The incident tibiofemoral osteoarthritis with rapid progression phenotype: development and validation of a prognostic prediction rule

Development of the machine learning model that is highly validated and easily applicable to predict radiographic knee osteoarthritis progression

Many models using the aid of artificial intelligence have been recently proposed to predict the progression of knee osteoarthritis. However, previous models have not been properly validated with an external data set or have reported poor predictive performances. Therefore, the purpose of this study was to design a machine learning model for knee osteoarthritis progression, focusing on high validation quality and clinical applicability. A retrospective analysis was conducted on prospectively collected data, using the Osteoarthritis Initiative data set (5966 knees) for model development and the Multicenter Osteoarthritis Study data set (3392 knees) for validation. The analysis aimed to predict Kellgren-Lawrence grade (KLG) progression over 4-5 years in knees with initial KLG of 0, 1, or 2. Possible predictors included demographics, comorbidities, history of meniscectomy, gait speed, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, and radiological findings. The Random Forest algorithm was employed for the predictive model development. Baseline KLG, contralateral knee osteoarthritis, lateral joint space narrowing (JSN) grade, BMI, medial JSN grade, and total WOMAC score were six features selected for the model in descending order of importance. Odds ratios of baseline KLG, contralateral knee osteoarthritis, and lateral JSN grade were 1.76, 2.59, and 4.74, respectively (all p < 0.001). The area-under-the-curve of the ROC curve in the validation set was 0.76 with an accuracy of 0.68 and an F1-score of 0.56. The progression of knee osteoarthritis in 4 ~ 5 years could be well-predicted using easily available variables. This simple and validated model may aid surgeons in knee osteoarthritis patient management.

Mitonuclear epistasis involving TP63 and haplogroup Uk: Risk of rapid progression of knee OA in patients from the OAI

OBJECTIVE

To investigate genetic interactions between mitochondrial deoxyribonucleic acid (mtDNA) haplogroups and nuclear single nucleotide polymorphisms (nSNPs) to analyze their impact on the development of the rapid progression of knee osteoarthritis (OA).

DESIGN

A total of 1095 subjects from the Osteoarthritis Initiative, with a follow-up time of at least 48-months, were included. Appropriate statistical approaches were performed, including generalized estimating equations adjusting for age, gender, body mass index, contralateral knee OA, Western Ontario and McMaster Universities Osteoarthritis Index pain, previous injury in target knee and the presence of the mtDNA variant m.16519C. Additional genomic data consisted in the genotyping of Caucasian mtDNA haplogroups and eight nSNPs previously associated with the risk of knee OA in robust genome-wide association studies.

RESULTS

The simultaneous presence of the G allele of rs12107036 at TP63 and the haplogroup Uk significantly increases the risk of a rapid progression of knee OA (odds ratio = 1.670; 95% confidence interval [CI]: 1.031-2.706; adjusted p-value = 0.027). The assessment of the population attributable fraction showed that the highest proportion of rapid progressors was under the simultaneous presence of the G allele of rs12107036 and the haplogroup Uk (23.4%) (95%CI: 7.89-38.9; p-value < 0.05). The area under the curve of the cross-validation model (0.730) was very similar to the obtained for the predictive model (0.735). A nomogram was constructed to help clinicians to perform clinical trials or epidemiologic studies.

CONCLUSIONS

This study demonstrates the existence of a mitonuclear epistasis in OA, providing new mechanisms by which nuclear and mitochondrial variation influence the susceptibility to develop different OA phenotypes.

Comparing Metabolic Preconditioning and Diabetes As Risk Factors in Knee Arthroplasty Complications

BACKGROUND AND OBJECTIVES

Advanced osteoarthritis of the knee joint severely affects the patient's mobility, compounded by pre-existing comorbidities such as metabolic preconditioning (such as obesity, dyslipidemia, hyperuricemia, and insulin resistance syndrome) and both type I and type II diabetes. The success of total knee arthroplasty is influenced by knowledge and management of risk factors. The present study aims to evaluate differences in the evolution of risk factors such as obesity, injuries, and sedentary lifestyle, distinguishing those with metabolic preconditions and diabetes. The objectives of our study include (1) investigating the prevalence of obesity among patients, highlighting their proportion in the five categories of body weight; (2) analyzing statistically significant differences between research groups in terms of weight status and physical activity; (3) evaluating postoperative evolution based on the administration of nonsteroidal anti-inflammatory drugs (NSAIDs) and without NSAIDs (N-NSAIDs), with an emphasis on overweight patients and those with diabetes; and (4) examining changes in metabolic preconditioning and the incidence of postoperative injury depending on the administration of anti-inflammatory drugs.

MATERIALS AND METHODS

 A cohort involving 730 patients diagnosed with gonarthrosis was divided into two groups according to the administration of anti-inflammatory drugs in the first seven postoperative days: N-NSAIDs group (394 patients, 55.3%) and respectively NSAIDs group (319 patients, 44.7%). The prospective, observational study was conducted in terms of risk factors and complications that occurred upon treatment administration in relation to each type of intervention and implant used. The outcomes were assessed in terms of the influence on quality of life, the data being collected and interpreted for the entire cohort, and for each study year individually.

RESULTS

The results indicate that almost 69% of them were overweight, while only 31% had a normal weight. Significant differences in weight status were observed between research groups, highlighting the association between obesity and metabolic preconditions or diabetes. Physical activity was absent in a significant proportion, having a notable impact on postoperative evolution, especially in the group without metabolic precondition. Administration of anti-inflammatory drugs influenced postoperative outcomes, with significant differences in overweight and diabetic patients.

CONCLUSIONS

The findings suggest the need to manage body weight, promote physical activity, and personalize postoperative treatments, given the complex interactions between obesity, metabolic preconditions, and the administration of NSAIDs.

Structural phenotypes of knee osteoarthritis: potential clinical and research relevance

A joint contains many different tissues that can exhibit pathological changes, providing many potential targets for treatment. Researchers are increasingly suggesting that osteoarthritis (OA) comprises several phenotypes or subpopulations. Consequently, a treatment for OA that targets only one pathophysiologic abnormality is unlikely to be similarly efficacious in preventing or delaying the progression of all the different phenotypes of structural OA. Five structural phenotypes have been proposed, namely the inflammatory, meniscus-cartilage, subchondral bone, and atrophic and hypertrophic phenotypes. The inflammatory phenotype is characterized by marked synovitis and/or joint effusion, while the meniscus-cartilage phenotype exhibits severe meniscal and cartilage damage. Large bone marrow lesions characterize the subchondral bone phenotype. The hypertrophic and atrophic OA phenotype are defined based on the presence large osteophytes or absence of any osteophytes, respectively, in the presence of concomitant cartilage damage. Limitations of the concept of structural phenotyping are that they are not mutually exclusive and that more than one phenotype may be present. It must be acknowledged that a wide range of views exist on how best to operationalize the concept of structural OA phenotypes and that the concept of structural phenotypic characterization is still in its infancy. Structural phenotypic stratification, however, may result in more targeted trial populations with successful outcomes and practitioners need to be aware of the heterogeneity of the disease to personalize their treatment recommendations for an individual patient. Radiologists should be able to define a joint at risk for progression based on the predominant phenotype present at different disease stages.

Impact of anterior cruciate ligament surgery on the development of knee osteoarthritis: A systematic literature review and meta-analysis comparing non-surgical and surgical treatments

Introduction

Context: The development of knee osteoarthritis (OA) after anterior cruciate ligament (ACL) injury is now widely recognized. The impact of surgical or non-surgical management on the development of post-traumatic osteoarthritis is still debated in the medical community.Here, we present a meta-analysis comparing the impact of surgical or non-surgical management of ACL injuries on the development of knee OA.

Method

A systematic literature review was conducted using data from the PubMed, EMBASE, Medline, and Cochrane libraries from February to May 2019. Only randomized clinical trials published between 2005 and 2019 with a non-surgical group and a surgical group were included to explore the onset or progression of knee OA after ACL injury. Trials had to have at least one radiographic endpoint (Kellgren-Lawrence scoring system). Heterogeneity was assessed using the Cochrane's Q and I2 statistical methods.

Results

Only three randomized controlled trials met the inclusion criteria and were selected for meta-analysis. Of the 343 injured knees included in the studies, 180 underwent ACL reconstruction and 163 underwent non-surgical treatment. The relative risk of knee osteoarthritis was higher after surgery than after non-surgical treatment (RR 1.72, CI 95% [1.18-2.53], I2 ​= ​0%).

Conclusion

The results of this meta-analysis suggest a predisposition to knee osteoarthritis after ACL reconstruction surgery compared with non-surgical management. Due to the small number of good quality studies available, further well-conducted randomised studies are needed to confirm these findings.

A meta-analysis and a functional study support the influence of mtDNA variant m.16519C on the risk of rapid progression of knee osteoarthritis

OBJECTIVES

To identify mitochondrial DNA (mtDNA) genetic variants associated with the risk of rapid progression of knee osteoarthritis (OA) and to characterise their functional significance using a cellular model of transmitochondrial cybrids.

METHODS

Three prospective cohorts contributed participants. The osteoarthritis initiative (OAI) included 1095 subjects, the Cohort Hip and Cohort Knee included 373 and 326 came from the PROspective Cohort of Osteoarthritis from A Coruña. mtDNA variants were screened in an initial subset of 450 subjects from the OAI by in-depth sequencing of mtDNA. A meta-analysis of the three cohorts was performed. A model of cybrids was constructed to study the functional consequences of harbouring the risk mtDNA variant by assessing: mtDNA copy number, mitochondrial biosynthesis, mitochondrial fission and fusion, mitochondrial reactive oxygen species (ROS), oxidative stress, autophagy and a whole transcriptome analysis by RNA-sequencing.

RESULTS

mtDNA variant m.16519C is over-represented in rapid progressors (combined OR 1.546; 95% CI 1.163 to 2.054; p=0.0027). Cybrids with this variant show increased mtDNA copy number and decreased mitochondrial biosynthesis; they produce higher amounts of mitochondrial ROS, are less resistant to oxidative stress, show a lower expression of the mitochondrial fission-related gene fission mitochondrial 1 and an impairment of autophagic flux. In addition, its presence modulates the transcriptome of cybrids, especially in terms of inflammation, where interleukin 6 emerges as one of the most differentially expressed genes.

CONCLUSIONS

The presence of the mtDNA variant m.16519C increases the risk of rapid progression of knee OA. Among the most modulated biological processes associated with this variant, inflammation and negative regulation of cellular process stand out. The design of therapies based on the maintenance of mitochondrial function is recommended.

Prediction Models for Knee Osteoarthritis: Review of Current Models and Future Directions

Background

Knee osteoarthritis (OA) is a prevalent joint disease. Clinical prediction models consider a wide range of risk factors for knee OA. This review aimed to evaluate published prediction models for knee OA and identify opportunities for future model development.

Methods

We searched Scopus, PubMed, and Google Scholar using the terms knee osteoarthritis, prediction model, deep learning, and machine learning. All the identified articles were reviewed by one of the researchers and we recorded information on methodological characteristics and findings. We only included articles that were published after 2000 and reported a knee OA incidence or progression prediction model.

Results

We identified 26 models of which 16 employed traditional regression-based models and 10 machine learning (ML) models. Four traditional and five ML models relied on data from the Osteoarthritis Initiative. There was significant variation in the number and type of risk factors. The median sample size for traditional and ML models was 780 and 295, respectively. The reported Area Under the Curve (AUC) ranged between 0.6 and 1.0. Regarding external validation, 6 of the 16 traditional models and only 1 of the 10 ML models validated their results in an external data set.

Conclusion

Diverse use of knee OA risk factors, small, non-representative cohorts, and use of magnetic resonance imaging which is not a routine evaluation tool of knee OA in daily clinical practice are some of the main limitations of current knee OA prediction models.

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.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Contralateral advanced radiographic knee osteoarthritis predicts radiographic progression and future arthroplasty in ipsilateral knee with early-stage osteoarthritis

PURPOSE

To explore whether the severity of contralateral knee osteoarthritis (OA) is associated with OA progression in ipsilateral knee with early OA.

METHODS

Knees in early OA (Kellgren-Lawrence grade (KLG):1-2) with intact baseline demographic and clinical data were retrieved from OAI database and defined as target knees. The target knees were divided into the exposure group (contralateral knees KLG 3 to 4) and the control group (contralateral knees KLG 0 to 2). Both groups underwent propensity score matching (PSM) concerning demographic data, as well as radiographic and clinical outcomes at the baseline. The primary outcome was the upgrade of KLG in the target knee in the first 12 and 24 months. The secondary outcome was the incidence of knee arthroplasty in ipsilateral knee during the first 108 months.

RESULTS

One thousand seven hundred fifty-two knees were included, with 449 in the exposure cohort and 1276 in the control cohort. Four hundred thirty-four knees in each group were matched after PSM. Target knees in the exposure cohort showed a significantly higher rate of radiographic progression in the first 12 months (12.9% vs. 5.1%, P < 0.001) and 24 months (19.6% vs. 8.1%, P < 0.001). As for the risk of future arthroplasty, a significant difference was also found between the two groups (7.8% vs. 4.0%, P = 0.02). Kaplan-Meier analysis showed that the 108-month accumulated knee survival rate was significantly lower in the exposure group (P = 0.01).

CONCLUSION

The ipsilateral knee with early-stage OA is prone to have worse early to mid-, and long-term prognosis in the circumstance of contralateral radiographic advanced knee OA. Key Points •Identifying early knee osteoarthritis (OA) with a high risk of radiographic progression and future arthroplasty enables early personalized intervention. •This is a novel study to investigate the relationship between the risk of future arthroplasty and contralateral knee status. •Propensity score matching holds promise to minimize selection bias in observational studies. •Knees with early OA are prone to have a high risk of radiographic progression and future arthroplasty in the circumstance of contralateral advanced knee OA.

A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis initiative

BACKGROUND

Knee osteoarthritis (OA) progresses in a heterogeneous way, as a majority of the patients gradually worsen over decades while some undergo rapid progression and require knee replacement. The aim of this study was to develop a predictive model that enables quantified risk prediction of future knee replacement in patients with early-stage knee OA.

METHODS

Patients with early-stage knee OA, intact MRI measurements, and a follow-up time larger than 108 months were retrieved from the Osteoarthritis Initiative database. Twenty-five candidate predictors including demographic data, clinical outcomes, and radiographic parameters were selected. The presence or absence of knee replacement during the first 108 months of the follow-up was regarded as the primary outcome. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. Those models were further tested in the validation group for external validation.

RESULTS

A total of 839 knees were enrolled, with 98 knees received knee replacement during the first 108 months. Glucocorticoid injection history, knee OA in the contralateral side, extensor muscle strength, area of cartilage deficiency, bone marrow lesion, and meniscus extrusion were selected to develop the nomogram after multivariable logistic regression analysis. The bias-corrected C-index and AUC of our nomogram in the validation group were 0.804 and 0.822, respectively.

CONCLUSION

Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA, which showed adequate predictive discrimination and calibration.

KEY POINTS

• Knee OA progresses in a heterogeneous way and rises to a challenge when making treatment strategies. • Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA.

m6A Regulator-Mediated RNA Methylation Modification Patterns Regulate the Immune Microenvironment in Osteoarthritis

Epigenetic regulation, particularly RNA n6 methyl adenosine (m6A) modification, plays an important role in the immune response. However, the regulatory role of m6A in the immune microenvironment in osteoarthritis (OA) remains unclear. Accordingly, we systematically studied RNA modification patterns mediated by 23 m6A regulators in 38 samples and discussed the characteristics of the immune microenvironment modified by m6A. Next, we constructed a novel OA m6A nomogram, an m6A-transcription factor-miRNA network, and a drug network. Healthy and OA samples showed distinct m6A regulatory factor expression patterns. YTHDF3 expression was upregulated in OA samples and positively correlated with type II helper cells and TGFb family member receptors. Furthermore, three different RNA modification patterns were mediated by 23 m6A regulatory factors; in Mode 3, the expression levels of YTHDF3, type II T helper cells, and TGFb family member receptors were upregulated. Pathways related to endoplasmic reticulum oxidative stress and mitochondrial autophagy showed a strong correlation with the regulatory factors associated with Mode 3 and 23 m6A regulatory factors. Through RT-qPCR we validated that SREBF2 and EGR1 as transcription factors of YTHDF3 and IGF2BP3 are closely associated with the development of OA, hsa-miR-340 as a miRNA for YTHDF3 and IGF2BP3 was involved in the development of OA, we also detected the protein expression levels of IGF2BP3, YTHDF3, EGR1 and SREBF2 by western blotting, and the results were consistent with PCR. Overall, the constructed nomogram can facilitate the prediction of OA risk.

Association between medial meniscus extrusion under weight-bearing conditions and pain in early-stage knee osteoarthritis

PURPOSE

This study aimed to investigate the association between the severity of medial meniscus extrusion (MME) under weight bearing and pain in patients with early-stage knee osteoarthritis (OA).

METHODS

Twenty-eight patients with symptomatic early-stage knee OA (Kellgren and Lawrence grade ≤ 2) who visited our outpatient clinic between 2016 and 2018 were included in this cross-sectional study (mean age: 58.0 ± 11.6 years, female: n = 10). MME was evaluated under weight-bearing conditions using ultrasonography. Patients were divided into two groups according to the severity of MME under weight bearing: those with MME ≥ 3 mm were assigned to the severe group, whereas those with MME < 3 mm were assigned to the mild group. The knee injury osteoarthritis outcome score (KOOS) system was used to evaluate knee pain. The incidence of bone marrow lesions (BMLs) was evaluated using magnetic resonance images.

RESULTS

The KOOS pain score was significantly lower in the severe group than in the mild group (P < 0.05). The incidence of BMLs was significantly higher in the severe group (69%) than in the mild group (7%) (P < 0.001).

CONCLUSION

Patients with early-stage knee OA who have greater MME under weight-bearing have more intense knee pain and a higher incidence of BMLs.

Longitudinal Femoral Cartilage T2 Relaxation Time and Thickness Changes with Fast Sequential Radiographic Progression of Medial Knee Osteoarthritis-Data from the Osteoarthritis Initiative (OAI)

This study tested for longitudinal changes in femoral cartilage T2 relaxation time and thickness in fast-progressing medial femorotibial osteoarthritis (OA). From the Osteoarthritis Initiative (OAI) database, nineteen knees fulfilled the inclusion criteria, which included medial femorotibial OA and sequential progression from Kellgren–Lawrence grade (KL) 1 to KL2 to KL3 within five years. Median T2 value and mean thickness were calculated for six condylar volumes of interest (VOIs; medial/lateral anterior, central, posterior) and six sub-VOIs (medial/lateral anterior external, central, internal). T2 value and thickness changes between severity timepoints were tested using repeated statistics. T2 values increased between KL1 and KL2 and between KL1 and KL3 in the medial compartment (p ≤ 0.02), whereas both increases and decreases were observed between the same timepoints in the lateral compartment (p ≤ 0.02). Cartilage thickness decreased in VOI/subVOIs of the medial compartment from KL1 to KL2 and KL3 (p ≤ 0.014). Cartilage T2 value and thickness changes varied spatially over the femoral condyles. While all T2 changes occurred in the early radiographic stages of OA, thickness changes occurred primarily in the later stages. These data therefore support the use of T2 relaxation time analyses in methods of detecting disease-related change during early OA, a valuable period for therapeutic interventions.

Comment on paper by Collins and colleagues - Trajectories of Structural Disease Progression in Knee Osteoarthritis

We read with interest the recently published paper by Collins and colleagues (1), which examined trajectories of worsening medial knee compartment joint space width (JSW). Osteoarthritis initiative (OAI) data from baseline to eight years of follow-up were used to define three JSW trajectories of persons with prevalent symptomatic knee OA, based on the presence of baseline pain and Kellgren and Lawrence OA grades of 1 to 3. Additionally, the investigators considered an analytic method that allowed for consideration of knee replacement participants, a subgroup whose knee OA data are usually censored in knee OA studies. Finally, the investigators determined trajectories of WOMAC Pain scores using the three trajectories defined from the JSW data as the predictor of WOMAC Pain scores over a nine-year period.

Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology

BACKGROUND

We developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use by patients and practitioners that is designed to be appropriate for integration into primary care health information technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes significant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses data that are readily available in primary care settings, it supports targeting of interventions delivered as part of clinical practice that are aimed at risk reduction.

METHODS

We used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients who were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a model that is to be integrated into the same context from which the data were derived.

RESULTS

The resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art discrimination (estimated AUROC 0.84) and good calibration (assessed visually.) The model relies only on information that is readily available in Canadian primary care settings, and hence is appropriate for integration into Canadian primary care health information technology.

CONCLUSIONS

If the contextual challenges arising when using primary care electronic medical record data are appropriately addressed, highly discriminative models for osteoarthritis risk may be constructed using only data commonly available in primary care. Because the models are constructed from data in the same setting where the model is to be applied, internal validation provides strong evidence that the resulting model will perform well in its intended application.

Risk factors and the natural history of accelerated knee osteoarthritis: a narrative review

BACKGROUND

Osteoarthritis is generally a slowly progressive disorder. However, at least 1 in 7 people with incident knee osteoarthritis develop an abrupt progression to advanced-stage radiographic disease, many within 12 months. We summarize what is known - primarily based on findings from the Osteoarthritis Initiative - about the risk factors and natural history of accelerated knee osteoarthritis (AKOA) - defined as a transition from no radiographic knee osteoarthritis to advanced-stage disease < 4 years - and put these findings in context with typical osteoarthritis (slowly progressing disease), aging, prior case reports/series, and relevant animal models. Risk factors in the 2 to 4 years before radiographic manifestation of AKOA (onset) include older age, higher body mass index, altered joint alignment, contralateral osteoarthritis, greater pre-radiographic disease burden (structural, symptoms, and function), or low fasting glucose. One to 2 years before AKOA onset people often exhibit rapid articular cartilage loss, larger bone marrow lesions and effusion-synovitis, more meniscal pathology, slower chair-stand or walking pace, and increased global impact of arthritis than adults with typical knee osteoarthritis. Increased joint symptoms predispose a person to new joint trauma, which for someone who develops AKOA is often characterized by a destabilizing meniscal tear (e.g., radial or root tear). One in 7 people with AKOA onset subsequently receive a knee replacement during a 9-year period. The median time from any increase in radiographic severity to knee replacement is only 2.3 years. Despite some similarities, AKOA is different than other rapidly progressive arthropathies and collapsing these phenomena together or extracting results from one type of osteoarthritis to another should be avoided until further research comparing these types of osteoarthritis is conducted. Animal models that induce meniscal damage in the presence of other risk factors or create an incongruent distribution of loading on joints create an accelerated form of osteoarthritis compared to other models and may offer insights into AKOA.

CONCLUSION

Accelerated knee osteoarthritis is unique from typical knee osteoarthritis. The incidence of AKOA in the Osteoarthritis Initiative and Chingford Study is substantial. AKOA needs to be taken into account and studied in epidemiologic studies and clinical trials.

Predicting Incident Radiographic Knee Osteoarthritis in Middle-Aged Women Within Four Years: The Importance of Knee-Level Prognostic Factors

OBJECTIVE

To develop and internally validate risk models and a clinical risk score tool to predict incident radiographic knee osteoarthritis (RKOA) in middle-aged women.

METHODS

We analyzed 649 women in the Chingford 1,000 Women study. The outcome was incident RKOA, defined as Kellgren/Lawrence grade 0-1 at baseline and ≥2 at year 5. We estimated predictors' effects on the outcome using logistic regression models. Two models were generated. The clinical model considered patient characteristics, medication, biomarkers, and knee symptoms. The radiographic model considered the same factors, plus radiographic factors (e.g., angle between the acetabular roof and the ilium's vertical cortex [hip α-angle]). The models were internally validated. Model performance was assessed using calibration and discrimination (area under the receiver characteristic curve [AUC]).

RESULTS

The clinical model contained age, quadriceps circumference, and a cartilage degradation marker (C-terminal telopeptide of type II collagen) as predictors (AUC = 0.692). The radiographic model contained older age, greater quadriceps circumference, knee pain, knee baseline Kellgren/Lawrence grade 1 (versus 0), greater hip α-angle, greater spinal bone mineral density, and contralateral RKOA at baseline as predictors (AUC = 0.797). Calibration tests showed good agreement between the observed and predicted incident RKOA. A clinical risk score tool was developed from the clinical model.

CONCLUSION

Two models predicting incident RKOA within 4 years were developed, including radiographic variables that improved model performance. First-time predictor hip α-angle and contralateral RKOA suggest OA origins beyond the knee. The clinical tool has the potential to help physicians identify patients at risk of RKOA in routine practice, but the tool should be externally validated.

Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period

OBJECTIVE

To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.

METHODS

Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as ≥ 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint training model was developed using a DL network to extract information from baseline knee X-rays as a feature vector, which was further concatenated with the risk factor data vector. Area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate model performance.

RESULTS

The traditional model had an AUC of 0.660 (61.5% sensitivity and 64.0% specificity) for predicting progression. The DL model had an AUC of 0.799 (78.0% sensitivity and 75.5% specificity), which was significantly higher (P < 0.001) than the traditional model. The combined model had an AUC of 0.863 (80.5% sensitivity and specificity), which was significantly higher than the DL (P = 0.015) and traditional (P < 0.001) models.

CONCLUSION

DL models using baseline knee X-rays had higher diagnostic performance for predicting the progression of radiographic joint space loss than the traditional model using demographic and radiographic risk factors.

Role of Patient-Reported Outcome Measures on Predicting Outcome of Bunion Surgery

BACKGROUND

Prior studies have suggested preoperative patient-reported outcome scores could predict patients who would achieve a clinically meaningful improvement with hallux valgus surgery. Our goal was to determine bunionectomy-specific thresholds using Patient-Reported Outcomes Measurement Information System (PROMIS) values to predict patients who would or would not benefit from bunion surgery.

METHODS

PROMIS physical function (PF), pain interference (PI), and depression assessments were prospectively collected. Forty-two patients were included in the study. Using preoperative and final follow-up visit scores, minimally clinically important differences (MCID), receiver operating characteristic (ROC) curves, and area under the curve (AUC) analyses were performed to determine if preoperative PROMIS scores predicted achieving MCID with 95% specificity or failing to achieve an MCID with 95% sensitivity.

RESULTS

PROMIS PF demonstrated a significant AUC and likelihood ratio. The preoperative threshold score for failing to achieve MCID for PF was 49.6 with 95% sensitivity. The likelihood ratio was 0.14 (confidence interval, 0.02-0.94). The posttest probability of failure to achieve an MCID for PF was 94.1%. PI and depression AUCs were not significant, and thus thresholds were not determined.

CONCLUSION

We identified a PF threshold of 49.6, which was nearly 1 standard deviation higher than previously published. If a patient is hoping to improve PF, a patient with a preoperative t score >49.6 may not benefit from surgery. This study also suggests the need for additional research to delineate procedure-specific thresholds.

LEVEL OF EVIDENCE

Level III, retrospective comparative series.

Effusion-synovitis and infrapatellar fat pad signal intensity alteration differentiate accelerated knee osteoarthritis

OBJECTIVES

To determine whether greater effusion-synovitis volume and infrapatellar fat pad (IFP) signal intensity alteration differentiate incident accelerated knee OA (KOA) from a gradual onset of KOA or no KOA.

METHODS

We classified three sex-matched groups of participants in the Osteoarthritis Initiative who had a knee with no radiographic KOA at baseline (recruited 2004-06; Kellgren-Lawrence <2; n = 125/group): accelerated KOA: ⩾1 knee progressed to Kellgren-Lawrence grade ⩾3 within 48 months; common KOA: ⩾1 knee increased in radiographic scoring within 48 months; and no KOA: both knees had the same Kellgren-Lawrence grade at baseline and 48 months. The observation period included up to 2 years before and after when the group criteria were met. Two musculoskeletal radiologists reported presence of IFP signal intensity alteration and independent readers used a semi-automated method to segment effusion-synovitis volume. We used generalized linear mixed models with group and time as independent variables, as well as testing a group-by-time interaction.

RESULTS

Starting at 2 years before disease onset, adults who developed accelerated KOA had greater effusion-synovitis volume than their peers (accelerated KOA: 11.94 ± 0.90 cm3, KOA: 8.29 ± 1.19 cm3, no KOA: 8.14 ± 0.90 cm3) and have greater odds of having IFP signal intensity alteration than those with no KOA (odds ratio = 2.07, 95% CI = 1.14-3.78). Starting at 1 year prior to disease onset, those with accelerated KOA have greater than twice the odds of having IFP signal intensity alteration than those with common KOA.

CONCLUSION

People with IFP signal intensity alteration and/or greater effusion-synovitis volume in the absence of radiographic KOA may be at high risk for accelerated KOA, which may be characterized by local inflammation.

Phenotypes of osteoarthritis: current state and future implications

In the most recent years, an extraordinary research effort has emerged to disentangle osteoarthritis heterogeneity, opening new avenues for progressing with therapeutic development and unravelling the pathogenesis of this complex condition. Several phenotypes and endotypes have been proposed albeit none has been sufficiently validated for clinical or research use as yet. This review discusses the latest advances in OA phenotyping including how new modern statistical strategies based on machine learning and big data can help advance this field of research.

Accelerated Knee Osteoarthritis Is Characterized by Destabilizing Meniscal Tears and Preradiographic Structural Disease Burden

OBJECTIVE

To determine whether accelerated knee osteoarthritis (KOA) is preceded by, and characterized over time by, destabilizing meniscal tears or other pathologic changes.

METHODS

We selected 3 sex-matched groups of subjects from the first 48 months of the Osteoarthritis Initiative, comprising adults who had a knee without KOA (Kellgren/Lawrence [K/L] radiographic grade <2) at baseline. Subjects in the accelerated KOA group developed KOA of K/L grade ≥3, those with typical KOA showed increased K/L radiographic scores, and those with no KOA had the same K/L grade over time. An index visit was the visit when the radiographic criteria for accelerated KOA and typical KOA were met (the no KOA group was matched to the accelerated KOA group). The observation period was up to 2 years before and after an index visit. Radiologists reviewed magnetic resonance (MR) images of the index knee and identified destabilizing meniscal tears (root tears, radial tears, complex tears), miscellaneous pathologic features (acute ligamentous or tendinous injuries, attrition, subchondral insufficiency fractures, other incidental findings), and meniscal damage in >2 of 6 regions (3 regions per meniscus: anterior horn, body, posterior horn). In addition, bone marrow lesions (BMLs) and cartilage damage on MR images were quantified. Linear mixed regression models were performed to analyze the results.

RESULTS

At 1 year before the index visit, >75% of adults with accelerated KOA had meniscal damage in ≥2 regions (odds ratio 3.19 [95% confidence interval 1.70-5.97] versus adults with typical KOA). By the index visit, meniscal damage in ≥2 regions was ubiquitous in adults with accelerated KOA, including 42% of subjects having evidence of a destabilizing meniscal tear (versus 14% of subjects with typical KOA). These changes corresponded to findings of larger BMLs and greater cartilage loss in the accelerated KOA group.

CONCLUSION

Accelerated KOA is characterized by destabilizing meniscal tears in a knee compromised by meniscal damage in >2 regions, and also characterized by the presence of large BMLs and greater cartilage loss.

Trajectories of femorotibial cartilage thickness among persons with or at risk of knee osteoarthritis: development of a prediction model to identify progressors

OBJECTIVE

There is significant variability in the trajectory of structural progression across people with knee osteoarthritis (OA). We aimed to identify distinct trajectories of femorotibial cartilage thickness over 2 years and develop a prediction model to identify individuals experiencing progressive cartilage loss.

METHODS

We analysed data from the Osteoarthritis Initiative (OAI) (n = 1,014). Latent class growth analysis (LCGA) was used to identify trajectories of medial femorotibial cartilage thickness assessed on magnetic resonance imaging (MRI) at baseline, 1 and 2 years. Baseline characteristics were compared between trajectory-based subgroups and a prediction model was developed including those with frequent knee symptoms at baseline (n = 686). To examine clinical relevance of the trajectories, we assessed their association with concurrent changes in knee pain and incidence of total knee replacement (TKR) over 4 years.

RESULTS

The optimal model identified three distinct trajectories: (1) stable (87.7% of the population, mean change -0.08 mm, SD 0.19); (2) moderate cartilage loss (10.0%, -0.75 mm, SD 0.16) and (3) substantial cartilage loss (2.2%, -1.38 mm, SD 0.23). Higher Western Ontario & McMaster Universities Osteoarthritis Index (WOMAC) pain scores, family history of TKR, obesity, radiographic medial joint space narrowing (JSN) ≥1 and pain duration ≤1 year were predictive of belonging to either the moderate or substantial cartilage loss trajectory [area under the curve (AUC) 0.79, 95% confidence interval (CI) 0.74, 0.84]. The two progression trajectories combined were associated with pain progression (OR 1.99, 95% CI 1.34, 2.97) and incidence of TKR (OR 4.34, 1.62, 11.62).

CONCLUSIONS

A minority of individuals follow a progressive cartilage loss trajectory which was strongly associated with poorer clinical outcomes. If externally validated, the prediction model may help to select individuals who may benefit from cartilage-targeted therapies.

Is osteoarthritis one disease or a collection of many?

OA is a multifaceted and heterogeneous syndrome that may be amenable to tailored treatment. There has been an increasing focus within the OA research community on the identification of meaningful OA phenotypes with potential implications for prognosis and treatment. Experimental and clinical data combined with sophisticated statistical approaches have been used to characterize and define phenotypes from the symptomatic and structural perspectives. An improved understanding of the existing phenotypes based on underlying disease mechanisms may shed light on the distinct entities that make up the disease. This narrative review provides an updated summary of the most recent advances in this field as well as limitations from previous approaches that can be addressed in future studies.

The Value of Phenotypes in Knee Osteoarthritis Research

Background:

Over the past decade, phenotypes have been used to help categorize knee osteoarthritis patients relative to being subject to disease, disease progression, and treatment response. A review of potential phenotype selection is now appropriate. The appeal of using phenotypes is that they most rely on simple physical examination, clinically routine imaging, and demographics. The purpose of this review is to describe the panoply of phenotypes that can be potentially used in osteoarthritis research.

Methods:

A search of PubMed was used singularly to review the literature on knee osteoarthritis phenotypes.

Results:

Four phenotype assembly groups were based on physical features and noninvasive imaging. Demographics included metabolic syndrome (dyslipidemia, hypertension, obesity, and diabetes). Mechanical characteristics included joint morphology, alignment, the effect of injury, and past and present history. Associated musculoskeletal disorder characteristics included multiple joint involvement, spine disorders, neuromuscular diseases, and osteoporosis. With the knee as an organ, tissue characteristics were used to focus on synovium, meniscus, articular cartilage, patella fat pad, bone sclerosis, bone cysts, and location of pain.

Discussion:

Many of these phenotype clusters require further validation studies. There is special emphasis on knee osteoarthritis phenotypes due to its predominance in osteoarthritic disorders and the variety of tissues in that joint. More research will be required to determine the most productive phenotypes for future studies.

Conclusion:

The selection and assignment of phenotypes will take on an increasing role in osteoarthritis research in the future.

Classification of patients with knee osteoarthritis in clinical phenotypes: Data from the osteoarthritis initiative

OBJECTIVES

The existence of phenotypes has been hypothesized to explain the large heterogeneity characterizing the knee osteoarthritis. In a previous systematic review of the literature, six main phenotypes were identified: Minimal Joint Disease (MJD), Malaligned Biomechanical (MB), Chronic Pain (CP), Inflammatory (I), Metabolic Syndrome (MS) and Bone and Cartilage Metabolism (BCM). The purpose of this study was to classify a sample of individuals with knee osteoarthritis (KOA) into pre-defined groups characterized by specific variables that can be linked to different disease mechanisms, and compare these phenotypes for demographic and health outcomes.

METHODS

599 patients were selected from the OAI database FNIH at 24 months' time to conduct the study. For each phenotype, cut offs of key variables were identified matching the results from previous studies in the field and the data available for the sample. The selection process consisted of 3 steps. At the end of each step, the subjects classified were excluded from the further classification stages. Patients meeting the criteria for more than one phenotype were classified separately into a 'complex KOA' group.

RESULTS

Phenotype allocation (including complex KOA) was successful for 84% of cases with an overlap of 20%. Disease duration was shorter in the MJD while the CP phenotype included a larger number of Women (81%). A significant effect of phenotypes on WOMAC pain (F = 16.736 p <0.001) and WOMAC physical function (F = 14.676, p < 0.001) was identified after controlling for disease duration.

CONCLUSION

This study signifies the feasibility of a classification of KOA subjects in distinct phenotypes based on subgroup-specific characteristics.

A single recent injury is a potent risk factor for the development of accelerated knee osteoarthritis: data from the osteoarthritis initiative

We examined the association between previously reported modifiable risk factors for accelerated knee osteoarthritis (AKOA) at the Osteoarthritis Initiative's (OAI) baseline and 48-month visits among adults who develop AKOA between the 48- and 96-month visits. We conducted a case-control study using data from the OAI baseline to the 96-month visit. Participants had no radiographic knee osteoarthritis (KOA) in the index knee at OAI baseline and 48-month visits [Kellgren-Lawrence (KL) <2]. We classified 2 groups: (1) AKOA: >1 knee developed advance-stage KOA (KL = 3 or 4) between 48- and 96-month visits and (2) No KOA: no KOA and no change in radiographic severity bilaterally over 96 months. We used logistic regression models to evaluate the association between the outcome of AKOA (versus no KOA) and several modifiable risk factors collected at OAI baseline and 48-month visits [body mass index (BMI), systolic blood pressure, comorbidity score, and NSAID use]. We also explored a new injury from baseline to 48 months and from 48- to 96 months. Adults with greater baseline and 48-month BMI were more likely to develop AKOA. Injury was only associated with AKOA onset when it occurred within 4 years of developing AKOA [prior 2 years: odds ratio = 6.21; 95% confidence interval (CI) 3.40, 11.35; 2-4 years prior: odds ratio = 4.42, 95% CI 2.06, 9.50]. BMI may consistently predispose an adult to AKOA, but certain injuries are likely a catalyst for AKOA.

Knee symptoms among adults at risk for accelerated knee osteoarthritis: data from the Osteoarthritis Initiative

The purpose of this study was to examine if adults who develop accelerated knee osteoarthritis (KOA) have greater knee symptoms with certain activities than those with or without incident common KOA. We conducted a case-control study using data from baseline and the first four annual visits of the Osteoarthritis Initiative. Participants had no radiographic KOA at baseline (Kellgren-Lawrence (KL) <2). We classified 3 groups as follows: (1) accelerated KOA: > = 1 knee developed advance-stage KOA (KL = 3 or 4) within 48 months, (2) common KOA: > = 1 knee increased in radiographic severity (excluding those with accelerated KOA), and (3) no KOA: no change in radiographic severity by 48 months. We focused on individual items from the WOMAC pain/function subscales and KOOS pain/symptoms subscales. The index visit was a year before a person met the definition for accelerated, common, or no KOA. To examine group difference in knee symptoms, we used ordinal logistic regression models for each symptom. Results are reported as odds ratios (OR) and 95% confidence intervals (CI). Individuals who developed accelerated KOA were more likely to report greater difficulty with lying down (OR = 2.10, 95% CI = 1.04 to 4.25), pain with straightening the knee fully (OR = 2.04, 95% CI = 1.08, 3.85), and pain walking (OR = 2.49, 95% CI = 1.38, 4.84) than adults who developed common KOA. Individuals who develop accelerated KOA report greater symptoms with certain activities than those with common KOA. Our results may help identify individuals at risk for accelerated KOA or with early-stage accelerated KOA.