Evaluation of separate quantitative radiographic features adds to the prediction of incident radiographic osteoarthritis in individuals with recent onset of knee pain: 5-year follow-up in the CHECK cohort

Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren-Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models' performance were not possible due to the lack of a unique definition of each of the three outcomes.

Prediction Models to Estimate the Future Risk of Osteoarthritis in the General Population: A Systematic Review

OBJECTIVE

To evaluate the performance and applicability of multivariable prediction models for osteoarthritis (OA).

METHODS

This was a systematic review and narrative synthesis using 3 databases (EMBASE, PubMed, and Web of Science) from inception to December 2021. We included general population longitudinal studies reporting derivation, comparison, or validation of multivariable models to predict individual risk of OA incidence, defined by recognized clinical or imaging criteria. We excluded studies reporting prevalent OA and joint arthroplasty outcome. Paired reviewers independently performed article selection, data extraction, and risk-of-bias assessment. Model performance, calibration, and retained predictors were summarized.

RESULTS

A total of 26 studies were included, reporting 31 final multivariable prediction models for incident knee (23), hip (4), hand (3) and any-site OA (1), with a median of 121.5 (range 27-12,803) outcome events, a median prediction horizon of 8 years (range 2-41), and a median of 6 predictors (range 3-24). Age, body mass index, previous injury, and occupational exposures were among the most commonly included predictors. Model discrimination after validation was generally acceptable to excellent (area under the curve = 0.70-0.85). Either internal or external validation processes were used in most models, although the risk of bias was often judged to be high with limited applicability to mass application in diverse populations.

CONCLUSION

Despite growing interest in multivariable prediction models for incident OA, focus remains predominantly on the knee, with reliance on data from a small pool of appropriate cohort data sets, and concerns over general population applicability.

Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort: prevalence and phenotyping

Objectives

Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component.

Methods

Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component.

Results

OA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different.

Conclusions

OA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments.

Trial registration number

The study is registered under clinicaltrials.gov nr: NCT03883568.

Performance of knee image digital analysis of radiographs of patients with end-stage knee osteoarthritis

OBJECTIVE

Knee Image Digital Analysis (KIDA) is standardized radiographic analysis software for measuring osteoarthritis (OA) characteristics. It was validated in mild OA, but used for severe OA as well. The current goal was to evaluate the performance of KIDA in severe OA.

DESIGN

Of 103 patients, standardized radiographs were performed before and one and 2 years after treatment for severe OA. All radiographs were evaluated on subchondral bone density, joint space width (JSW), osteophytes, eminence height, and joint angle, twice within years by the same observer. Part of the radiographs were randomly selected for reevaluation twice within 1 month and evaluation by another observer. The intraclass correlation coefficient (ICC), smallest detectable difference (SDD) and coefficient of variation (CV) were calculated; the SDD and CV were compared to those in mild OA. The relation of severity with KIDA parameters and with observer differences was calculated with linear regression.

RESULTS

Intra-observer ICCs were higher in the 98 severe radiographs reanalyzed within 1 month (all >0.8) than the 293 reanalyzed within years (all >0.5; most >0.8) and than inter-observer ICCs (all >0.7). SDDs and CVs were smaller when reanalyzed within a month and comparable to those in mild OA. Some parameters showed bias between readings. Severity showed significant relation with osteophytes and JSW parameters, and with the observer variation in these parameters (all P < 0.04).

CONCLUSIONS

KIDA is a well-performing tool also for severe OA. In order to decrease variability and SDDs, images should be analyzed in a limited time frame and randomized order.

Diagnosis of early stage knee osteoarthritis based on early clinical course: data from the CHECK cohort

Background

Early diagnosis of knee osteoarthritis (OA) is important in managing this disease, but such an early diagnostic tool is still lacking in clinical practice. The purpose of this study was to develop diagnostic models for early stage knee OA based on the first 2-year clinical course after the patient’s initial presentation in primary care and to identify whether these course factors had additive discriminative value over baseline factors.

Methods

We extracted eligible patients’ clinical and radiographic data from the CHECK cohort and formed the first 2-year course factors according to the factors’ changes over the 2 years. Clinical expert consensus-based diagnosis, which was made via evaluating patients’ 5- to 10-year follow-up data, was used as the outcome factor. Four models were developed: model 1, included clinical course factors only; model 2, included clinical and radiographic course factors; model 3, clinical baseline factors + clinical course factors; and model 4, clinical and radiographic baseline factors + clinical and radiographic course factors. All the models were built by a generalized estimating equation with a backward selection method. Area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (CI) were calculated for assessing model discrimination. Delong’s method compared AUCs.

Results

Seven hundred sixty-one patients with 1185 symptomatic knees were included in this study. Thirty-seven percent knees were diagnosed as OA at follow-up. Model 1 contained 6 clinical course factors; model 2: 6 clinical and 3 radiographic course factors; model 3: 6 baseline clinical factors combined with 5 clinical course factors; and model 4: 4 clinical and 1 radiographic baseline factors combined with 5 clinical and 3 radiographic course factors. Model discriminations are as follows: model 1, AUC 0.70 (95% CI 0.67–0.74); model 2, 0.74 (95% CI 0.71–0.77); model 3, 0.77 (95% CI 0.74–0.80); and model 4, 0.80 (95% CI 0.77–0.82). AUCs of model 3 and model 4 were slightly but significantly higher than corresponding baseline-factor models (model 3 0.77 vs 0.75, p = 0.031; model 4 0.80 vs 0.76, p = 0.003).

Conclusions

Four diagnostic models were developed with “fair” to “good” discriminations. First 2-year course factors had additive discriminative value over baseline factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02598-5.

Relationship between motion, using the GaitSmartTM system, and radiographic knee osteoarthritis: an explorative analysis in the IMI-APPROACH cohort

Objectives

To assess underlying domains measured by GaitSmart^TMparameters and whether these are additional to established OA markers including patient reported outcome measures (PROMs) and radiographic parameters, and to evaluate if GaitSmart analysis is related to the presence and severity of radiographic knee OA.

Methods

GaitSmart analysis was performed during baseline visits of participants of the APPROACH cohort (n = 297). Principal component analyses (PCA) were performed to explore structure in relationships between GaitSmart parameters alone and in addition to radiographic parameters and PROMs. Logistic and linear regression analyses were performed to analyse the relationship of GaitSmart with the presence (Kellgren and Lawrence grade ≥2 in at least one knee) and severity of radiographic OA (ROA).

Results

Two hundred and eighty-four successful GaitSmart analyses were performed. The PCA identified five underlying GaitSmart domains. Radiographic parameters and PROMs formed additional domains indicating that GaitSmart largely measures separate concepts. Several GaitSmart domains were related to the presence of ROA as well as the severity of joint damage in addition to demographics and PROMs with an area under the receiver operating characteristic curve of 0.724 and explained variances (adjusted R²) of 0.107, 0.132 and 0.147 for minimum joint space width, osteophyte area and mean subchondral bone density, respectively.

Conclusions

GaitSmart analysis provides additional information over established OA outcomes. GaitSmart parameters are also associated with the presence of ROA and extent of radiographic severity over demographics and PROMS. These results indicate that Gaitsmart^TM may be an additional outcome measure for the evaluation of OA.

Classification, Categorization, and Algorithms for Articular Cartilage Defects

There is a critical unmet need in the clinical implementation of valid preventative and therapeutic strategies for patients with articular cartilage pathology based on the significant gap in understanding of the relationships between diagnostic data, disease progression, patient-related variables, and symptoms. In this article, the current state of classification and categorization for articular cartilage pathology is discussed with particular focus on machine learning methods and the authors propose a bedside-bench-bedside approach with highly quantitative techniques as a solution to these hurdles. Leveraging computational learning with available data toward articular cartilage pathology patient phenotyping holds promise for clinical research and will likely be an important tool to identify translational solutions into evidence-based clinical applications to benefit patients. Recommendations for successful implementation of these approaches include using standardized definitions of articular cartilage, to include characterization of depth, size, location, and number; using measurements that minimize subjectivity or validated patient-reported outcome measures; considering not just the articular cartilage pathology but the whole joint, and the patient perception and perspective. Application of this approach through a multistep process by a multidisciplinary team of clinicians and scientists holds promise for validating disease mechanism-based phenotypes toward clinically relevant understanding of articular cartilage pathology for evidence-based application to orthopaedic practice.

Statistical shape modelling provides a responsive measure of morphological change in knee osteoarthritis over 12 months

OBJECTIVES

Responsive biomarkers are needed to assess the progression of OA and their lack has hampered previous clinical trials. Statistical shape modelling (SSM) from radiographic images identifies those at greatest risk of fast-progression or joint replacement, but its sensitivity to change has not previously been measured. This study evaluates the responsiveness of SSM in knee OA in a 12-month observational study.

METHODS

A total of 109 people were recruited who had undergone knee radiographs in the previous 12 months, and were grouped based on severity of radiographic OA (Kellgren-Lawrence grading). An SSM was built from three dual-energy X-ray absorptiometry scans at 6-month intervals. Change-over-time and OA were assessed using generalized estimating equations, standardized response means (SRM) and reliable change indices.

RESULTS

Mode 1 showed typical features of radiographic OA and had a strong link with Kellgren-Lawrence grading but did not change significantly during the study. Mode 3 showed asymmetrical changes consistent with medial cartilage loss, osteophytes and joint malalignment, and was responsive to change, with a 12-month SRM of 0.63. The greatest change was observed in the moderate radiographic OA group (SRM 0.92) compared with the controls (SRM 0.21), and the reliable change index identified 14% of this group whose progression was clinically significant.

CONCLUSION

Shape changes linked the progression of osteophytosis with increasing malalignment within the joint. Modelling of the whole joint enabled quantification of change beyond the point where bone-to-bone contact has been made. The knee SSM is, therefore, a responsive biomarker for radiographic change in knees over 12 months.

Risk factors for pain and functional impairment in people with knee and hip osteoarthritis: a systematic review and meta-analysis

OBJECTIVE

To identify risk factors for pain and functional deterioration in people with knee and hip osteoarthritis (OA) to form the basis of a future 'stratification tool' for OA development or progression.

DESIGN

Systematic review and meta-analysis.

METHODS

An electronic search of the literature databases, Medline, Embase, CINAHL, and Web of Science (1990-February 2020), was conducted. Studies that identified risk factors for pain and functional deterioration to knee and hip OA were included. Where data and study heterogeneity permitted, meta-analyses presenting mean difference (MD) and ORs with corresponding 95% CIs were undertaken. Where this was not possible, a narrative analysis was undertaken. The Downs & Black tool assessed methodological quality of selected studies before data extraction. Pooled analysis outcomes were assessed and reported using the Grading of Reccomendation, Assessment, Development and Evaluation (GRADE) approach.

RESULTS

82 studies (41 810 participants) were included. On meta-analysis: there was moderate quality evidence that knee OA pain was associated with factors including: Kellgren and Lawrence≥2 (MD: 2.04, 95% CI 1.48 to 2.81; p<0.01), increasing age (MD: 1.46, 95% CI 0.26 to 2.66; p=0.02) and whole-organ MRI scoring method (WORMS) knee effusion score ≥1 (OR: 1.35, 95% CI 0.99 to 1.83; p=0.05). On narrative analysis: knee OA pain was associated with factors including WORMS meniscal damage ≥1 (OR: 1.83). Predictors of joint pain in hip OA were large acetabular bone marrow lesions (BML; OR: 5.23), chronic widespread pain (OR: 5.02) and large hip BMLs (OR: 4.43).

CONCLUSIONS

Our study identified risk factors for clinical pain in OA by imaging measures that can assist in predicting and stratifying people with knee/hip OA. A 'stratification tool' combining verified risk factors that we have identified would allow selective stratification based on pain and structural outcomes in OA.

PROSPERO REGISTRATION NUMBER

CRD42018117643.

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.

Machine-learning-based patient-specific prediction models for knee osteoarthritis

Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process. Comprehensive patient-specific prediction models need to be developed. Approaches such as data mining and machine learning should aid in the development of such models. Although a challenging task, technology is now available that should enable subgrouping of patients with OA and lead to improved clinical decision-making and precision medicine.

A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women

OBJECTIVE

Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low sensitivity. Therefore, there is a need for new biomarkers to correctly identify early knee OA.

METHOD

We have created an analytics pipeline based on machine learning to identify small models (having few variables) that predict the 30-months incidence of knee OA (using multiple clinical and structural OA outcome measures) in overweight middle-aged women without knee OA at baseline. The data included clinical variables, food and pain questionnaires, biochemical markers (BM) and imaging-based information.

RESULTS

All the models showed high performance (AUC > 0.7) while using only a few variables. We identified both the importance of each variable within the models as well its direction. Finally, we compared the performance of two models with the state-of-the-art approaches available in the literature.

CONCLUSIONS

We showed the potential of applying machine learning to generate predictive models for the knee OA incidence. Imaging-based information were found particularly important in the proposed models. Furthermore, our analysis confirmed the relevance of known BM for knee OA. Overall, we propose five highly predictive small models that can be possibly adopted for an early prediction of knee OA.

Progression of medial compartmental osteoarthritis 2-8 years after lateral closing-wedge high tibial osteotomy

PURPOSE

The primary purpose of this study is to investigate the progression of medial osteoarthritis (OA) following lateral closing-wedge high tibial osteotomy (HTO). Secondary outcomes included functional and pain scores.

METHODS

This prospective cohort study analysed 298 patients treated with lateral closing-wedge HTO surgery for medial compartmental OA. OA progression was measured by comparing the minimum joint space width (mJSW) and Kellgren-Lawrence (KL) score on radiographs preoperatively and postoperatively. The WOMAC score and NRS score for pain were obtained preoperatively and postoperatively to assess secondary outcomes. Failure was defined as revision surgery; survival was estimated.

RESULTS

Mean follow-up was 5.2 ± 1.8 years (range 2-8.5). Mean preoperative mJSW was 3.4 ± 1.6 mm, which changed nonsignificantly (p = 0.51) to 3.4 ± 1.7 mm postoperatively. Mean annual joint space narrowing was 0.02 ± 0.34 mm/year. Progression to 1 KL grade or more was seen in 132 (44 %) patients, and annual risk of KL progression was 8.6 %. No KL progression was seen in 56 % of patients. Mean NRS decreased from 7.3 ± 1.5 to 3.5 ± 2.5 (p < 0.001). WOMAC scores decreased from 48.0 ± 17.2 to 23.6 ± 19.7 (p < 0.001). Failure was seen in 21 patients.

CONCLUSION

Compared to demographic data in the literature, valgus high tibial osteotomy seems to reduce the progression of OA, reduces pain and improves knee function in patients with medial compartment OA and a varus alignment.

LEVEL OF EVIDENCE

III.

Knee joint distraction compared with high tibial osteotomy: a randomized controlled trial

PURPOSE

Both, knee joint distraction as a relatively new approach and valgus-producing opening-wedge high tibial osteotomy (HTO), are knee-preserving treatments for knee osteoarthritis (OA). The efficacy of knee joint distraction compared to HTO has not been reported.

METHODS

Sixty-nine patients with medial knee joint OA with a varus axis deviation of <10° were randomized to either knee joint distraction (n = 23) or HTO (n = 46). Questionnaires were assessed at baseline and 3, 6, and 12 months. Joint space width (JSW) as a surrogate measure for cartilage thickness was determined on standardized semi-flexed radiographs at baseline and 1-year follow-up.

RESULTS

All patient-reported outcome measures (PROMS) improved significantly over 1 year (at 1 year p < 0.02) in both groups. At 1 year, the HTO group showed slightly greater improvement in 4 of the 16 PROMS (p < 0.05). The minimum medial compartment JSW increased 0.8 ± 1.0 mm in the knee joint distraction group (p = 0.001) and 0.4 ± 0.5 mm in the HTO group (p < 0.001), with minimum JSW improvement in favour of knee joint distraction (p = 0.05). The lateral compartment showed a small increase in the knee joint distraction group and a small decrease in the HTO group, leading to a significant increase in mean JSW for knee joint distraction only (p < 0.02).

CONCLUSION

Cartilaginous repair activity, as indicated by JSW, and clinical outcome improvement occurred with both, knee joint distraction and HTO. These findings suggest that knee joint distraction may be an alternative therapy for medial compartmental OA with a limited mechanical leg malalignment.

LEVEL OF EVIDENCE

Randomized controlled trial, Level I.

Prediction of cartilaginous tissue repair after knee joint distraction

BACKGROUND

For young patients (<65years), knee joint distraction (KJD) may be a joint-saving treatment option for end-stage knee osteoarthritis. Distracting the femur from the tibia by five millimeters for six to eight weeks using an external fixation frame results in cartilaginous tissue repair, in addition to clinical benefits. This study is a first attempt to predict the degree of cartilaginous tissue repair after KJD.

METHODS

Fifty-seven consecutive patients received KJD. At baseline and at one year of follow-up, mean and minimum joint space width (JSW) of the most-affected compartment was determined on standardized radiographs. To evaluate the predictive ability of baseline characteristics for JSW at one year of follow-up, multivariable linear regression analysis was performed.

RESULTS

Mean JSW±SD of the most affected compartment increased by 0.95±1.23mm to 3.08±1.43mm at one year (P<0.001). The minimum JSW increased by 0.94±1.03mm to 1.63±1.21mm at one year of follow-up (P<0.001). For a larger mean JSW one year after KJD, only Kellgren & Lawrence grade (KLG) at baseline was predictive (Regression coefficient (β)=0.47, 95% CI=0.18 to 0.77, P=0.002). For a larger minimum JSW, KLG (β=0.46, 95% CI=0.19 to 0.73, P=0.001) and male gender (β=0.52, 95% CI=0.06 to 0.99, P=0.028) were statistically predictive. Eight weeks of distraction time neared significance (β=0.44, 95% CI=-0.05 to 0.93, P=0.080).

CONCLUSIONS

In our cohort of patients treated with KJD, males with higher KLG had the best chance of cartilaginous tissue repair by distraction.

The MRI-detected osteophyte score is a predictor for undergoing joint replacement in patients with end-stage knee osteoarthritis

OBJECTIVES

The aim of this prospective cohort study was to examine whether MRI-detected osteoarthritis (OA)-structural changes at baseline could predict knee OA patients who would undergo total knee arthroplasty (TKA).

METHODS

In total, 128 end-stage medial-type knee OA patients were enrolled and followed up for 6 months. MRI using the whole-organ MRI scoring (WORMS) method, radiographic findings, visual analog scale (VAS) for pain and a patient-oriented outcome measure, and the Japanese Knee Osteoarthritis Measure (JKOM) were recorded at baseline. The area under the curve (AUC) was estimated to determine the discriminative value of the prediction models.

RESULTS

While 74 patients (57.8%) did not undergo TKA, the remaining 54 patients (42.2%) underwent TKA during this period. The AUCs of the receiver operating characteristic (ROC) curve for the activities of daily living (ADL) score evaluated by the JKOM ADL score [0.70 (95% CI: 0.60-0.79)] and osteophyte score [0.72 (0.64-0.81)] were 0.70 or greater. The JKOM ADL score (17/40) and the osteophyte score (30/98) showed relative risks (RR) of 2.61 (1.32-5.15) and 3.01 (1.39-6.52) for undergoing TKA, respectively.

CONCLUSION

The osteophyte score detected by MRI, in addition to ADL score, was found to be an important factor in determining whether the patient should undergo TKA.

Alarmins S100A8/S100A9 aggravate osteophyte formation in experimental osteoarthritis and predict osteophyte progression in early human symptomatic osteoarthritis

OBJECTIVE

Alarmins S100A8 and S100A9 are major products of activated macrophages regulating cartilage damage and synovial activation during murine and human osteoarthritis (OA). In the current study, we investigated whether S100A8 and S100A9 are involved in osteophyte formation during experimental OA and whether S100A8/A9 predicts osteophyte progression in early human OA.

METHODS

OA was elicited in S100A9-/- mice in two experimental models that differ in degree of synovial activation. Osteophyte size, S100A8, S100A9 and VDIPEN neoepitope was measured histologically. Chondrogenesis was induced in murine mesenchymal stem cells in the presence of S100A8. Levels of S100A8/A9 were determined in plasma of early symptomatic OA participants of the Cohort Hip and Cohort Knee (CHECK) cohort study and osteophytes measured after 2 and 5 years.

RESULTS

Osteophyte size was drastically reduced in S100A9-/- mice in ligaments and at medial femur and tibia on days 21 and 42 of collagenase-induced OA, in which synovial activation is high. In contrast, osteophyte size was not reduced in S100A9-/- mice during destabilised medial meniscus OA, in which synovial activation is scant. S100A8 increased expression and activation of matrix metalloproteinases during micromass chondrogenesis, thereby possibly increasing cartilage matrix remodelling allowing for larger osteophytes. Interestingly, early symptomatic OA participants of the CHECK study with osteophyte progression after 2 and 5 years had elevated S100A8/A9 plasma levels at baseline, while C-reactive protein, erythrocyte sedimentation rate and cartilage oligomeric matrix protein were not elevated at baseline.

CONCLUSIONS

S100A8/A9 aggravate osteophyte formation in experimental OA with high synovial activation and may be used to predict osteophyte progression in early symptomatic human OA.

Validity and responsiveness of a new measure of knee osteophytes for osteoarthritis studies: data from the osteoarthritis initiative

PURPOSE

To validate a novel quantitative MRI method to measure osteophyte volume.

METHODS

90 subjects were selected from the Progression Cohort of the Osteoarthritis Initiative (OAI) at baseline and 48 months, and analyzed using a semi-automated software tool. Marginal osteophyte volume was calculated for four compartments of the central weight-bearing region of the tibiofemoral joint. Standardized response mean (SRM) for change in volume was used to quantify responsiveness. Concurrent validity was assessed via a comparison with MRI Osteoarthritis Knee Score (MOAKS) using Kruskal-Wallis analysis and Spearman's correlation coefficient. Intra- and inter-reader reliability was assessed on a subset of 20 knees using intra-class correlation coefficients (ICCs) and the root mean square standard deviation (RMSSD).

RESULTS

The average change in osteophyte volume (ΔV) was 196 mm(3) (SD = 272 mm(3)), and the baseline to 48-month SRM was 0.72. An increase in osteophyte volume was observed for 84% (76/90) of the subjects. Kruskal-Wallis analysis across the four MOAKS osteophyte categories was significant for medial and lateral compartments of both the tibia and femur (P < 0.001 for all). The intra-reader ICC was 0.98, and RMSSD was 82 mm(3), while inter-reader ICC was 0.97 and RMSSD was 91 mm(3). A statistically significant positive correlation was observed between osteophyte volume and several MOAKS cartilage and BML scores. The reader time was approximately 10 min per knee.

CONCLUSIONS

The method is responsive, efficient, and precise, making it practical for use in large cohort studies and observational research.

Knee joint subchondral bone structure alterations in active athletes: a cross-sectional case-control study

OBJECTIVE

It has been shown that trabecular bone structure parameters extracted from radiographs known as fractal signature analysis (FSA) are able to predict structural outcomes such as radiographic osteoarthritis (OA) progression. Little is known about their involvement in early disease or about differences between subjects exposed to increased joint loading such as young active athletes compared to non-athletes. Aim was to compare horizontal and vertical dimensions of bone texture considering athlete status, gender, previous anterior cruciate ligament (ACL) surgery and age.

DESIGN

Included were 685 patients of which 135 consecutive athletes (82% soccer players) 18-36 years old and 550 non-athletes controls in the same age range had knee radiography for assessment of subacute or chronic knee complaints. Regions of interest (ROI) were placed in the subchondral medial and lateral tibial plateaus. Fractal signatures were calculated in the horizontal and vertical dimensions. Curve fitting algorithms were applied taking into account all four risk factors in the same model adjusting for each other.

RESULTS

For the horizontal dimensions significant differences were observed for gender (estimate (E) 0.098 (95% confidence interval(CI)) (-0.009, 0.008), P < .0001), previous ACL surgery (E -0.031, 95% CI (-0.043, -0.019), P < .0001) and highest age group (E -0.039, 95% CI (-0.048, -0.029), P < .0001). For vertical dimensions, significant differences were shown for athletes (E -0.012, 95% CI (-0.020, -0.004), P < .0001), gender (E 0.056, 95% CI (0.049, 0.062), P < .0001), and age range from 28 to 32 years (E -0.028, 95% CI (-0.037, -0.019), P < .0001).

CONCLUSIONS

Trabecular bone structure differs between athletes and non-athletes, in regard to previous ACL surgery, gender and higher age.

Radiographic assessment of leg alignment and grading of knee osteoarthritis: A critical review

Knee osteoarthritis (OA) is a progressive joint disease hallmarked by cartilage and bone breakdown and associated with changes to all of the tissues in the joint, ultimately causing pain, stiffness, deformity and disability in many people. Radiographs are commonly used for the clinical assessment of knee OA incidence and progression, and to assess for risk factors. One risk factor for the incidence and progression of knee OA is malalignment of the lower extremities (LE). The hip-knee-ankle (HKA) angle, assessed from a full-length LE radiograph, is ideally used to assess LE alignment. Careful attention to LE positioning is necessary to obtain the most accurate measurement of the HKA angle. Since full-length LE radiographs are not always available, the femoral shaft - tibial shaft (FS-TS) angle may be calculated from a knee radiograph instead. However, the FS-TS angle is more variable than the HKA angle and it should be used with caution. Knee radiographs are used to assess the severity of knee OA and its progression. There are three types of ordinal grading scales for knee OA: global, composite and individual feature scales. Each grade on a global scale describes one or more features of knee OA. The entire description must be met for a specific grade to be assigned. The Kellgren-Lawrence scale is the most commonly-used global scale. Composite scales grade several features of knee OA individually and sum the grades to create a total score. One example is the compartmental grading scale for knee OA. Composite scales can respond to change in a variety of presentations of knee OA. Individual feature scales assess one or more OA features individually and do not calculate a total score. They are most often used to monitor change in one OA feature, commonly joint space narrowing. The most commonly-used individual feature scale is the OA Research Society International atlas. Each type of scale has its advantages; however, composite scales may offer greater content validity. Responsiveness to change is unknown for most scales and deserves further evaluation.

Toward a clinical definition of early osteoarthritis: onset of patient-reported knee pain begins on stairs. Data from the osteoarthritis initiative

Objective

Early detection of osteoarthritis (OA) would increase the chances of effective intervention. We aimed to investigate which patient-reported activity is first associated with knee pain. We hypothesized that pain would occur first during activities requiring weight bearing and knee bending.

Methods

Data were obtained from the Osteoarthritis Initiative (OAI), a multicenter, longitudinal prospective observational cohort of people who have or are at high risk of OA. Participants completed the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC; Likert scale) annually for up to 7 years. Rasch analysis was used to rank the WOMAC pain questions (activities) in order of affirmation as the pain score increased from 0. For each total WOMAC score category (0–20) we selected 25 individuals at random based on their maximum score across all visits. Fit to the Rasch model was assessed in this subset; stability of question ranking over successive visits was confirmed in the full OAI.

Results

WOMAC data on 4,673 people were included, with 491 selected for subset analysis. The subset data showed good fit to the Rasch model (χ² = 43.31, P = 0.332). In the full OAI, the “using stairs” question was the first to score points as the total pain score increased from 0 (baseline logit score ± 95% confidence interval −4.74 ± 0.07), then “walking” (−2.94 ± 0.07), “standing” (−2.65 ± 0.07), “lying/sitting” (−2.00 ± 0.08), and finally “in bed” (−1.32 ± 0.09). This ordering was consistent over successive visits.

Conclusion

Knee pain is most likely to first appear during weight-bearing activities involving bending of the knee, such as using stairs. First appearance of this symptom may identify a group suitable for early intervention strategies.

Increased risk for radiographic osteoarthritis features in young active athletes: a cross-sectional matched case-control study

OBJECTIVE

Prevalence data on radiographic osteoarthritis (ROA) in young active athletes is sparse. Aim was to assess in a matched case-control design the frequency of ROA in an athlete population and whether athlete status, gender, previous anterior cruciate ligament (ACL) surgery and age increase the odds for ROA.

DESIGN

135 consecutive athletes (82% soccer players) 18-36 years old and 550 non-athletes aged-matched controls had knee radiography (Lyon-Schuss protocol) for assessment of subacute or chronic knee complaints. Patients with acute trauma or fractures were excluded. Radiographs were graded according to the Kellgren-Lawrence and OARSI grading schemes. In addition, medial and lateral intercondylar notch osteophytes were scored. We used logistic regression model to assess the association of ROA and specific radiographic OA features with athlete status, prior ACL surgery, gender and age, adjusting for each other.

RESULTS

19.4% of patients were 18-22 years old, 26.4% were 23-27, 22.6% were 28-32, and 31.5% were 33-36 years old. 18.7% were female and 8.8% had previous ACL surgery. 8.5% had ROA and 6.0% had evidence of JSN. The adjusted odds ratios (aOR) for ROA were 2.8 (95% confidence interval 1.4, 5.5) for athletes, 7.0 (3.5, 13.9) for previous ACL surgery and 3.3 (1.2, 9.0) for age range 32-36. Athlete status significantly increased odds for tibiofemoral osteophytes [aOR 2.9 (1.6, 5.4)] and comparably for notch osteophytes [aOR 2.3 (1.1, 4.7)].

CONCLUSIONS

Athlete status, higher age and previous ACL surgery increase the risk of ROA with surgery being the strongest risk factor.

How to define responders in osteoarthritis

BACKGROUND

Osteoarthritis is a clinical syndrome of failure of the joint accompanied by varying degrees of joint pain, functional limitation, and reduced quality of life due to deterioration of articular cartilage and involvement of other joint structures.

SCOPE

Regulatory agencies require relevant clinical benefit on symptoms and structure modification for registration of a new therapy as a disease-modifying osteoarthritis drug (DMOAD). An international Working Group of the European Society on Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) and International Osteoporosis Foundation was convened to explore the current burden of osteoarthritis, review current regulatory guidelines for the conduct of clinical trials, and examine the concept of responder analyses for improving drug evaluation in osteoarthritis.

FINDINGS

The ESCEO considers that the major challenges in DMOAD development are the absence of a precise definition of the disease, particularly in the early stages, and the lack of consensus on how to detect structural changes and link them to clinically meaningful endpoints. Responder criteria should help identify progression of disease and be clinically meaningful. The ideal criterion should be sensitive to change over time and should predict disease progression and outcomes such as joint replacement.

CONCLUSION

The ESCEO considers that, for knee osteoarthritis, clinical trial data indicate that radiographic joint space narrowing >0.5 mm over 2 or 3 years might be a reliable surrogate measure for total joint replacement. On-going research using techniques such as magnetic resonance imaging and biochemical markers may allow the identification of these patients earlier in the disease process.

Identifying phenotypes of knee osteoarthritis by separate quantitative radiographic features may improve patient selection for more targeted treatment

OBJECTIVE

Expression of osteoarthritis (OA) varies significantly between individuals, and over time, suggesting the existence of different phenotypes, possibly with specific etiology and targets for treatment. Our objective was to identify phenotypes of progression of radiographic knee OA using separate quantitative features.

METHODS

Separate radiographic features of OA were measured by Knee Images Digital Analysis (KIDA) in individuals with early knee OA (the CHECK cohort: Cohort Hip & Cohort Knee), at baseline and at 2-year and 5-year followup. Hierarchical clustering was performed to identify phenotypes of radiographic knee OA progression. The phenotypes identified were compared for changes in joint space width (JSW), varus angle, osteophyte area, eminence height, bone density, for Kellgren-Lawrence (K-L) grade, and for clinical characteristics. Logistic regression analysis evaluated whether baseline radiographic features and demographic/clinical characteristics were associated with each of the specific phenotypes.

RESULTS

The 5 clusters identified were interpreted as "Severe" or "No," "Early" or "Late" progression of the radiographic features, or specific involvement of "Bone density." Medial JSW, varus angle, osteophyte area, eminence height, and bone density at baseline were associated with the Severe and Bone density phenotypes. Lesser eminence height and bone density were associated with Early and Late progression. Larger varus angle and smaller osteophyte area were associated with No progression.

CONCLUSION

Five phenotypes of radiographic progression of early knee OA were identified using separate quantitative features, which were associated with baseline radiographic features. Such phenotypes might require specific treatment and represent relevant subgroups for clinical trials.

Osteoarthritis: a review of strengths and weaknesses of different imaging options

Slowing of radiographic joint space narrowing represents the only recommended imaging-based outcome measure to assess structural disease progression in osteoarthritis (OA) clinical trials. There are no effective disease-modifying OA drugs. The ability of magnetic resonance (MR) to image structures within the knee and to visualize cartilage morphology and composition gives MR imaging a critical role in understanding the natural history of the disease and in the search for therapies. In this article, the roles and limitations of conventional radiography and MR imaging, focusing on knee OA, and the use of other modalities in clinical practice and OA research are described.

Quantitative radiographic features of early knee osteoarthritis: development over 5 years and relationship with symptoms in the CHECK cohort

OBJECTIVE

To evaluate whether computer-assisted, interactive digital analysis of knee radiographs enables identification of different quantitative features of joint damage, and to evaluate the relationship of such features with each other and with clinical characteristics during 5-year followup in early osteoarthritis (OA).

METHODS

Knee radiographs from the Cohort Hip and Cohort Knee (CHECK) study, including 1002 individuals with early OA complaints, were evaluated for different measures with knee images digital analysis (KIDA). To aid definition of different radiographic features of OA, principal component analysis of KIDA was used. Features were correlated (Pearson) to each other, evaluated for changes over time, and related to clinical outcome (Western Ontario and McMaster Universities Osteoarthritis Index for pain and function) using baseline, 2-year, and 5-year followup data.

RESULTS

The identified radiographic features were joint space width (JSW: minimum, medial, lateral), varus angle, osteophyte area, eminence height, and bone density. The features progressed in severity at different times during followup: early (medial JSW, osteophyte area), late (minimum and lateral JSW, eminence height), and both early and late (varus angle, bone density). Correlations between different radiographic features varied between timepoints. The JSW features were most strongly related to each other (largest r = 0.82), but also, e.g., osteophytes and bone density were correlated (largest r = 0.33). The relationships with clinical outcome varied over time, but were most commonly found for osteophyte area and JSW.

CONCLUSION

In this early OA cohort, different radiographic features were identified that progressed at different rates between timepoints. The relations between radiographic features and with clinical outcome varied over time. This implies that longitudinal evaluation of different features can improve insight into progression of OA.