were no differences among grades for either quadriceps strength or bone properties. Conclusions: Participants exhibited good quadriceps strength and bone properties regardless of their KL grade. Relatively high mechanical loading of muscle and bone incurred while walking independently, likely explaining this result. Clinically, this study demonstrated the absence of correlations between KL grade and quadriceps strength and bone properties, as was previously reported in studies showing the absence of a correlation between KL grade and pain.

Comparison of three sets of clinical classification criteria for knee osteoarthritis: a cross-sectional study of 13,459 patients treated in primary care.

OBJECTIVE: To determine and compare the proportion of patients treated in a primary care setting that have knee osteoarthritis (OA) according to the European League Against Rheumatism (EULAR), the American College of Rheumatology (ACR), and the National Institute for Health and Care Excellence (NICE) clinical classification criteria. DESIGN: A cross-sectional analysis of baseline data from 13,459 patients with knee symptoms or functional limitations associated with OA participating in the Good Life with osteoArthritis in Denmark (GLA:D®) treatment program. The prevalence of knee OA according to the EULAR, ACR and NICE criteria were calculated in all participants and in the subgroup of patients with self-reported radiographic changes associated with knee OA (n = 10,651 or 79%). RESULTS: Mean age (SD) was 65.3 (9.8) years, median (IQR) duration of symptoms 12 (6-36) months and mean pain intensity (0-100; SD) 46.5 (22.1) mm. 39% fulfilled all three sets of criteria. 48%, 52% and 89% fulfilled the EULAR, ACR, and NICE criteria for having knee OA, respectively. In the subgroup with self-reported radiographic changes, the corresponding numbers were 49%, 54% and 90%. CONCLUSIONS: While the EULAR and ACR criteria only identified around half of those, with or without self-reported radiographic knee OA, that were treated because of symptoms or functional limitations associated with knee OA, the NICE criteria identified most patients. The results indicate that the NICE criteria are relevant and appropriate to identify individuals treated for knee OA in primary care.

A Microarray Study of Articular Cartilage in Relation to Obesity and Severity of Knee Osteoarthritis.

OBJECTIVE: To query the transcript-level changes in the medial and lateral tibial plateau cartilage in tandem with obesity in patients with end-stage osteoarthritis (OA). DESIGN: Cartilage was obtained from 23 patients (20 obese [body mass index > 30 kg/m2], 3 overweight [body mass index < 30 kg/m2]) at the time of total knee replacement. Cartilage integrity was assessed using Outerbridge scale, while radiographic changes were scored on preoperative X-rays using Kellgren-Lawrence (K-L) classification. RNA was probed for differentially expressed transcripts between medial and lateral compartments using Affymetrix Gene 2.0 ST Array and validated via real-time polymerase chain reaction. Gene ontology and pathway analyses were also queried. RESULTS: Scoring of cartilage integrity by the Outerbridge scale indicated that the medial and lateral compartments were similar, while scoring by the K-L classification indicated that the medial compartment was more severely damaged than the lateral compartment. We observed a distinct transcript profile with >50% of transcripts unique between medial and lateral compartments. MMP13 and COL2A1 were more highly expressed in medial versus lateral compartment. Polymerase chain reaction confirmed expression of 4 differentially expressed transcripts. Numerous transcripts, biological processes, and pathways were significantly different between overweight and obese patients with a differential response of obesity on medial and lateral compartments. CONCLUSIONS: Our findings support molecular differences between medial and lateral compartments reflective of the greater severity of OA in the medial compartment. The K-L system better reflected the molecular results than did the Outerbridge. Moreover, the molecular effect of obesity was different between the medial and lateral compartments of the same knee plausibly reflecting the molecular effects of differential biomechanical loading.

MRI-based screening for structural definition of eligibility in clinical DMOAD trials: Rapid OsteoArthritis MRI Eligibility Score (ROAMES).

PURPOSE: Our aim was to introduce a simplified MRI instrument, Rapid OsteoArthritis MRI Eligibility Score (ROAMES), for defining structural eligibility of patients for inclusion in disease-modifying osteoarthritis drug trials using a tri-compartmental anatomic approach that enables stratification of knees into different structural phenotypes and includes diagnoses of exclusion. We also aimed to define overlap between phenotypes and determine reliability. METHODS: 50 knees from the Foundation for National Institutes of Health Osteoarthritis Biomarkers study, a nested case-control study within the Osteoarthritis Initiative, were selected within pre-defined definitions of phenotypes as either inflammatory, subchondral bone, meniscus/cartilage, atrophic or hypertrophic. A focused scoring instrument was developed covering cartilage, meniscal damage, inflammation and osteophytes. Diagnoses of exclusion were meniscal root tears, osteonecrosis, subchondral insufficiency fracture, tumors, malignant marrow infiltration and acute traumatic changes. Reliability was determined using weighted kappa statistics. Descriptive statistics were used for determining concordance between the a priori phenotypic definition and ROAMES and overlap between phenotypes. RESULTS: ROAMES identified 43 of 50 (86%) pre-defined phenotypes correctly. Of the 50 participants, 27 (54%) had no additional phenotypes other than the pre-defined phenotype. 18 (36%) had one and 5 (10%) had two additional phenotypes. None had three or four additional phenotypes. All features of ROAMES showed almost perfect agreement. One case with osteonecrosis and one with a tumor were detected. CONCLUSIONS: ROAMES is able to screen and stratify potentially eligible knees into different structural phenotypes and record relevant diagnoses of exclusion. Reliability of the instrument showed almost perfect agreement.

Automated detection & classification of knee arthroplasty using deep learning.

BACKGROUND: Preoperative identification of knee arthroplasty is important for planning revision surgery. However, up to 10% of implants are not identified prior to surgery. The purposes of this study were to develop and test the performance of a deep learning system (DLS) for the automated radiographic 1) identification of the presence or absence of a total knee arthroplasty (TKA); 2) classification of TKA vs. unicompartmental knee arthroplasty (UKA); and 3) differentiation between two different primary TKA models. METHOD: We collected 237 anteroposterior (AP) knee radiographs with equal proportions of native knees, TKA, and UKA and 274 AP knee radiographs with equal proportions of two TKA models. Data augmentation was used to increase the number of images for deep convolutional neural network (DCNN) training. A DLS based on DCNNs was trained on these images. Receiver operating characteristic (ROC) curves with area under the curve (AUC) were generated. Heatmaps were created using class activation mapping (CAM) to identify image features most important for DCNN decision-making. RESULTS: DCNNs trained to detect TKA and distinguish between TKA and UKA both achieved AUC of 1. Heatmaps demonstrated appropriate emphasis of arthroplasty components in decision-making. The DCNN trained to distinguish between the two TKA models achieved AUC of 1. Heatmaps showed emphasis of specific unique features of the TKA model designs, such as the femoral component anterior flange shape. CONCLUSIONS: DCNNs can accurately identify presence of TKA and distinguish between specific arthroplasty designs. This proof-of-concept could be applied towards identifying other prosthesis models and prosthesis-related complications.