Diagnosis Of Knee Osteoarthritis Research Articles

Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data

Knee osteoarthritis (OA) diagnosis is based on symptoms, assessed through questionnaires such as the WOMAC. However, the inconsistency of pain recording and the discrepancy between joint phenotype and symptoms highlight the need for objective biomarkers in knee OA diagnosis. To this end, we study relationships among clinical and molecular data in a cohort of women (n = 51) with Kellgren–Lawrence grade 2–3 knee OA through a Support Vector Machine (SVM) and a regulation network model. Clinical descriptors (i.e., pain catastrophism, depression, functionality, joint pain, rigidity, sensitization and synovitis) are used to classify patients. A Youden’s test is performed for each classifier to determine optimal binarization thresholds for the descriptors. Thresholds are tested against patient stratification according to baseline WOMAC data from the Osteoarthritis Initiative, and the mean accuracy is 0.97. For our cohort, the data used as SVM inputs are knee OA descriptors, synovial fluid proteomic measurements (n = 25), and transcription factor activation obtained from regulatory network model stimulated with the synovial fluid measurements. The relative weights after classification reflect input importance. The performance of each classifier is evaluated through ROC-AUC analysis. The best classifier with clinical data is pain catastrophism (AUC = 0.9), highly influenced by funcionality and pain sensetization, suggesting that kinesophobia is involved in pain perception. With synovial fluid proteins used as input, leptin strongly influences every classifier, suggesting the importance of low-grade inflammation. When transcription factors are used, the mean AUC is limited to 0.608, which can be related to the pleomorphic behaviour of osteoarthritic chondrocytes. Nevertheless, funcionality has an AUC of 0.7 with a decisive importance of FOXO downregulation. Though larger and longitudinal cohorts are needed, this unique combination of SVM and regulatory network model shall help to stratify knee OA patients more objectively.

MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis.

In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called "MedKnee" is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist's diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee's satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis.

MRI use leading up to total knee arthroplasty: a retrospective cohort study.

Demand for total knee arthroplasty (TKA) is increasing as it remains the gold-standard treatment for end-stage osteoarthritis (OA) of the knee. Magnetic resonance imaging (MRI) scans of the knee are not indicated for diagnosing knee OA and represent a possible delay to orthopaedic surgeon referral and unnecessary expenditure. The purpose of this study was to determine the proportion of patients who underwent an MRI in the two years prior to their primary TKA for OA and determine patient and physician associations with increased MRI usage. This is a population-based cohort study using administrative data from Ontario, Canada. All patients over 40years old who underwent their first primary TKA between April 1, 2008, and March 31, 2019, were included. Statistical analyses were performed using SAS and included the Cochran-Armitage test for trend of MRI prior to surgery. A predictive multivariable regression model was used to determine features correlated to receiving an MRI. There were 194,989 eligible first-time TKA recipients, of which 38,244 (19.6%) received an MRI in the two years prior to their surgery. The majority of these (69.6%) were ordered by primary care physicians. Patients who received an MRI were younger, had fewer comorbidities and were more affluent than patients who did not (p < 0.001). MRI use prior to TKA increased from 2008 to 2018 (p < 0.001). Despite MRIs rarely being indicated for the work-up of end-stage OA, nearly one in five patients have an MRI in the two years prior to their TKA. This may be increasing healthcare expenditure and surgical wait-times.

Demonstrating the effectiveness of intra-articular prolotherapy combined with peri-articular perineural injection in knee osteoarthritis: a randomized controlled trial

BackgroundThis study aimed to compare the efficacy of intra-articular prolotherapy (IG) combined with peri-articular perineural injection (PG) in the management of knee osteoarthritis (KOA).MethodsA total of 60 patients with the diagnosis of KOA were included in this double-blinded randomized controlled clinical trials. The inclusion criteria were as follow: (1) 48–80 years old; (2) the diagnose of KOA; (3) the grade 2 and 3 of the Kellgern–Lawrence grading scale; (4) the pain, crepitation, and knee joint stiffness continuing for 3 months at least. The main exclusion criteria were as follow: (1) any infection involving the knee skin; (2) history of any Influencing factors of disease. All patients were divided into three groups and received either IG, PG and I + PG under the ultrasound guidance and the 2, 4 and 8 weeks follow-up data of patients were available. (IG n = 20 or PG n = 20, I + PG n = 20). Visual Analogue Scale (VAS), The Western Ontario McMaster University Osteoarthritis Index (WOMAC) and the pressure pain threshold (PPT) were used as outcome measures at baseline, 2, 4 and 8 weeks.ResultsThere were no statistically significant differences in terms of age, sex, BMI, duration of current condition and baseline assessments of pain intensity, WOMAC scores and PPT. After treatment, the improvement of VAS activity, WOMAC and PPT values was showed compared with pre-treatment in all groups (p < 0.05). At 4 and 8 weeks after treatment, the VAS and WOMAC scores of the I + PG were significantly lower than those of the PG or IG, and the difference was statistically significant (p < 0.05). The PPT values of PG and I + PG were significantly improved compared to IG at 2, 4, and 8 weeks after treatment.ConclusionThe ultrasound guided I + PG of 5% glucose seem to be more effective to alleviate pain and improve knee joint function than single therapy in short term. Clinical rehabilitators could clinically try this combination of I + PG to improve clinical symptoms in patients with KOA.

PAINFUL VS NON-PAINFUL EXERCISE ON CREPITUS IN PEOPLE WITH KNEE OSTEOARTHRITIS: A MIXED-METHODS ANALYSIS

INTRODUCTION &amp; AIMS Crepitus (or physiological noise) is a key criterion in the diagnosis of knee osteoarthritis (OA). Exercise is foundational to guideline-based care for knee OA, however the effect exercise on knee crepitus is unknown. This study aimed to explore the perceptions of people with knee OA following a 6-week accredited exercise physiologist (AEP) led intervention involving painful vs non-painful exercise, especially in relation to their knee crepitus. METHODS Participants were randomly assigned to either a painful (higher intensity) or non-painful (lower intensity) exercise program supervised by an AEP twice a week for six weeks with concurrent education. Volunteers partook in a post-intervention semi-structured interview with questions surrounding their experience and perceived outcomes of the program. Thematic qualitative analysis was conducted and compared to the Knee Injury and Osteoarthritis Outcome Score (KOOS) crepitus question (0 - 4 scale). RESULTS Eleven participants volunteered. Qualitatively, approximately half of the participants self-reported a decrease in their knee crepitus following the program “I haven’t had the creaky knees so much”, with the rest stating their crepitus did not change. No participants perceived their crepitus increased (n=9 no change, n=2 decreased) or said that their crepitus was worse, “the noise and grinding doesn’t become evident (now) until the weights become higher”. Quantitatively, KOOS crepitus scores also did not change following the intervention [mean (SD) (2.4(1.0) to 2.1(1.3), p=0.19)]. There were no differences between exercise groups in self-perceived crepitus results, and most said that the exercise program was beneficial “Right after this whole program, I find that I can move much easier.” CONCLUSION Exercise at higher intensities (into painful range) does not change crepitus or may improve it for some. People with OA perceive that exercise at both higher and lower intensity is beneficial and enjoyable.

Adaptive Fusion of Deep Learning With Statistical Anatomical Knowledge for Robust Patella Segmentation From CT Images.

Kneeosteoarthritis (KOA), as a leading joint disease, can be decided by examining the shapes of patella to spot potential abnormal variations. To assist doctors in the diagnosis of KOA, a robust automatic patella segmentation method is highly demanded in clinical practice. Deep learning methods, especially convolutional neural networks (CNNs) have been widely applied to medical image segmentation in recent years. Nevertheless, poor image quality and limited data still impose challenges to segmentation via CNNs. On the other hand, statistical shape models (SSMs) can generate shape priors which give anatomically reliable segmentation to varying instances. Thus, in this work, we propose an adaptive fusion framework, explicitly combining deep neural networks and anatomical knowledge from SSM for robust patella segmentation. Our adaptive fusion framework will accordingly adjust the weight of segmentation candidates in fusion based on their segmentation performance. We also propose a voxel-wise refinement strategy to make the segmentation of CNNs more anatomically correct. Extensive experiments and thorough assessment have been conducted on various mainstream CNN backbones for patella segmentation in low-data regimes, which demonstrate that our framework can be flexibly attached to a CNN model, significantly improving its performance when labeled training data are limited and input image data are of poor quality.

Performance analysis of Alexnet for Classification of Knee Osteoarthritis.

In recent years, automated grading of knee osteoarthritis (KOA) has focused on determining disease progression. Clinical examinations and radiographic image review are necessary for diagnosis. Timely and accurate diagnosis, along with medical care, can slow down KOA progression. X-rays and MRI are crucial diagnostic tools. KOA diagnosis traditionally relies on radiologists' and clinicians' experience. However, the rapid development of deep learning technology (AI) offers promising solutions for medical applications. The objective of this study was to review and summarize various methods proposed by researchers for the automated grading of KOA. Additionally, this study aimed to evaluate the performance of the AlexNet model in classifying the severity of KOA. The performance of the AlexNet model has been compared to that of other models, and the results have been assessed. A comprehensive review of existing research on automated grading of KOA has been conducted. Various methods proposed by different researchers have been examined and summarized. The AlexNet model has been employed for classifying the severity of KOA, and its performance has been evaluated. A comparative analysis has been carried out to compare the performance of the AlexNet model with that of other models. The results obtained from the evaluation have been assessed to determine the effectiveness of the AlexNet model in the automated grading of KOA. The results of the study indicate that the AlexNet model demonstrates promising performance in classifying the severity of KOA. Comparative analysis reveals that the AlexNet model outperforms other models in terms of accuracy and efficiency. The evaluation of the model's performance provides valuable insights into the effectiveness of deep learning techniques for automated grading of KOA. This study highlights the significance of automated grading in the diagnosis and management of knee osteoarthritis. The utilization of deep learning technology, particularly the AlexNet model, shows promise in accurately classifying the severity of KOA. The findings suggest that automated grading methods can serve as valuable tools for healthcare professionals in assessing the progression of KOA and providing appropriate medical care. Further research and development in this area can contribute to enhancing the efficiency and accuracy of automated grading systems for KOA.

An exploration of footwear preferences, attitudes and beliefs in people with knee osteoarthritis: A qualitative study

BackgroundAlthough footwear can improve pain and function in individuals with knee osteoarthritis (OA), perspectives about footwear in this population have not been explored. ObjectivesThis qualitative study explored preferences, attitudes and beliefs about footwear in adults with knee OA. MethodsTwenty individuals with a clinical diagnosis of knee OA (aged 45–79 years, 65% women) participated in semi-structured interviews about factors which influence footwear selection, the effect of footwear on knee symptoms, and footwear modifications. Data were analysed thematically. ResultsFour themes, with sub-themes, were identified: i) there are specific footwear characteristics people look for, with comfort as their top priority; ii) shoe appearance is important; iii) footwear can aggravate or ease symptoms; and iv) people with knee OA find footwear in a variety of ways. Participants related built-in arch support, a cushioned insole and low/no heel, without addition of foot orthoses, to comfort, and were willing to pay more for comfort and quality. Appearance was also a consideration, and participants indicated they would tolerate short periods of symptom aggravation for aesthetic shoes. Participants felt that footwear choice affected their knee symptoms and risk of slipping/twisting. Participants reported that their footwear choices were determined through trial-and-error, and sometimes on advice from health professionals or shoe store salespersons. ConclusionsThere are specific footwear features important to individuals with knee OA. Knowledge of these features can be used by health professionals to inform footwear discussions with knee OA patients and serve as considerations when developing footwear targeted for this population.

Deep Learning Based Knee Osteoarthritis Detection and Classification

Knee osteoarthritis (OA) is a musculoskeletal disease that significantly affects patients' quality of life. Early and accurate diagnosis of knee osteoarthritis is important for timely intervention and development of appropriate treatment plans. In this study, we present a novel approach using artificial intelligence (AI)-based deep learning to solve the problem of knee joint localization, joint width (JSW) area, and grading of knee OA from digital radiographs. The main aim of this study is to investigate the effectiveness of the deep learning method in predicting knee OA severity based on the Kellgren Lawrence (KL) rating. To achieve this, we developed a custom tool that uses neural networks (CNN) to analyze digital radiographs of the knee joint. Our approach focuses on identifying the presence of knee OA and measuring its severity according to the KL score.

Efficacy of intra-articular platelet -rich plasma in osteoarthritis knee

Efficacy of intra-articular platelet -rich plasma in osteoarthritis knee

Computerized diagnosis of knee osteoarthritis from x‐ray images using combined texture features: Data from the osteoarthritis initiative

AbstractThe prevalence of knee osteoarthritis (KOA) cases has witnessed a significant increase on a global scale in recent years, emphasizing the need for automated diagnostic computer systems to aid in early‐stage osteoarthritis (OA) diagnosis. The accurate characterization of knee KOA stages through feature extraction poses significant research challenges due to the complexity of identifying relevant attributes. In this study, the development of a KOA diagnostic system is presented, leveraging a combination of Gabor, and Tamura parameters using the Canonical Correlation Analysis algorithm. Two feature selection algorithms, namely Principal Component Analysis and Relief, were employed for KOA classification. Furthermore, various classifiers, including K‐Nearest Neighbors, AdaBoost, Bagging, and Random Forest, were used to assess the proposed feature extraction approach. The diagnostic system was assessed using a dataset comprising 688 x‐ray images sourced from the OA initiative (OAI) dataset, consisting of 344 images from healthy subjects (Grade 0) and 344 images from pathological patients (Grade 2). To mitigate overfitting, a 10‐fold cross‐validation method was utilized. The experimental results indicate that the combination of Tamura and Gabor parameters with the Random Forest classifier achieved remarkable performance in KOA diagnosis, yielding an accuracy of 94.59%, and an area under the curve of 98.3%. Notably, the combined Gabor and Tamura models exhibited superior performance compared to individual models, as well as existing techniques reported in the literature.

Assessment of Quality of Life in Patient with Knee Osteoarthritis

Background: Knee osteoarthritis (OA) is a prevalent degenerative musculoskeletal disorder that significantly impairs the quality of life (QoL) of affected individuals, primarily impacting their physical capabilities and emotional well-being. Understanding the factors contributing to the quality of life in knee OA patients is crucial for developing effective management strategies. Objective: This study aims to ascertain the quality of life in patients suffering from knee osteoarthritis, emphasizing the role of demographic factors, physical limitations, and educational levels in influencing patient outcomes. Methods: A cross-sectional survey was conducted among 100 patients diagnosed with knee osteoarthritis, recruited from the Pakistan Society for the Rehabilitation of the Disabled (PSRD) over a four-month period. Participants were selected using a convenient sampling technique, with inclusion criteria set for individuals aged between 40 and 70 years who had a medical diagnosis of knee OA and were willing to sign the consent form. Data were collected through the AIMS-2 SF questionnaire and analyzed using SPSS version 25.0, focusing on demographic details, physical abilities and limitations, and overall quality of life scores. Results: The study population comprised 84% females and 16% males, with a significant portion having an intermediate level of education (35%). The quality of life scores indicated a poor to fair level of QoL among participants, with mean scores slightly below the borderline for being considered 'fair.' Physical limitations were significant, with 44% of participants experiencing difficulty in performing heavy physical activities daily and 36% facing challenges in walking or climbing stairs. Educational level emerged as a crucial factor, with lower educational attainment associated with poorer functional performance and quality of life. Conclusion: The findings highlight a predominantly poor to fair quality of life among knee OA patients, influenced significantly by gender, physical limitations, and educational levels. The study underscores the need for targeted interventions focusing on awareness, lifestyle modifications, and patient education to improve the quality of life in this population.

Causal associations of central and peripheral risk factors with knee osteoarthritis: a longitudinal and Mendelian Randomisation study using UK Biobank data.

Our aim was to investigate relative contributions of central and peripheral mechanisms to knee osteoarthritis (OA) diagnosis and their independent causal association with knee OA. We performed longitudinal analysis using data from UK-Biobank participants. Knee OA was defined using International Classification of Diseases manual 10 codes from participants' hospital records. Central mechanisms were proxied using multisite chronic pain (MCP) and peripheral mechanisms using body mass index (BMI). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated, and proportional risk contribution (PRC) was estimated from receiver-operator-characteristic (ROC) analysis. To estimate the causal effects, we performed 2-sample multivariable Mendelian Randomisation (MR) analysis. We selected genetic instruments from the largest Genome Wide Association Study of BMI (N = 806,834) and MCP (N = 387,649) and estimated the instruments genetic associations with knee OA in the largest available dataset (62,497 cases and 333,557 control subjects). The multivariable MR was performed using modified inverse-variance weighting methods. Of the 203,410 participants, 6% developed knee OA. Both MCP (OR 1.23, 95% CI; 1.21-1.24) and BMI (1.10, 95% CI; 1.10-1.11) were associated with knee OA diagnosis. The PRC was 6.9% (95% CI; 6.7%-7.1%) for MCP and 21.9% (95% CI; 21.4%-22.5%) for BMI; the combined PRC was 38.8% (95% CI; 37.9%-39.8%). Body mass index and MCP had independent causal effects on knee OA (OR 1.76 [95% CI, 1.64-1.88] and 1.83 [95% CI, 1.54-2.16] per unit change, respectively). In conclusion, peripheral risk factors (eg, BMI) contribute more to the development of knee OA than central risk factors (eg, MCP). Peripheral and central factors are independently causal on knee OA.

Does the Combination of Platelet-rich Plasma and Supervised Exercise Yield Better Pain Relief and Enhanced Function in Knee Osteoarthritis? A Randomized Controlled Trial.

Knee osteoarthritis is a leading cause of disability with substantial healthcare costs, and efficient nonsurgical treatment methods are still needed. Platelet-rich plasma (PRP) injections and exercise therapy are used frequently in clinical practice. Whether PRP or PRP combined with exercise is more effective than exercise alone is unclear. (1) Which treatment relieves knee osteoarthritis pain better: PRP alone, exercise, or PRP combined with exercise? (2) Does PRP alone, exercise, or PRP combined with exercise yield better results in terms of the WOMAC score, performance on the 40-m fast-paced walk test and stair climbing test, and the SF-12 health-related quality of life score? In this randomized, controlled, three-arm clinical trial, we recruited patients with mild-to-moderate (Kellgren-Lawrence Grade II or III) knee osteoarthritis with a minimum of 3 points on the 11-point numeric rating scale for pain. During the study period, 157 patients with a diagnosis of knee osteoarthritis were screened and 84 eligible volunteers were enrolled in the study. Patients were randomly allocated (1:1:1) into either the exercise group (28), PRP group (28), or PRP + exercise group (28). Follow-up proportions were similar between the groups (exercise: 89% [25], PRP: 86% [24], PRP + exercise: 89% [25]; p = 0.79). All patients were analyzed in an intention-to-treat manner. There were no between-group differences in age, gender, arthritis severity, and baseline clinical scores (pain, WOMAC, functional performance tests, and health-related quality of life). The exercise group underwent a 6-week structured program consisting of 12 supervised individual sessions focused on strengthening and functional exercises. Meanwhile, the PRP group received three weekly injections of fresh, leukocyte-poor PRP. The PRP + exercise group received a combined treatment with both interventions. The primary outcome was knee pain over 24 weeks, measured on an 11-point numeric rating scale for pain (ranging from 0 to 10, where 0 represents no pain and 10 represents the worst pain, with a minimum clinically important difference [MCID] of 2). The secondary outcome measures included the WOMAC index (ranging from 0 to 100, with lower scores indicating a lower level of disability and an MCID of 12), the durations of the 40-meter fast-paced walk test and stair climbing test, and the SF-12 health-related quality of life score. For the a priori sample size calculation, we used the numeric rating scale score for pain at 24 weeks as the primary outcome variable. The MCID for the numeric rating scale was deemed to be 2 points, with an estimated standard deviation of 2.4. Based on sample size calculations, a sample of 24 patients per group would provide 80% power to detect an effect of this size between the groups at the significance level of p = 0.05. We found no clinically important differences in improvements in pain-defined as ≥ 2 points of 10-at 24 weeks when comparing exercise alone to PRP alone to PRP + exercise (1.9 ± 0.7 versus 3.8 ± 1.8 versus 1.4 ± 0.6; mean difference between PRP + exercise group and exercise group -0.5 [95% confidence interval -1.2 to 0.4]; p = 0.69). Likewise, we found no differences in WOMAC scores at 24 weeks of follow-up when comparing exercise alone to PRP alone to PRP + exercise (10 ± 9 versus 26 ± 20 versus 7 ± 6; mean difference between PRP + exercise group and exercise group -3 [95% CI -12 to -5]; p = 0.97). There were no differences in any of the other secondary outcome metrics among the PRP + exercise and exercise groups. PRP did not improve pain at 24 weeks of follow-up in patients with mild-to-moderate knee osteoarthritis compared with exercise alone. Moreover, exercise alone was clinically superior to PRP alone, considering function and the physical component of health-related quality of life. Despite the additional costs and endeavors related to PRP products, the combination of PRP and exercise did not differ from exercise alone. The results of this randomized controlled trial do not support the use of PRP injections in the treatment of patients diagnosed with mild-to-moderate knee osteoarthritis. Consequently, exercise alone is the recommended treatment for reducing pain and enhancing function throughout this timeframe. Level I, therapeutic study.

Racialized Inequities in Knee Arthroplasty Receipt After Osteoarthritis Diagnosis in the US Military Health System.

The goal was to evaluate institutional inequities in the US Military Health System in knee arthroplasty receipt within three years of knee osteoarthritis diagnosis when accounting for other treatments received (eg, physical therapy, medications). In this retrospective observational cohort study, medical record data of patients (n=29,734) who received a primary osteoarthritis diagnosis in the US Military Health System between January 2016 and January 2020 were analyzed. Data included receipt of physical therapy one year before diagnosis and up to three years after diagnosis, prediagnosis opioid and nonopioid prescription receipt, health-related factors associated with levels of racism, and the primary outcome, knee arthroplasty receipt within three years after diagnosis. In a generalized additive model with time-varying covariates, Asian and Pacific Islander (incidence rate ratio [IRR] 0.58, 95% confidence interval [CI] 0.45-0.74), Black (IRR 0.52, 95%CI 0.46-0.59), and Latine (IRR 0.66, 95%CI 0.52-0.85) patients experienced racialized inequities in knee arthroplasty receipt, relative to white patients (all P < 0.001). In the present sample, Asian and Pacific Islander, Black, and Latine patients were significantly less likely to receive a knee arthroplasty, relative to white patients. Taken together, system-level resources are needed to identify and address mechanisms underlying institutional inequities in knee arthroplasty receipt, such as factors related to systemic and structural, institutional, and personally mediated racism.

Effects of blood flow restriction therapy in patients with knee osteoarthritis: protocol for an overview of systematic reviews.

Osteoarthritis (OA) is the most common and prevalent musculoskeletal disease associated with population aging, negatively impacting function and quality of life. A consequence of knee OA is quadriceps muscle weakness. Musculoskeletal rehabilitation using low load exercises, associated with Blood Flow Restriction (BFR) may be a useful alternative to high load exercises when those cannot be tolerated. Several systematic reviews have reported inconclusive results due to discrepancies in study findings, heterogeneity of results, evaluated time points, and research questions explored. To perform an overview of systematic reviews with meta-analyses, synthesizing the most recent evidence on the effects of muscle strength training with BFR for knee OA. Systematic reviews that include primary controlled and randomized clinical trials will be considered for inclusion. Articles will be considered only if they present a clear and reproducible methodological structure, and when they clearly demonstrate that a critical analysis of the evidence was carried out using instrumented analysis. Narrative reviews, other types of review, overviews of systematic reviews, and diagnostic, prognostic and economic evaluation studies will be excluded. Studies must include adults aged 40 years and older with a diagnosis of knee OA. Two authors will perform an electronic search with guidance from an experienced librarian. The following databases will be searched: PubMed via MEDLINE, Embase, CENTRAL (Cochrane Central Register of Controlled Trials), PEDro, Cumulative Index to Nursing and Allied Health Literature (CINAHL) via EBSCO host, Web of Science, and the gray literature. The search strategy used in the databases will follow the acronym PICOS (population, intervention, comparison, outcome, and study design). Screening (i.e., titles and abstracts) of studies identified by the search strategy will be selected using Rayyan (http://rayyan.qcri.org). The quality assessment will be performed using the "Assessment of Multiple Systematic Reviews" (AMSTAR-2) tool. PROSPERO, CRD42022367209.

Bioinformatics Analysis and Experimental Validation of Mitochondrial Autophagy Genes in Knee Osteoarthritis.

Mitochondrial autophagy is closely related to the pathogenesis of osteoarthritis, In order to explore the role of mitochondrial autophagy related genes in knee osteoarthritis (KOA) and its molecular mechanism. KOA-related transcriptome data were extracted from the Gene Expression Omnibus (GEO) database. Differentially expressed mitochondrial autophagy gene (DEMGs) were screened in patients with KOA by differential expression analysis. The STRING website was used to construct a protein-protein interaction (PPI) network among DEMGs. Molecular complex detection (MCODE) method in Cytoscape software was performed to identify hub DEMGs. Support vector machine recursive feature elimination (SVM-RFE) method was used to construct the hub DEMG diagnosis model. Genes with diagnostic value were identified as biomarkers by plotting receiver operating characteristic (ROC) curves and Expression validation. CIBERSORT algorithm was used to calculate the proportion of 22 immune cells in each sample in the GSE114007 dataset. Finally, biomarker expression was verified by qPCR. A total of 15 DEMGs were obtained and enrichment analyses showed that these DEMG strains were mainly enriched in the mitophagy-animal, shigellosis, autophagy-animal and FoxO signal pathways. The PPI network unveiled 13 DEMGs with interactions. In addition, 8 hub DEMGs (ULK1, CALCOCO2, MAP1LC3B, BNIP3L, GABARAPL1, BNIP3, FKBP8 and FOXO3) were obtained for KOA. And 5 model DEMGs (BNIP3L, BNIP3, MAP1LC3B, ULK1 and FOXO3) were screened. The ROC curves revealed that BNIP3 and FOXO3 has strong diagnostic value in these models of DEMG. Immune-infiltration and correlation analysis showed that BNIP3 and FOXO3 were significantly correlated with three different immune cells, including primary B cells, M0 macrophage and M2 macrophage. The cartilage tissue samples qPCR verification results show that FOXO3 and BNIP3 were all down-regulated in KOA (p < 0.01), and the validation results are consistent with the above analysis. BNIP3 and FOXO3 have been identified as biomarkers for the diagnosis of KOA, which might supply a new insight for the pathogenesis and treatment of KOA.

Autologous microfragmented adipose tissue treatment of knee osteoarthritis demonstrates effectiveness in 68% of patients at 4-year follow-up.

Adipose tissue-derived stem cells are an interesting therapeutic option for early knee osteoarthritis (OA) treatment due to their high plasticity, easiness of harvesting and rapidity of administration. The aim of this study was to evaluate the medium-term effectiveness and safety of Microfragmented Autologous Fat Tissue (MFAT) injection treatment at 4-year follow-up and to investigate potential correlations among patients' pre-treatment clinical condition and clinical outcomes to identify possible predicting factors for procedure success or failure. This is a prospective trial enrolling 46 patients with diagnosis of symptomatic knee OA and failure of previous conservative measures who underwent diagnostic arthroscopy and single autologous MFAT injection between June 2017 and July 2018. Patients were assessed with repeated clinical scoring systems at baseline, 6months, 1 and 4years after surgery. The evaluation included demographic characteristics, arthroscopic findings, and stem cell number from injected tissue. No major complications were reported during follow-up period and there was a significant increase of Lysholm knee score from baseline value of 61.7 ± 13.8 to 79.5 ± 16.9 at 4years (p < 0.001). The WOMAC score increased from a baseline value of 66.5 ± 14.7 to 82.8 ± 15.7 at 4years (p < 0.001) and there was a significant decrease of VAS pain score from baseline value of 6.3 ± 1.5 to 3.5 ± 2.6 at 4-year follow-up (p < 0.001). ROM improved significantly from 118.4 ± 2.6 to 122.5 ± 2.5 at 12months (p < 0.001), but did not improve at 4years (p > 0.05). 15 patients (32.6%) were considered treatment failures, because they required secondary surgery, further injection therapy or experienced symptoms persistence. Patient with synovitis had 75% failure rate, although synovitis did not result as a statistically significant factor influencing clinical outcome up to 4-year follow-up (p = 0.058). Age, cartilage defects severity, BMI, concomitant procedures, and stem cell number from injected MFAT did not show any significant correlation with the results. MFAT intra-articular injection is a safe procedure with positive improvements up to 4-year follow-up in patients with early knee OA. These findings suggest MFAT could be a minimally invasive treatment of early knee OA with durable benefits at mid-term evaluation. IRB number ID-3522.

Prognostic model to predict the incidence of radiographic knee osteoarthritis

ObjectiveEarly diagnosis of knee osteoarthritis (KOA) in asymptomatic stages is essential for the timely management of patients using preventative strategies. We develop and validate a prognostic model useful for predicting...

CHARACTERIZATION OF CLINICAL DATA FOR IN MODERATE OSTEOARTHRITIS WITH SUPPORT VECTOR MACHINES AND REGULATORY NETWORK MODELS

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