Towards developing diagnostic criteria for early knee osteoarthritis: data from the CHECK study

Planned or recent first consultation with the general practitioner for knee complaints: Is it indicative of early-stage knee osteoarthritis?

Objective

No established definition for early-stage knee osteoarthritis (KOA) is available, nor classification criteria. Identifying the characteristics of individuals presenting with early-stage KOA symptoms can enhance diagnosis to prevent progression. This study aimed to describe clinical and structural features of individuals presenting with knee complaints within two years after their first consultation, while exploring differences in the duration of knee complaints.

Method

Baseline data was used from the LITE randomized controlled trial, assessing the effectiveness of a lifestyle intervention for individuals with knee complaints and overweight in primary care. Baseline assessments included questionnaires, clinical assessment, and MRI of the most symptomatic knee. Differences between groups with varying durations of knee complaints (<12, ≥12-<24, ≥24 months) were evaluated.

Results

Participants (N ​= ​218, 65% female, mean age 59 ​± ​6 years, mean BMI 32 ​± ​5 ​kg/m2) had a median knee complaint duration of 14 months, with an average KOOS pain score of 60 ​± ​17.46% reported their symptoms as unacceptable. Structural MRI-defined KOA was observed in 71% of participants. There were no significant differences in clinical or structural MRI features between different durations of knee complaints.

Conclusion

Within 24 months of initial consultation, over two-thirds of participants displayed MRI-defined structural KOA, and nearly half reported unacceptable symptom states. This study found no association between the duration of knee complaints and symptoms severity or structural KOA presence, underscoring the complexity of identifying stages of KOA among individuals with overweight. Future studies should explore additional features beyond current considerations to facilitate early-stage KOA diagnosis, specifically for individuals with overweight.

Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction

OBJECTIVE

Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features.

METHODS

In the Osteoarthritis Initiative cohort, participants with knees without radiographic KOA at baseline but at high risk for radiographic KOA were included. Patients' knees developed radiographic KOA, whereas control knees did not over four years. We randomly split the participants into development and test cohorts (8:2) and extracted features from baseline three-dimensional double-echo steady-state sequence MRI. Model performance was evaluated using an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both cohorts. Nine resident surgeons performed the reader experiment without/with the JS-RM aid.

RESULTS

Our study included 549 knees in the development cohort (275 knees of patients with KOA vs 274 knees of controls) and 137 knees in the test cohort (68 knees of patients with KOA vs 69 knees of controls). In the test cohort, JS-RM had a favorable accuracy for predicting the radiographic KOA incidence with an AUC of 0.931 (95% confidence interval [CI] 0.876-0.963), a sensitivity of 84.4% (95% CI 83.9%-84.9%), and a specificity of 85.6% (95% CI 85.2%-86.0%). The mean specificity and sensitivity of resident surgeons through MRI reading in predicting radiographic KOA incidence were increased from 0.474 (95% CI 0.333-0.614) and 0.586 (95% CI 0.429-0.743) without the assistance of JS-RM to 0.874 (95% CI 0.847-0.901) and 0.812 (95% CI 0.742-0.881) with JS-RM assistance, respectively (P < 0.001).

CONCLUSION

JS-RM integrating the features of the meniscus and cartilage showed improved predictive values in radiographic KOA incidence.

Evaluation of the Diagnostic Performance of American College of Rheumatology, EULAR, and National Institute for Health and Clinical Excellence Criteria Against Clinically Relevant Knee Osteoarthritis: Data From the CHECK Cohort

OBJECTIVE

Our objective was to evaluate the diagnostic performance of the EULAR, American College of Rheumatology (ACR), and National Institute for Health and Care Excellence (NICE) criteria by using clinical experts' diagnosis of clinically relevant knee osteoarthritis (OA) as the outcome of interest.

METHODS

In a previous study, we recruited clinical experts to evaluate longitudinal (5-, 8-, and 10-year follow-up) clinical and radiographic data of symptomatic knees from the Cohort Hip and Cohort Knee (CHECK) study for the presence or absence of clinically relevant OA. In the current study, ACR, EULAR, and NICE criteria were applied to the same 5-, 8-, and 10-year follow-up data; then a knee was diagnosed with OA if fulfilling the criteria at one of the three time points (F1), two of the time points (F2), or at all three time points (F3). Using clinically relevant OA as the reference standard, the sensitivity, specificity, and positive and negative predictive values for the three criteria were assessed.

RESULTS

A total of 539 participants for a total of 833 examined knees were included. Thirty-six percent of knees were diagnosed with clinically relevant OA by experts. Sixty-seven percent to 74% of the knees received the same diagnosis (OA or non-OA) by the three criteria sets for the different definitions (F1 to F3). EULAR consistently (F1 through F3) had the highest specificity, and NICE consistently had the highest sensitivity.

CONCLUSION

The diagnoses only moderately overlapped among the three criteria sets. The EULAR criteria seemed to be more suitable for study enrollment (when aimed at recruiting clinically relevant OA knees), given the highest specificities. The NICE criteria, given the highest sensitivities, could be more useful for an initial diagnosis in clinical practice.

Development of radiographic knee osteoarthritis and the associations to radiographic changes and baseline variables in individuals with knee pain: a 2-year longitudinal study

OBJECTIVES

The aim was to study the development of radiographic knee osteoarthritis (RKOA) in individuals with knee pain over 2 years, and the associations between radiographic changes and baseline variables.

DESIGN

Longitudinal cohort study.

PARTICIPANTS AND SETTING

This study is part of the Halland Osteoarthritis cohort. The included 178 individuals, aged 30-67, had knee pain, without cruciate ligament injury or radiographic findings and 67% were women. The presence of RKOA was defined as Ahlbäck score of ≥1 in ≥1 knee. (Ahlbäck grade 1: joint space narrowing in the tibiofemoral joint <3 mm). Diagnosis of clinical KOA was based on the clinical guideline from the National Institute for Health and Care Excellence (NICE). Knee injury and Osteoarthritis Outcome Score (KOOS), pain intensity, physical function, body mass index (BMI) and visceral fat area (VFA) were measured. Associations to RKOA were analysed with logistic regression (OR).

RESULTS

In all, 13.8% (n=24) developed RKOA in 2 years whereof all had clinical KOA at baseline, as defined by NICE. Deterioration to RKOA was significantly associated with higher BMI, OR 1.119 (95% CI 1.024 to 1.223; p=0.013), and VFA, 1.008 (95% CI 1.000 to 1.016; p=0.049), worse knee pain intensity, 1.238 (95% CI 1.028 to 1.490; p=0.024), worse scores for KOOS Pain, 0.964 (95% CI 0.937 to 0.992; p=0.013) and KOOS Symptoms, 0.967 (95% CI 0.939 to 0.996; p=0.027), KOOS Activities of daily living 0.965 (95% CI 0.935 to 0.996; p=0.026) and KOOS Quality of Life 0.973 (95% CI 0.947 to 0.999; p=0.044), at baseline.

CONCLUSIONS

One out of seven individuals with clinical KOA developed RKOA in only 2 years. Baseline variables associated with RKOA after 2 years may possibly be detected early by using the NICE guideline, assessment of obesity and self-reported data of symptoms to support first-line treatment: education, exercise and weight control.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov (NCT04928170).

Expanding from unilateral to bilateral: A robust deep learning-based approach for predicting radiographic osteoarthritis progression

OBJECTIVE

To develop and validate a deep learning (DL) model for predicting osteoarthritis (OA) progression based on bilateral knee joint views.

METHODS

In this retrospective study, knee joints from bilateral posteroanterior knee radiographs of participants in the Osteoarthritis Initiative were analyzed. At baseline, participants were divided into testing set 1 and development set according to the different enrolled sites. The development set was further divided into a training set and a validation set in an 8:2 ratio for model development. At 48-month follow-up, eligible patients were formed testing set 2. The Bilateral Knee Neural Network (BikNet) was developed using bilateral views, with the knee to be predicted as the main view and the contralateral knee as the auxiliary view. DenseNet and ResNext were also trained and compared as the unilateral model. Two reader tests were conducted to evaluate the model's value in predicting incident OA.

RESULTS

Totally 3583 participants were evaluated. The BikNet we proposed outperformed ResNext and DenseNet (all area under the curve [AUC] < 0.71, P < 0.001) with AUC values of 0.761 and 0.745 in testing sets 1 and 2, respectively. With assistance of the BikNet increased clinicians' sensitivity (from 28.1-63.2% to 42.1-68.4%) and specificity (from 57.4-83.4% to 64.1-87.5%) of incident OA prediction and improved inter-observer reliability.

CONCLUSION

The DL model, constructed based on bilateral knee views, holds promise for enhancing the assessment of OA and demonstrates greater robustness during subsequent follow-up evaluations as compared with unilateral models. BikNet represents a potential tool or imaging biomarker for predicting OA progression.

Imaging of early-stage osteoarthritis: the needs and challenges for diagnosis and classification

In an effort to boost the development of new management strategies for OA, there is currently a shift in focus towards the diagnosis and treatment of early-stage OA. It is important to distinguish diagnosis from classification of early-stage OA. Diagnosis takes place in clinical practice, whereas classification is a process to stratify participants with OA in clinical research. For both purposes, there is an important opportunity for imaging, especially with MRI. The needs and challenges differ for early-stage OA diagnosis versus classification. Although it fulfils the need of high sensitivity and specificity for making a correct diagnosis, implementation of MRI in clinical practice is challenged by long acquisition times and high costs. For classification in clinical research, more advanced MRI protocols can be applied, such as quantitative, contrast-enhanced, or hybrid techniques, as well as advanced image analysis methods including 3D morphometric assessments of joint tissues and artificial intelligence approaches. It is necessary to follow a step-wise and structured approach that comprises, technical validation, biological validation, clinical validation, qualification, and cost-effectiveness, before new imaging biomarkers can be implemented in clinical practice or clinical research.

Quadriceps Strength, Postural Stability, and Pain Mediation in Bilateral Knee Osteoarthritis: A Comparative Analysis with Healthy Controls

Bilateral knee osteoarthritis (OA) poses significant challenges to individuals' functional abilities, including quadriceps strength, postural stability, and pain perception. Understanding the complex relationships among these factors is crucial for enhancing knee OA management strategies. The primary objective of this research is to evaluate and draw comparisons between the strength of the quadriceps and the level of postural stability in two distinct groups: individuals afflicted with bilateral knee OA and those who are healthy. Furthermore, the study seeks to examine the potential correlation between the strength of the quadriceps and the level of postural stability in individuals with knee OA. In addition to this, an investigation into the potential mediating effect of pain on the relationship between these physiological factors will also be conducted. A total of 95 participants with bilateral knee OA and 95 healthy controls were recruited. Quadriceps strength was assessed using dynamometry and postural stability was evaluated through anterior-posterior and medial-lateral sway measurements along with the ellipse area using a force plate. Pain levels were measured using the Visual Analog Scale (VAS). Mediation analysis was employed to explore the role of pain in mediating the relationship between quadriceps strength and postural stability. Statistical analyses included t-tests, Pearson correlation coefficients, and mediation analysis. Knee OA participants exhibited significantly lower quadriceps strength (1.08 Nm/kg ± 0.54) compared to controls (1.54 Nm/kg ± 0.57, p < 0.001). They also demonstrated compromised postural stability with increased anterior-posterior sway (9.86 mm ± 3.017 vs. 2.98 mm ± 1.12, p < 0.001), medial-lateral sway (7.87 mm ± 2.23 vs. 3.12 mm ± 1.34, p < 0.001), and larger ellipse area (935.75 mm2 ± 172.56 vs. 436.19 mm2 ± 135.48, p < 0.001). Negative correlations were observed between quadriceps strength and postural stability variables (r = from -0.43 to -0.51, p < 0.001). Pain significantly mediated the relationship between quadriceps strength and postural stability variables (p < 0.05). This study highlights the associations between quadriceps strength, postural stability, and pain mediation in individuals with bilateral knee OA. Our findings emphasize the need for targeted interventions addressing quadriceps weakness and compromised postural stability. Additionally, the mediation effect of pain underscores the complexity of these relationships, offering insights for more effective management strategies.

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.

A machine learning approach reveals features related to clinicians' diagnosis of clinically relevant knee osteoarthritis

OBJECTIVES

To identify highly ranked features related to clinicians' diagnosis of clinically relevant knee OA.

METHODS

General practitioners (GPs) and secondary care physicians (SPs) were recruited to evaluate 5-10 years follow-up clinical and radiographic data of knees from the CHECK cohort for the presence of clinically relevant OA. GPs and SPs were gathered in pairs; each pair consisted of one GP and one SP, and the paired clinicians independently evaluated the same subset of knees. A diagnosis was made for each knee by the GP and SP before and after viewing radiographic data. Nested 5-fold cross-validation enhanced random forest models were built to identify the top 10 features related to the diagnosis.

RESULTS

Seventeen clinician pairs evaluated 1106 knees with 139 clinical and 36 radiographic features. GPs diagnosed clinically relevant OA in 42% and 43% knees, before and after viewing radiographic data, respectively. SPs diagnosed in 43% and 51% knees, respectively. Models containing top 10 features had good performance for explaining clinicians' diagnosis with area under the curve ranging from 0.76-0.83. Before viewing radiographic data, quantitative symptomatic features (i.e. WOMAC scores) were the most important ones related to the diagnosis of both GPs and SPs; after viewing radiographic data, radiographic features appeared in the top lists for both, but seemed to be more important for SPs than GPs.

CONCLUSIONS

Random forest models presented good performance in explaining clinicians' diagnosis, which helped to reveal typical features of patients recognized as clinically relevant knee OA by clinicians from two different care settings.

Soluble and EV-Associated Diagnostic and Prognostic Biomarkers in Knee Osteoarthritis Pathology and Detection

Osteoarthritis (OA) is the most common degenerative disease of the connective tissue of the human musculoskeletal system. Despite its widespread prevalence, there are many limitations in its diagnosis and treatment. OA diagnosis currently relies on the presence of clinical symptoms, sometimes accompanied by changes in joint X-rays or MRIs. Biomarkers help not only to diagnose early disease progression but also to understand the process of OA in many ways. In this article, we briefly summarize information on articular joints and joint tissues, the pathogenesis of OA and review the literature about biomarkers in the field of OA, specifically inflammatory cytokines/chemokines, proteins, miRNA, and metabolic biomarkers found in the blood, synovial fluid and in extracellular vesicles.

Defining and predicting radiographic knee osteoarthritis progression: a systematic review of findings from the osteoarthritis initiative

PURPOSE

The purposes of this systematic review were to (1) identify the commonly used definitions of radiographic KOA progression, (2) summarize the important associative risk factors for disease progression based on findings from the OAI study and (3) summarize findings from radiographic KOA progression prediction modeling studies regarding the characterization of progression and outcomes.

METHODS

A systematic review was performed by conducting a literature search of definitions, risk factors and predictive models for radiographic KOA progression that utilized data from the OAI database. Radiographic progression was further characterized into "accelerated KOA" and "typical progression," as defined by included studies.

RESULTS

Of 314 studies identified, 41 studies were included in the present review. Twenty-eight (28) studies analyzed risk factors associated with KOA progression, and 13 studies created or validated prediction models or risk calculators for progression. Kellgren-Lawrence (KL) grade based on radiographs was most commonly used to characterize KOA progression (50%), followed by joint space width (JSW) narrowing (32%) generally over 48 months. Risk factors with the highest odds ratios (OR) for progression included periarticular bone mineral density (OR 10.40), any knee injury within 1 year (OR 9.22) and baseline bone mineral lesions (OR 7.92). Nine prediction modeling studies utilized both clinical and structural risk factors to inform their models, and combined models outperformed purely clinical or structural models.

CONCLUSION

The cumulative evidence suggests that combinations of structural and clinical risk factors may be able to predict radiographic KOA progression, particularly in patients with accelerated progression. Clinically relevant and feasible prediction models and risk calculators may provide valuable decision-making support when caring for patients at risk of KOA progression, although standardization in modeling and variable identification does not yet exist.

Effect of Wu Qin Xi exercises on pain and function in people with knee osteoarthritis: A systematic review and meta-analysis

BACKGROUND

As a chronic disease that affects the whole world, there is no definite treatment for knee osteoarthritis (KOA). Wu Qin Xi (WQX) is still in preliminary exploration as a traditional Chinese exercise in the treatment of osteoarthritis of the knee. The purpose of this study was to conduct a meta-analysis of previous studies and to investigate the efficacy of the WQX exercises on pain and function in patients with KOA.

METHODS

We searched six databases (Pubmed, Embase, Cochrane Library, Wanfang, CQVIP, and CNKI) for articles on WQX for KOA up to May 10, 2022. Literature search, study selection, data extraction, and quality evaluation were performed by two independent authors. In terms of statistical results, we presented mean differences (MD), 95% CI, and I 2 to show heterogeneity, and, based on that, we chose either a random effects model or a fixed effects model.

RESULTS

Seven studies were selected for inclusion in this meta-analysis. The WQX intervention group showed statistical differences for both the total Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score and its various bylaws, the Visual Analogue Score (VAS), and the presence of general functional exercise in the control group. We also demonstrated the clinically meaningful efficacy of WQX treatment by calculating minimum clinical importance difference (MCID) values that met the MCID values on the WOMAC score. A sensitivity analysis was also performed in this study by subgroup analysis for greater heterogeneity, and it was inferred that the difference in follow-up time was a likely source of heterogeneity.

CONCLUSION

Despite some limitations, the current study showed a definite effect of WQX in improving pain symptoms and joint function in patients with KOA.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero/, identifier: CRD42022332209.

Cartilage Oligomeric Matrix Protein, Diseases, and Therapeutic Opportunities

Cartilage oligomeric matrix protein (COMP) is an extracellular matrix (ECM) glycoprotein that is critical for collagen assembly and ECM stability. Mutations of COMP cause endoplasmic reticulum stress and chondrocyte apoptosis, resulting in rare skeleton diseases. The bouquet-like structure of COMP allows it to act as a bridging molecule that regulates cellular phenotype and function. COMP is able to interact with many other ECM components and binds directly to a variety of cellular receptors and growth factors. The roles of COMP in other skeleton diseases, such as osteoarthritis, have been implied. As a well-established biochemical marker, COMP indicates cartilage turnover associated with destruction. Recent exciting achievements indicate its involvement in other diseases, such as malignancy, cardiovascular diseases, and tissue fibrosis. Here, we review the basic concepts of COMP and summarize its novel functions in the regulation of signaling events. These findings renew our understanding that COMP has a notable function in cell behavior and disease progression as a signaling regulator. Interestingly, COMP shows distinct functions in different diseases. Targeting COMP in malignancy may withdraw its beneficial effects on the vascular system and induce or aggravate cardiovascular diseases. COMP supplementation is a promising treatment for OA and aortic aneurysms while it may induce tissue fibrosis or cancer metastasis.

Risk assessment for hip and knee osteoarthritis using polygenic risk scores

Objective

Polygenic risk scores (PRS) allow risk stratification using common single‐nucleotide polymorphisms (SNPs), and clinical applications are currently explored for several diseases. This study was undertaken to assess the risk of hip and knee osteoarthritis (OA) using PRS.

Methods

We analyzed 12,732 individuals from a population‐based cohort from the Rotterdam Study (n = 11,496), a clinical cohort (Cohort Hip and Cohort Knee [CHECK] study; n = 908), and a high‐risk cohort of overweight women (Prevention of Knee OA in Overweight Females [PROOF] study; n = 328), for the association of the PRS with prevalence/incidence of radiographic OA, of clinical OA, and of total hip replacement (THR) or total knee replacement (TKR). The hip PRS and knee PRS contained 44 and 24 independent SNPs, respectively, and were derived from a recent genome‐wide association study meta‐analysis. Standardized PRS (with Z transformation) were used in all analyses.

Results

We found a stronger association of the PRS for clinically defined OA compared to radiographic OA phenotypes, and we observed the highest PRS risk stratification for TKR/THR. The odds ratio (OR) per SD was 1.3 for incident THR (95% confidence interval [95% CI] 1.1–1.5) and 1.6 (95% CI 1.3–1.9) for incident TKR in the Rotterdam Study. The knee PRS was associated with incident clinical knee OA in the CHECK study (OR 1.3 [95% CI 1.1–1.5]), but not for the PROOF study (OR 1.2 [95% CI 0.8–1.7]). The OR for OA increased gradually across the PRS distribution, up to 2.1 (95% CI 1.4–3.2) for individuals with the 10% highest PRS compared to the middle 50% of the PRS distribution.

Conclusion

Our findings validated the association of PRS across OA definitions. Since OA is becoming frequent and primary prevention is not commonly applicable, PRS‐based risk assessment could play a role in OA prevention. However, the utility of PRS is dependent on the setting. Further studies are needed to test the integration of genetic risk assessment in diverse health care settings.

Diagnostic criteria for early hip osteoarthritis: first steps, based on the CHECK study

Objectives

Although there is a general focus on early diagnosis and treatment of hip OA, there are no validated diagnostic criteria for early-stage hip OA. The current study aimed to take the first steps in developing diagnostic criteria for early-stage hip OA, using factors obtained through history taking, physical examination, radiography and blood testing at the first consultation in individuals presenting with hip pain, suspicious for hip OA, in primary care.

Methods

Data of the 543 individuals with 735 symptomatic hips at baseline who had any follow-up data available from the prospective CHECK cohort study were used. A group of 26 clinical experts [general practitioners (GPs), rheumatologists and orthopaedic surgeons] evaluated standardized clinical assessment forms of all subjects on the presence of clinically relevant hip OA 5–10 years after baseline. Using the expert-based diagnoses as reference standard, a backward selection method was used to create predictive models based on pre-defined baseline factors from history taking, physical examination, radiography and blood testing.

Results

Prevalence of clinically relevant hip OA during follow-up was 22%. Created models contained four to eight baseline factors (mainly WOMAC pain items, painful/restricted movements and radiographic features) and obtained area under the curve between 0.62 (0.002) and 0.71 (0.002).

Conclusion

Based on clinical and radiographic features of hip OA obtained at first consultation at a GP for pain/stiffness of the hip, the prediction of clinically relevant hip OA within 5–10 years was ‘poor’ to ‘fair’.

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.