The natural history of marginal erosions in hands with and without osteoarthritis.

INTRODUCTION/OBJECTIVES: Among people with or without hand osteoarthritis, we aimed to identify characteristics of people (e.g., age and gender) with marginal erosions (MEs). We also examined changes in MEs during 48 months. We described radiographic severity and progression among joints with MEs, changes in MEs, or central erosions (CEs). DESIGN: We studied participants from the Osteoarthritis Initiative with baseline and 48-month hand radiographs. A radiologist and rheumatologist evaluated the radiographs for disease severity (Kellgren-Lawrence grades) and erosions (central or marginal), respectively. We used descriptive statistics to characterize participants and calculated frequencies at the joint level. RESULTS: Of the 3558 participants, 89 had a ME at baseline. People with MEs were more often male, older, and ever (former and current) smokers than those without a ME. There was no difference in inflammatory biomarkers or the presence of hand pain between individuals with and without a baseline ME. Almost all hands had only one ME (80%), whereas only 50% of individuals with CEs had only one CE at baseline. Compared to CEs, MEs appeared more frequently in joints without osteoarthritis (54% vs. < 1%). Approximately 18% of joints with an ME progressed in Kellgren-Lawrence grade versus 4% without ME. Among the joints with an ME at baseline, 10% resolved by 48 months. Less than 0.1% of joints developed a new ME. CONCLUSION: MEs appear to be distinct from CEs. MEs are predominantly present in males, isolated to one specific joint without osteoarthritis within a hand, and possibly predictive of radiographic progression. Key Points • Marginal erosions appear to be distinct from central erosions. • Most marginal erosions occur in joints without radiographic osteoarthritis. • Radiographic progression but not hand pain is more common in joints with a marginal erosion than without one.

Severity and progression of structural hand OA is not associated with progression of structural knee OA: The IMI-APPROACH cohort.

Objective: To investigate whether structural hand OA or its progression is associated with structural knee OA progression after two years in a population with symptomatic knee OA. Methods: We used baseline and two-year follow-up data from the IMI-APPROACH cohort. Symptomatic hand and knee OA were defined using ACR criteria. Radiographs of hands and knees were scored semi-quantitatively for osteophytes and joint space narrowing (JSN) following the OARSI atlas, and Kellgren-Lawrence (KL) scale. Knee images were also scored quantitatively with the Knee Image Digital Analysis (KIDA). Progression was defined as change above the minimal detectable change on patient level, except for KIDA (most affected knee compartment level). With logistic regression analyses the severity or progression of hand OA was associated with knee OA progression. Results: In 221 participants (mean age 66, 77% women, mean BMI 27.7, 19% hand OA), OA progression occurred in 18%-28%, and 9%-38% in hands and knees respectively, depending on features. Baseline structural hand OA features were not significantly associated with knee OA progression, except for hand osteophytes with KIDA osteophytes progression (odds ratio (OR) 1.03, 95% confidence interval (CI) 1.01-1.06). Progression of structural hand OA features was not significantly associated with knee OA progression, except for hand osteophyte or JSN progression, which was significantly associated with knee osteophyte progression (OR 0.44, 95%CI 0.22-0.84 and OR 0.43, 95%CI 0.18-0.94, respectively), and hand osteophyte progression for knee JSN (OR 2.51, 95%CI 1.15-5.48). Conclusions: In patients with symptomatic knee OA, no consistent associations between baseline structural hand OA or hand OA progression and knee OA progression were shown.

Hand osteoarthritis phenotypes based on a biopsychosocial approach, and their associations with cross-sectional and longitudinal pain.

OBJECTIVE: Hand osteoarthritis (OA) pain is characterized as heterogeneous and multifactorial. Differences in pain may be explained by underlying phenotypes, which have not been previously explored DESIGN: Latent class analysis determined classes of participants with hand OA from the Nor-Hand study baseline examination (2016-17) based on a biopsychosocial framework. Outcomes were hand and overall bodily pain intensity (Numeric Rating Scale, 0-10) at baseline and follow-up (2019-21), The relations of the classes to pain outcomes at baseline, follow-up, and change over time were analysed in separate models by linear regression, using the overall healthiest class as reference. RESULTS: Five classes differing in radiographic hand OA burden and OA burden in the lower extremities by ultrasound, demographic factors, psychosocial burden and pain sensitization was identified. Persons with the least severe OA but higher burden of biopsychosocial factors reported the most hand pain (beta 3.65, 95% CI 2.53, 4.75). Pain was less pronounced in persons with the most severe hand OA but low burden of biopsychosocial factors (beta 1.03, 95% CI 0.41, 1.65). Results were similar for overall bodily pain and at follow-up. Changes in pain were small, but the association between a separate class defined by higher levels of biopsychosocial burden and pain changes was significant. CONCLUSION: The five hand OA phenotypes were associated with pain at baseline and 3.5 years later. The phenotype with the least OA severity, but higher burden of biopsychosocial factors reported more pain than the phenotype with the most severe OA, reflecting the symptom-structure discordance of the hand OA pain experience.

2023 EULAR classification criteria for hand osteoarthritis.

OBJECTIVES: The objective of this study is to develop classification criteria for overall hand osteoarthritis (OA), interphalangeal OA and thumb base OA based on self-reported data and radiographic features. METHODS: The classification criteria sets were developed in three phases. In phase 1, we identified criteria that discriminated hand OA from controls. In phase 2, we used a consensus-based decision analysis approach to derive a clinician-based evaluation of the relative importance of the criteria. In phase 3, we refined the scoring system, determined the cut-offs for disease classification and compared the sensitivity and specificity of the European Alliance of Associations for Rheumatology (EULAR) criteria with the 1990 American College of Rheumatology (ACR) criteria. RESULTS: In persons with hand symptoms and no other disease (including psoriasis) or acute injury that can explain the hand symptoms (mandatory criteria), hand OA can be classified based on age, duration of morning stiffness, number of joints with osteophytes and joint space narrowing, and concordance between symptoms and radiographic findings. Using a sum of scores based on each diagnostic element, overall hand OA can be classified if a person achieves 9 or more points on a 0-15 scale. The cut-off for interphalangeal OA and thumb base OA is 8 points. While the EULAR criteria demonstrated better sensitivity than the ACR criteria in the phase 1 data set, the performance of the two criteria sets was similar in two external cohorts. CONCLUSIONS: International experts developed the EULAR criteria to classify overall hand OA, interphalangeal OA and thumb base OA in clinical studies using a rigorous methodology.

Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data, and Symptomatic Assessments.

OBJECTIVE: In this study, we propose a novel framework that utilizes deep learning and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of 7 years. MATERIAL AND METHODS: This study included subjects (1,832 subjects, 3,276 knees) from the baseline of the Multicenter Osteoarthritis Study (MOST). Patellofemoral joint regions of interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. An end-to-end deep learning method was developed for predicting PFOA progression based on imaging data in a five-fold cross-validation setting. To evaluate the performance of the models, a set of baselines based on known risk factors were developed and analyzed using gradient boosting machine (GBM). Risk factors included age, sex, body mass index, and Western Ontario and McMaster Universities Arthritis Index score, and the radiographic osteoarthritis stage of the tibiofemoral joint (Kellgren and Lawrence [KL] score). Finally, to increase predictive power, we trained an ensemble model using both imaging and clinical data. RESULTS: Among the individual models, the performance of our deep convolutional neural network attention model achieved the best performance with an area under the receiver operating characteristic curve (AUC) of 0.856 and average precision (AP) of 0.431, slightly outperforming the deep learning approach without attention (AUC = 0.832, AP = 0.4) and the best performing reference GBM model (AUC = 0.767, AP = 0.334). The inclusion of imaging data and clinical variables in an ensemble model allowed statistically more powerful prediction of PFOA progression (AUC = 0.865, AP = 0.447), although the clinical significance of this minor performance gain remains unknown. The spatial attention module improved the predictive performance of the backbone model, and the visual interpretation of attention maps focused on the joint space and the regions where osteophytes typically occur. CONCLUSION: This study demonstrated the potential of machine learning models to predict the progression of PFOA using imaging and clinical variables. These models could be used to identify patients who are at high risk of progression and prioritize them for new treatments. However, even though the accuracy of the models were excellent in this study using the MOST dataset, they should be still validated using external patient cohorts in the future.

Associations of inflammatory and metabolic biomarkers with incident erosive hand osteoarthritis in the osteoarthritis initiative cohort.

OBJECTIVE: Erosive hand osteoarthritis (eHOA) is a subtype of hand osteoarthritis (OA) that develops in finger joints with pre-existing OA and is differentiated by clinical characteristics (hand pain/disability, inflammation, and erosions) that suggest inflammatory or metabolic processes. METHOD: This was a longitudinal nested case-cohort design among Osteoarthritis Initiative participants who had hand radiographs at baseline and 48-months, and biospecimens collected at baseline. We classified incident radiographic eHOA in individuals with ≥1 joint with Kellgren-Lawrence ≥2 and a central erosion present at 48-months but not at baseline. We used a random representative sample (n = 1282) for comparison. We measured serum biomarkers of inflammation, insulin resistance and dysglycemia, and adipokines using immunoassays and enzymatic colorimetric procedures, blinded to case status. RESULTS: Eighty-six participants developed incident radiographic eHOA. In the multivariate analyses adjusted for age, gender, race, smoking, and body mass index, and after adjustment for multiple analyses, incident radiographic eHOA was associated with elevated levels of interleukin-7 (risk ratio (RR) per SD = 1.30 [95% confidence interval (CI) 1.09, 1.55] p trend 0.01). CONCLUSION: This exploratory study suggests an association of elevated interleukin-7, an inflammatory cytokine, with incident eHOA, while other cytokines or biomarkers of metabolic inflammation were not associated. Interleukin-7 may mediate inflammation and tissue damage in susceptible osteoarthritic finger joints and participate in erosive progression.

Assessing the association of epigenetic age acceleration with osteoarthritis in the Multicenter Osteoarthritis Study (MOST).

PURPOSE: Advancing age is one of the strongest risk factors for osteoarthritis (OA). DNA methylation-based measures of epigenetic age acceleration may provide insights into mechanisms underlying OA. METHODS: We analyzed data from the Multicenter Osteoarthritis Study in a subset of 671 participants ages 45-69 years with no or mild radiographic knee OA. DNA methylation was assessed with the Illumina Infinium MethylationEPIC 850K array. We calculated predicted epigenetic age according to Hannum, Horvath, PhenoAge, and GrimAge epigenetic clocks, then regressed epigenetic age on chronological age to obtain the residuals. Associations between the residuals and knee, hand, and multi-joint OA were assessed using logistic regression, adjusted for chronological age, sex, clinical site, smoking status, and race. RESULTS: Twenty-three percent met criteria for radiographic hand OA, 25% met criteria for radiographic knee OA, and 8% met criteria for multi-joint OA. Mean chronological age (SD) was 58.4 (6.7) years. Mean predicted epigenetic age (SD) according to Horvath, Hannum, PhenoAge, and GrimAge epigenetic clocks was 64.9 (6.4), 68.6 (5.9), 50.5 (7.7), and 67.0 (6.2), respectively. Horvath epigenetic age acceleration was not associated with an increased odds of hand OA, odds ratio (95% confidence intervals) = 1.03 (0.99-1.08), with similar findings for knee and multi-joint OA. We found similar magnitudes of associations for Hannum epigenetic age, PhenoAge, and GrimAge acceleration compared to Horvath epigenetic age acceleration. CONCLUSIONS: Epigenetic age acceleration as measured by various well-validated epigenetic clocks based on DNA methylation was not associated with increased risk of knee, hand, or multi-joint OA independent of chronological age.

Erosive hand osteoarthritis and sarcopenia: data from Osteoarthritis Initiative cohort.

OBJECTIVES: There is no evidence linking specific osteoarthritis (OA) types, such as erosive hand OA (EHOA), with distant generalised changes in muscle composition (sarcopenia), which can potentially be modified. This study pioneers the exploration of the association between EHOA and sarcopenia, both of which are predominantly observed in the older adults. METHODS: Using the Osteoarthritis Initiative cohort, we selected hand OA (modified Kellgren and Lawrence (grade ≥2 in ≥1 hand joint) participants with radiographic central erosions in ≥1 joints (EHOA group) and propensity score-matched hand OA participants with no erosion (non-EHOA group). MRI biomarkers of thigh muscles were measured at baseline, year 2 and year 4 using a validated deep-learning algorithm. To adjust for 'local' effects of coexisting knee OA (KOA), participants were further stratified according to presence of radiographic KOA. The outcomes were the differences between EHOA and non-EHOA groups in the 4-year rate of change for both intramuscular adipose tissue (intra-MAT) deposition and contractile (non-fat) area of thigh muscles. RESULTS: After adjusting for potential confounders, 844 thighs were included (211 EHOA:633 non-EHOA; 67.1±7.5 years, female/male:2.9). Multilevel mixed-effect regression models showed that EHOA is associated a different 4-year rate of change in intra-MAT deposition (estimate, 95% CI: 71.5 mm2/4 years, 27.9 to 115.1) and contractile area (estimate, 95% CI: -1.8%/4 years, -2.6 to -1.0) of the Quadriceps. Stratified analyses showed that EHOA presence is associated with adverse changes in thigh muscle quality only in participants without KOA. CONCLUSIONS: EHOA is associated with longitudinal worsening of thigh muscle composition only in participants without concomitant KOA. Further research is needed to understand the systemic factors linking EHOA and sarcopenia, which unlike EHOA is modifiable through specific interventions.

A perspective on the evolution of semi-quantitative MRI assessment of osteoarthritis: Past, present and future.

OBJECTIVE: This perspective describes the evolution of semi-quantitative (SQ) magnetic resonance imaging (MRI) in characterizing structural tissue pathologies in osteoarthritis (OA) imaging research over the last 30 years. METHODS: Authors selected representative articles from a PubMed search to illustrate key steps in SQ MRI development, validation, and application. Topics include main scoring systems, reading techniques, responsiveness, reliability, technical considerations, and potential impact of artificial intelligence (AI). RESULTS: Based on original research published between 1993 and 2023, this article introduces available scoring systems, including but not limited to Whole-Organ Magnetic Resonance Imaging Score (WORMS) as the first system for whole-organ assessment of the knee and the now commonly used MRI Osteoarthritis Knee Score (MOAKS) instrument. Specific systems for distinct OA subtypes or applications have been developed as well as MRI scoring instruments for other joints such as the hip, the fingers or thumb base. SQ assessment has proven to be valid, reliable, and responsive, aiding OA investigators in understanding the natural history of the disease and helping to detect response to treatment. AI may aid phenotypic characterization in the future. SQ MRI assessment's role is increasing in eligibility and safety evaluation in knee OA clinical trials. CONCLUSIONS: Evidence supports the validity, reliability, and responsiveness of SQ MRI assessment in understanding structural aspects of disease onset and progression. SQ scoring has helped explain associations between structural tissue damage and clinical manifestations, as well as disease progression. While AI may support human readers to more efficiently perform SQ assessment in the future, its current application in clinical trials still requires validation and regulatory approval.

Hand joints without radiographic osteoarthritis maintain their joint space width over 4 years despite what's happening elsewhere in the hand.

OBJECTIVES: We aimed to determine if hand osteoarthritis is characterized by systemic cartilage loss by assessing if radiographically normal joints had greater joint space width (JSW) loss during four years in hands with incident or prevalent osteoarthritis elsewhere in the hand compared with hands without osteoarthritis. METHODS: We used semi-automated software to measure JSW in the distal and proximal interphalangeal joints of 3,368 participants in the Osteoarthritis Initiative who had baseline and 48-month hand radiographs. A reader scored 16 hand joints (including the thumb-base) for Kellgren-Lawrence (KL) Grade. A joint had osteoarthritis if scored as KL ≥ 2. We identified three groups based on longitudinal hand osteoarthritis status: 1) no hand osteoarthritis (KL < 2 in all 16 joints) at the baseline and 48-month visits, 2) incident hand osteoarthritis (KL < 2in all 16 joints at baseline and then ≥1 joint with KL ≥ 2 at 48-months), and 3) prevalent hand osteoarthritis (≥1 joint with KL ≥ 2 at baseline and 48-months). We then assessed if JSW in radiographically normal joints (KL = 0) differed across these three groups. We calculated unpooled effect sizes to help interpret the differences between groups. RESULTS: We observed small differences in JSW loss that are unlikely to be clinically important between radiographically normal joints between those without hand osteoarthritis (n = 1054) and those with incident (n = 102) or prevalent hand osteoarthritis (n = 2212) (effect size range: -0.01 to 0.24). These findings were robust when examining JSW loss dichotomized based on meaningful change and in other secondary analyses. CONCLUSIONS: Hand osteoarthritis is not a systemic disease of cartilage.

Comparative effectiveness of pharmacological interventions for hand osteoarthritis: a systematic review and network meta-analysis of randomised trials.

OBJECTIVE: To explore the comparative effectiveness of pharmacological interventions for hand osteoarthritis (OA). METHODS: We systematically searched Embase, MEDLINE, and the Cochrane Central Register of Controlled Trials from inception until 26 December 2021, for randomised trials of pharmacological interventions for people with hand OA. Two reviewers independently extracted study data and assessed the risk of bias. We calculated the effect sizes for pain (standardised mean differences) using Bayesian random effects models for network meta-analysis (NMA) and pairwise meta-analysis. Based on a pre-specified protocol, we prospectively registered the study at PROSPERO, CRD42021215393. RESULTS: We included 72 trials with 7609 participants. 65 trials (n=5957) were eligible for the quantitative synthesis, investigating 29 pharmacological interventions. Oral non-steroidal anti-inflammatory drugs (NSAIDs) and oral glucocorticoids' NMA effect sizes were -0.18 (95% credible interval -0.36 to 0.02) and -0.54 (-0.83 to -0.24), respectively, compared with placebo, and the result was consistent when limiting evidence to the pairwise meta-analysis of trials without high risk of bias. Intra-articular hyaluronate, intra-articular glucocorticoids, hydroxychloroquine, and topical NSAIDs' NMA effect sizes were 0.22 (-0.08 to 0.51), 0.25 (0.00 to 0.51), -0.01 (-0.19 to 0.18), and -0.14 (-0.33 to 0.08), respectively, compared with placebo. Oral NSAIDs were inferior to oral glucocorticoids with an NMA effect size of 0.36 (0.01 to 0.72). No intervention was superior to placebo when stratifying for thumb and finger OA. CONCLUSION: Oral NSAIDs and glucocorticoids are apparently effective pharmacological interventions in hand OA. Intra-articular therapies and topical NSAIDs were not superior to placebo.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study.

Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P â€‹+ â€‹S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81-0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52-0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P â€‹+ â€‹S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

Associations between pain sensitization and measures of physical function in people with hand osteoarthritis: Results from the Nor-Hand study.

OBJECTIVE: To examine whether pain sensitization is associated with hand and lower extremity function in people with hand osteoarthritis (OA) in the Nor-Hand study. DESIGN: Pain sensitization was assessed by pressure pain thresholds (PPTs) and temporal summation (TS). Hand function was assessed by Australian/Canadian Osteoarthritis Hand Index (AUSCAN) (range: 0-36), grip strength and Moberg pick-up test, and lower extremity function was assessed by Western Ontario and McMaster Universities Osteoarthritis Index (range: 0-68), 30-s chair stand test, and 40-m walk test. We examined whether sex-standardized PPT and TS values were cross-sectionally associated with measures of physical function using linear regression analyses. Beta coefficients were presented per sex-specific standard deviation of PPT and TS. The mediating effect of pain was examined by causal-inference based mediation analysis. RESULTS: In 206 participants, higher PPTs at/near the hand, indicative of less peripheral and/or central pain sensitization, were associated with greater grip strength and better self-reported hand function (beta for PPT at finger joint on AUSCAN function: -1.41, 95% CI -2.40, -0.42). Higher PPTs at/near the hand, near the knee and at trapezius were associated with lower extremity function, although not statistically significant for all outcomes. Self-reported pain severity mediated the effect of PPT on self-reported function. TS was not associated with hand or lower extremity function. CONCLUSION: Peripheral sensitization, and possibly central sensitization, was associated with impaired function. Effects of PPTs on self-reported function were mediated by self-reported pain, whereas there might be a direct effect of sensitization or effects through other mediators on performance-based function.

Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort.

In the Innovative Medicine's Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained to predict the probability of structural progression (s-score), predefined as >0.3 mm/year joint space width (JSW) decrease and used as inclusion criterion. The current objective was to evaluate predicted and observed structural progression over 2 years according to different radiographic and magnetic resonance imaging (MRI)-based structural parameters. Radiographs and MRI scans were acquired at baseline and 2-year follow-up. Radiographic (JSW, subchondral bone density, osteophytes), MRI quantitative (cartilage thickness), and MRI semiquantitative [SQ; cartilage damage, bone marrow lesions (BMLs), osteophytes] measurements were obtained. The number of progressors was calculated based on a change exceeding the smallest detectable change (SDC) for quantitative measures or a full SQ-score increase in any feature. Prediction of structural progression based on baseline s-scores and Kellgren-Lawrence (KL) grades was analyzed using logistic regression. Among 237 participants, around 1 in 6 participants was a structural progressor based on the predefined JSW-threshold. The highest progression rate was seen for radiographic bone density (39%), MRI cartilage thickness (38%), and radiographic osteophyte size (35%). Baseline s-scores could only predict JSW progression parameters (most P>0.05), while KL grades could predict progression of most MRI-based and radiographic parameters (P<0.05). In conclusion, between 1/6 and 1/3 of participants showed structural progression during 2-year follow-up. KL scores were observed to outperform the machine-learning-based s-scores as progression predictor. The large amount of data collected, and the wide range of disease stage, can be used for further development of more sensitive and successful (whole joint) prediction models. Trial Registration: Clinicaltrials.gov number NCT03883568.

Automatic Measuring of Finger Joint Space Width on Hand Radiograph using Deep Learning and Conventional Computer Vision Methods.

Hand osteoarthritis (OA) severity can be assessed visually through radiographs using semi-quantitative grading systems. However, these grading systems are subjective and cannot distinguish minor differences. Joint space width (JSW) compensates for these disadvantages, as it quantifies the severity of OA by accurately measuring the distances between joint bones. Current methods used to assess JSW require users' interaction to identify the joints and delineate initial joint boundary, which is time-consuming. To automate this process and offer a more efficient and robust measurement for JSW, we proposed two novel methods to measure JSW: 1) The segmentation-based (SEG) method, which uses traditional computer vision techniques to calculate JSW; 2) The regression-based (REG) method, which is a deep learning approach employing a modified VGG-19 network to predict JSW. On a dataset with 3,591 hand radiographs, 10,845 DIP joints were cut as regions of interest and served as input to the SEG and REG methods. The bone masks of the ROI images generated by a U-Net model were sent as input in addition to the ROIs. The ground truth of JSW was labeled by a trained research assistant using a semi-automatic tool. Compared with the ground truth, the REG method achieved a correlation coefficient of 0.88 and mean square error (MSE) of 0.02 mm on the testing set; the SEG method achieved a correlation coefficient of 0.42 and MSE of 0.15 mm. Results show the REG method has promising performance in automatic JSW measurement and in general, Deep Learning approaches can facilitate the automatic quantification of distance features in medical images.

Comorbidities in people with hand OA and their associations with pain severity and sensitization: Data from the longitudinal Nor-Hand study.

Objective: To determine whether the comorbidity burden and co-existing comorbidities are cross-sectionally and/or longitudinally associated with pain and pain sensitization in a cohort study of people with hand OA. Design: We examined whether comorbidity burden and individual comorbidities based on the self-administered Comorbidity Index (range: 0-42) at baseline were associated with pain outcomes at baseline and 3 years follow-up. Pain outcomes included hand and overall bodily pain (range: 0-10) as well as pressure pain thresholds at the tibialis anterior muscle (kg/cm2) and temporal summation (distal radioulnar joint) as measures of central pain sensitization. We performed linear regression analyses adjusted for age, sex, body mass index, physical exercise and education. Results: We included 300 and 196 participants in cross-sectional and longitudinal analyses, respectively. Using baseline data, the burden of comorbidities was associated with greater pain in hands (beta â€‹= â€‹0.61, 95% CI 0.37, 0.85) and overall body (beta â€‹= â€‹0.60, 95% CI 0.37, 0.87). Similar strength of associations was found between comorbidity burden (baseline) and follow-up pain. Among the individual comorbidities, back pain and depression were associated with nearly one unit higher pain score in hands and overall body at both baseline and follow-up. Only back pain was related to lower pressure pain thresholds at follow up (beta â€‹= â€‹-0.24, 95% CI -0.50, -0.001). Conclusion: People with hand OA and greater comorbidity burden, co-existing back pain or depression reported greater pain severity than their counterparts, also 3 years later. These results acknowledge the relevance of accounting for comorbidities in the pain experience in people with hand OA.

A scoping review of how early-stage knee osteoarthritis has been defined.

BACKGROUND: Early-stage knee osteoarthritis (KOA) classification criteria will enable consistent identification and trial recruitment of individuals with knee osteoarthritis (OA) at an earlier stage of the disease when interventions may be more effective. Toward this goal, we identified how early-stage KOA has been defined in the literature. METHODS: We performed a scoping literature review in PubMed, EMBASE, Cochrane, and Web of Science, including human studies where early-stage KOA was included as a study population or outcome. Extracted data included demographics, symptoms/history, examination, laboratory, imaging, performance-based measures, gross inspection/histopathologic domains, and the components of composite early-stage KOA definitions. RESULTS: Of 6142 articles identified, 211 were included in data synthesis. An early-stage KOA definition was used for study inclusion in 194 studies, to define study outcomes in 11 studies, and in the context of new criteria development or validation in six studies. The element most often used to define early-stage KOA was Kellgren-Lawrence (KL) grade (151 studies, 72%), followed by symptoms (118 studies, 56%), and demographic characteristics (73 studies, 35%); 14 studies (6%) used previously developed early-stage KOA composite criteria. Among studies defining early-stage KOA radiographically, 52 studies defined early-stage KOA by KL grade alone; of these 52, 44 (85%) studies included individuals with KL grade 2 or higher in their definitions. CONCLUSION: Early-stage KOA is variably defined in the published literature. Most studies included KL grades of 2 or higher within their definitions, which reflects established or later-stage OA. These findings underscore the need to develop and validate classification criteria for early-stage KOA.

Exploring the differences between radiographic joint space width and MRI cartilage thickness changes using data from the IMI-APPROACH cohort.

OBJECTIVE: Longitudinal weight-bearing radiographic joint space width (JSW) and non-weight-bearing MRI-based cartilage thickness changes often show weak correlations. The current objective was to investigate these correlations, and to explore the influence of different factors that could contribute to longitudinal differences between the two methods. METHODS: The current study included 178 participants with medial osteoarthritis (OA) out of the 297 knee OA participants enrolled in the IMI-APPROACH cohort. Changes over 2 years in medial JSW (ΔJSWmed), minimum JSW (ΔJSWmin), and medial femorotibial cartilage thickness (ΔMFTC) were assessed using linear regression, using measurements from radiographs and MRI acquired at baseline, 6 months, and 1 and 2 years. Pearson R correlations were calculated. The influence of cartilage quality (T2 mapping), meniscal extrusion (MOAKS scoring), potential pain-induced unloading (difference in knee-specific pain scores), and increased loading (BMI) on the correlations was analyzed by dividing participants in groups based on each factor separately, and comparing correlations (slope and strength) between groups using linear regression models. RESULT: Correlations between ΔMFTC and ΔJSWmed and ΔJSWmin were statistically significant (p < 0.004) but weak (R < 0.35). Correlations were significantly different between groups based on cartilage quality and on meniscal extrusion: only patients with the lowest T2 values and with meniscal extrusion showed significant moderate correlations. Pain-induced unloading or BMI-induced loading did not influence correlations. CONCLUSIONS: While the amount of loading does not seem to make a difference, weight-bearing radiographic JSW changes are a better reflection of non-weight-bearing MRI cartilage thickness changes in knees with higher quality cartilage and with meniscal extrusion.

Quantitative CT of the knee in the IMI-APPROACH osteoarthritis cohort: Association of bone mineral density with radiographic disease severity, meniscal coverage and meniscal extrusion.

OBJECTIVE: Osteoarthritis (OA) is a highly prevalent chronic condition. The subchondral bone plays an important role in onset and progression of OA making it a potential treatment target for disease-modifying therapeutic approaches. However, little is known about changes of periarticular bone mineral density (BMD) in OA and its relation to meniscal coverage and meniscal extrusion at the knee. Thus, the aim of this study was to describe periarticular BMD in the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) cohort at the knee and to analyze the association with structural disease severity, meniscal coverage and meniscal extrusion. DESIGN: Quantitative CT (QCT), MRI and radiographic examinations were acquired in 275 patients with knee osteoarthritis (OA). QCT was used to assess BMD at the femur and tibia, at the cortical bone plate (Cort) and at the epiphysis at three locations: subchondral (Sub), mid-epiphysis (Mid) and adjacent to the physis (Juxta). BMD was evaluated for the medial and lateral compartment separately and for subregions covered and not covered by the meniscus. Radiographs were used to determine the femorotibial angle and were evaluated according to the Kellgren and Lawrence (KL) system. Meniscal extrusion was assessed from 0 to 3. RESULTS: Mean BMD differed significantly between each anatomic location at both the femur and tibia (p < 0.001) in patients with KL0. Tibial regions assumed to be covered with meniscus in patients with KL0 showed lower BMD at Sub (p < 0.001), equivalent BMD at Mid (p = 0.07) and higher BMD at Juxta (p < 0.001) subregions compared to regions not covered with meniscus. Knees with KL2-4 showed lower Sub (p = 0.03), Mid (p = 0.01) and Juxta (p < 0.05) BMD at the medial femur compared to KL0/1. Meniscal extrusion grade 2 and 3 was associated with greater BMD at the tibial Cort (p < 0.001, p = 0.007). Varus malalignment is associated with significant greater BMD at the medial femur and at the medial tibia at all anatomic locations. CONCLUSION: BMD within the epiphyses of the tibia and femur decreases with increasing distance from the articular surface. Knees with structural OA (KL2-4) exhibit greater cortical BMD values at the tibia and lower BMD at the femur at the subchondral level and levels beneath compared to KL0/1. BMD at the tibial cortical bone plate is greater in patients with meniscal extrusion grade 2/3.