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

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

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

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

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

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

Structural tissue damage and 24-month progression of semi-quantitative MRI biomarkers of knee osteoarthritis in the IMI-APPROACH cohort.

BACKGROUND: The IMI-APPROACH cohort is an exploratory, 5-centre, 2-year prospective follow-up study of knee osteoarthritis (OA). Aim was to describe baseline multi-tissue semiquantitative MRI evaluation of index knees and to describe change for different MRI features based on number of subregion-approaches and change in maximum grades over a 24-month period. METHODS: MRIs were acquired using 1.5 T or 3 T MRI systems and assessed using the semi-quantitative MRI OA Knee Scoring (MOAKS) system. MRIs were read at baseline and 24-months for cartilage damage, bone marrow lesions (BML), osteophytes, meniscal damage and extrusion, and Hoffa- and effusion-synovitis. In descriptive fashion, the frequencies of MRI features at baseline and change in these imaging biomarkers over time are presented for the entire sample in a subregional and maximum score approach for most features. Differences between knees without and with structural radiographic (R) OA are analyzed in addition. RESULTS: Two hundred eighty-nine participants had readable baseline MRI examinations. Mean age was 66.6 ± 7.1 years and participants had a mean BMI of 28.1 ± 5.3 kg/m2. The majority (55.3%) of included knees had radiographic OA. Any change in total cartilage MOAKS score was observed in 53.1% considering full-grade changes only, and in 73.9% including full-grade and within-grade changes. Any medial cartilage progression was seen in 23.9% and any lateral progression on 22.1%. While for the medial and lateral compartments numbers of subregions with improvement and worsening of BMLs were very similar, for the PFJ more improvement was observed compared to worsening (15.5% vs. 9.0%). Including within grade changes, the number of knees showing BML worsening increased from 42.2% to 55.6%. While for some features 24-months change was rare, frequency of change was much more common in knees with vs. without ROA (e.g. worsening of total MOAKS score cartilage in 68.4% of ROA knees vs. 36.7% of no-ROA knees, and 60.7% vs. 21.8% for an increase in maximum BML score per knee). CONCLUSIONS: A wide range of MRI-detected structural pathologies was present in the IMI-APPROACH cohort. Baseline prevalence and change of features was substantially more common in the ROA subgroup compared to the knees without ROA. TRIAL REGISTRATION: Clinicaltrials.gov identification: NCT03883568.

Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort.

OBJECTIVES: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. METHODS: Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden's index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. RESULTS: Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively). CONCLUSION: The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors. TRIAL REGISTRATION: ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568.

GaitSmart motion analysis compared to commonly used function outcome measures in the IMI-APPROACH knee osteoarthritis cohort.

BACKGROUND: There are multiple measures for assessment of physical function in knee osteoarthritis (OA), but each has its strengths and limitations. The GaitSmart® system, which uses inertial measurement units (IMUs), might be a user-friendly and objective method to assess function. This study evaluates the validity and responsiveness of GaitSmart® motion analysis as a function measurement in knee OA and compares this to Knee Injury and Osteoarthritis Outcome Score (KOOS), Short Form 36 Health Survey (SF-36), 30s chair stand test, and 40m self-paced walk test. METHODS: The 2-year Innovative Medicines Initiative-Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee OA cohort was conducted between January 2018 and April 2021. For this study, available baseline and 6 months follow-up data (n = 262) was used. Principal component analysis was used to investigate whether above mentioned function instruments could represent one or more function domains. Subsequently, linear regression was used to explore the association between GaitSmart® parameters and those function domains. In addition, standardized response means, effect sizes and t-tests were calculated to evaluate the ability of GaitSmart® to differentiate between good and poor general health (based on SF-36). Lastly, the responsiveness of GaitSmart® to detect changes in function was determined. RESULTS: KOOS, SF-36, 30s chair test and 40m self-paced walk test were first combined into one function domain (total function). Thereafter, two function domains were substracted related to either performance based (objective function) or self-reported (subjective function) function. Linear regression resulted in the highest R2 for the total function domain: 0.314 (R2 for objective and subjective function were 0.252 and 0.142, respectively.). Furthermore, GaitSmart® was able to distinguish a difference in general health status, and is responsive to changes in the different aspects of objective function (Standardized response mean (SRMs) up to 0.74). CONCLUSION: GaitSmart® analysis can reflect performance based and self-reported function and may be of value in the evaluation of function in knee OA. Future studies are warranted to validate whether GaitSmart® can be used as clinical outcome measure in OA research and clinical practice.

Return to Sport and Work after Randomization for Knee Distraction versus High Tibial Osteotomy: Is There a Difference?

Knee joint distraction (KJD) is a novel technique for relatively young knee osteoarthritis (OA) patients. With KJD, an external distraction device creates temporary total absence of contact between cartilage surfaces, which results in pain relief and possibly limits the progression of knee OA. Recently, KJD showed similar clinical outcomes compared with high tibial osteotomy (HTO). Yet, no comparative data exist regarding return to sport (RTS) and return to work (RTW) after KJD. Therefore, our aim was to compare RTS and RTW between KJD and HTO. We performed a cross-sectional follow-up study in patients <65 years who previously participated in a randomized controlled trial comparing KJD and HTO. Out of 62 eligible patients, 55 patients responded and 51 completed the questionnaire (16 KJDs and 35 HTOs) at 5-year follow-up. The primary outcome measures were the percentages of RTS and RTW. Secondary outcome measures included time to RTS/RTW, and pre- and postoperative Tegner's (higher is more active), and Work Osteoarthritis or Joint-Replacement Questionnaire (WORQ) scores (higher is better work ability). Patients' baseline characteristics did not differ. Total 1 year after KJD, 79% returned to sport versus 80% after HTO (not significant [n.s.]). RTS <6 months was 73 and 75%, respectively (n.s.). RTW 1 year after KJD was 94 versus 97% after HTO (n.s.), and 91 versus 87% <6 months (n.s.). The median Tegner's score decreased from 5.0 to 3.5 after KJD, and from 5.0 to 3.0 after HTO (n.s.). The mean WORQ score improvement was higher after HTO (16 ± 16) than after KJD (6 ± 13; p = 0.04). Thus, no differences were found for sport and work participation between KJD and HTO in our small, though first ever, cohort. Overall, these findings may support further investigation into KJD as a possible joint-preserving option for challenging "young" knee OA patients. The level of evidence is III.

Utility of the HandScan in monitoring disease activity and prediction of clinical response in rheumatoid arthritis patients: an explorative study.

OBJECTIVES: The aims were to determine the ability of the HandScan [assessing inflammation in hand and wrist joints using optical spectral transmission (OST)] to measure RA disease activity longitudinally, compared with DAS28, and to determine whether short-term (i.e. 1 month) changes in the OST score can predict treatment response at 3 or 6 months. METHODS: Participants visited the outpatient clinic before the start of (additional) RA medication and 1, 3 and 6 months thereafter. Disease activity was monitored at each visit with the HandScan and DAS28 in parallel. A mixed effects model with DAS28 as the outcome variable with a random intercept at patient level, visit month and DAS28 one visit earlier was used to evaluate whether changes in the OST score are related to changes in DAS28. Binary logistic regression was used to test the predictive value of short-term changes in the OST score together with the baseline OST score for achievement of treatment response (EULAR or ACR criteria). All models were adjusted for RA stage (early or established). RESULTS: In total, 64 RA patients were included. One unit change in OST score was found to be related to an average DAS28 change of 0.03 (95% CI: 0.01, 0.06, P = 0.03). When adding OST score as a variable in the longitudinal model, the ability of the model to estimate DAS28 (i.e. explained variance) increased by 2%, to 59%. Neither baseline OST score nor short-term change in OST score was predictive for treatment response at 3 or 6 months. CONCLUSION: A longitudinal association of OST score with DAS28 exists, although explained variance is low. The predictive ability of short-term changes in HandScan for treatment response is limited.

Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis.

OBJECTIVE: To predict response to anti-tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment. METHODS: Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study. RESULTS: Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up-regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. CONCLUSION: Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti-TNF treatment.

Cohort profile: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) study: a 2-year, European, cohort study to describe, validate and predict phenotypes of osteoarthritis using clinical, imaging and biochemical markers.

PURPOSE: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) consortium intends to prospectively describe in detail, preselected patients with knee osteoarthritis (OA), using conventional and novel clinical, imaging, and biochemical markers, to support OA drug development. PARTICIPANTS: APPROACH is a prospective cohort study including 297 patients with tibiofemoral OA, according to the American College of Rheumatology classification criteria. Patients were (pre)selected from existing cohorts using machine learning models, developed on data from the CHECK cohort, to display a high likelihood of radiographic joint space width (JSW) loss and/or knee pain progression. FINDINGS TO DATE: Selection appeared logistically feasible and baseline characteristics of the cohort demonstrated an OA population with more severe disease: age 66.5 (SD 7.1) vs 68.1 (7.7) years, min-JSW 2.5 (1.3) vs 2.1 (1.0) mm and Knee injury and Osteoarthritis Outcome Score pain 31.3 (19.7) vs 17.7 (14.6), except for age, all: p<0.001, for selected versus excluded patients, respectively. Based on the selection model, this cohort has a predicted higher chance of progression. FUTURE PLANS: Patients will visit the hospital again at 6, 12 and 24 months for physical examination, pain and general health questionnaires, collection of blood and urine, MRI scans, radiographs of knees and hands, CT scan of the knee, low radiation whole-body CT, HandScan, motion analysis and performance-based tests.After two years, data will show whether those patients with the highest probabilities for progression experienced disease progression as compared to those wit lower probabilities (model validation) and whether phenotypes/endotypes can be identified and predicted to facilitate targeted drug therapy. TRIAL REGISTRATION NUMBER: NCT03883568.

Effectiveness of TOcilizumab in comparison to Prednisone In Rheumatoid Arthritis patients with insufficient response to disease-modifying antirheumatic drugs (TOPIRA): study protocol for a pragmatic trial.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, predominantly affecting joints, which is initially treated with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). In RA patients with insufficient response to csDMARDs, the addition of prednisone or tocilizumab, a biological DMARD (bDMARD), to the medication has been shown to be effective in reducing RA symptoms. However, which of these two treatment strategies has superior effectiveness and safety is unknown. METHODS: In this multicenter, investigator-initiated, open-label, randomized, pragmatic trial, we aim to recruit 120 RA patients meeting the 2010 ACR/EULAR classification criteria for RA, with active disease defined as a Clinical Disease Activity Index (CDAI) > 10 and at least one swollen joint of the 28 assessed. Patients must be on stable treatment with csDMARDs for ≥ 8 weeks prior to screening and must have been treated with ≥ 2 DMARDs, of which a maximum of one tumor necrosis factor inhibitor (a class of bDMARDs) is allowed. Previous use of other bDMARDs or targeted synthetic DMARDs is not allowed. Patients will be randomized in a 1:1 ratio to receive either tocilizumab (subcutaneously at 162 mg/week) or prednisone (orally at 10 mg/day) as an addition to their current csDMARD therapy. Study visits will be performed at screening; baseline; and months 1, 2, 3, 6, 9, and 12. Study medication will be tapered in case of clinical remission (CDAI ≤ 2.8 and ≤ 1 swollen joint at two consecutive 3-monthly visits) with careful monitoring of disease activity. In case of persistent high disease activity at or after month 3 (CDAI > 22 at any visit or > 10 at two consecutive visits), patients will switch to the other strategy arm. Primary outcome is a change in CDAI from baseline to 12 months. Secondary outcomes are additional clinical response and quality of life measures, drug retention rate, radiographically detectable progression of joint damage, functional ability, and cost utility. Safety outcomes include tocilizumab-associated adverse events (AEs), glucocorticoid-associated AEs, and serious AEs. DISCUSSION: This will be the first randomized clinical trial comparing addition of oral prednisone or of tocilizumab head to head in RA patients with insufficient response to csDMARD therapy. It will yield important information for clinical rheumatology practice. TRIAL REGISTRATION: This trial was prospectively registered in the Netherlands Trial Register on October 7, 2019 (NL8070). The Netherlands Trial Register contains all items from the World Health Organization Trial Registration Data Set.

Cartilage Quality (dGEMRIC Index) Following Knee Joint Distraction or High Tibial Osteotomy.

OBJECTIVE: High tibial osteotomy (HTO) and knee joint distraction (KJD) are treatments to unload the osteoarthritic (OA) joint with proven success in postponing a total knee arthroplasty (TKA). While both treatments demonstrate joint repair, there is limited information about the quality of the regenerated tissue. Therefore, the change in quality of the repaired cartilaginous tissue after KJD and HTO was studied using delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC). DESIGN: Forty patients (20 KJD and 20 HTO), treated for medial tibiofemoral OA, were included in this study. Radiographic outcomes, clinical characteristics, and cartilage quality were evaluated at baseline, and at 1- and 2-year follow-up. RESULTS: Two years after KJD treatment, clear clinical improvement was observed. Moreover, a statistically significant increased medial (Δ 0.99 mm), minimal (Δ 1.04 mm), and mean (Δ 0.68 mm) radiographic joint space width (JSW) was demonstrated. Likewise, medial (Δ 1.03 mm), minimal (Δ 0.72 mm), and mean (Δ 0.46 mm) JSW were statistically significantly increased on radiographs after HTO. There was on average no statistically significant change in dGEMRIC indices over two years and no difference between treatments. Yet there seemed to be a clinically relevant, positive relation between increase in cartilage quality and patients' experienced clinical benefit. CONCLUSIONS: Treatment of knee OA by either HTO or KJD leads to clinical benefit, and an increase in cartilage thickness on weightbearing radiographs for over 2 years posttreatment. This cartilaginous tissue was on average not different from baseline, as determined by dGEMRIC, whereas changes in quality at the individual level correlated with clinical benefit.

Optical spectral transmission to assess inflammation in hand and wrist joints of rheumatoid arthritis patients.

Objective: To develop an optical spectral transmission (OST) model to measure joint inflammation, and thus disease activity, as well as to evaluate (patho-)physiological findings that could lead to misclassification of inflammation. Methods: Forty-six RA patients were included in this cross-sectional study, where US scores, duplicate OST measurements and 28-joint DAS (DAS28) were acquired. With US as a reference standard, the diagnostic performance of OST in detecting inflammation at the joint level was evaluated using receiver operating characteristic (ROC) curve analyses. At the patient level, correlations with US were analysed for DAS28 and OST, and at joint level for OST and tender and swollen joint counts (TJC and SJC, respectively). Joint pathology potentially influencing misclassification by OST [erosions, osteophytes, tendon (sheath) inflammation (ab)normal vasculature and chondrocalcinosis] was evaluated for significance in a multivariate nominal logistic regression model. Results: Diagnostic performance of OST was good for MCP [area under the ROC curve (AUC-ROC) 0.88], PIP (AUC-ROC 0.83) and wrist (AUC-ROC 0.74) joints and for all joints together (AUC-ROC 0.85). At the patient level, DAS28 correlated very poorly (ρ = 0.06) and OST moderately (ρ = 0.54) with US. At the joint level, US correlation with OST was strong (ρ = 0.64), with SJC it was weak (ρ = 0.30) and with TJC it was very weak (ρ = -0.02). Misclassification of inflammation by OST was relatively rare (17%). Dorsal erosions [odds ratio (OR) 4.0], osteophytes (OR 2.1) and extensor tendinitis (OR 4.6) increased the risk of underestimating inflammation of MCP and PIP joints and osteophytes (OR 3.0) also increased the risk of overestimating inflammation. Conclusion: OST is a sensitive, specific and objective technique to assess joints inflammation of the hands and wrists of RA patients, even though bone and tendon pathology increases the risk of misclassification.

Polymorphisms in the multidrug-resistance 1 gene related to glucocorticoid response in rheumatoid arthritis treatment.

A substantial proportion of rheumatoid arthritis (RA)-patients experience an insufficient response to glucocorticoids, an important therapeutic agent in RA. The multidrug-resistance 1 (MDR1) gene product P-glycoprotein (P-gp) is an efflux pump that actively transports substrates, such as glucocorticoids, out of the cell. We investigated if the variation in response might be explained by single-nucleotide polymorphisms (SNPs) in the MDR1 gene. RA-patients treated with intravenous methylprednisolone pulses (n = 18) or oral prednisone/prednisolone (n = 22) were included in a prospective cohort, and clinical response was measured after 5 and 30 days, respectively. The C1236T, G2677A/T, and C3435T SNPs were determined, and the functionality of P-gp was assessed by flow cytometry (Rhodamine efflux assay). Carriage of the G2677A/T SNP was significantly associated with response (OR = 6.18, p = 0.035), the other SNPs showed trends. Stratified for received treatment, the effect was only present in methylprednisolone treated patients. Mutant allele carriage significantly decreased functionality of P-gp in B cells, though had a smaller impact in other PBMC subtypes. Carriage of a MDR1 SNP was related to a response to methylprednisolone in this study, which his suggests that RA-patients carrying wild-type alleles might benefit from P-gp inhibition or administration of glucocorticoid analogues that are non-P-gp substrates.

Necessity of TNF-alpha inhibitor discontinuation in rheumatoid arthritis is predicted by smoking and number of previously used biological DMARDs.

OBJECTIVES: Despite the success of TNF-alpha inhibitor (TNFi) treatment in rheumatoid arthritis (RA), a substantial number of patients necessitate discontinuation. Prediction thereof would be clinically relevant and guide the decision whether to start TNFi treatment. METHODS: Data were used from the observational BiOCURA cohort, in which patients initiating biological treatment were enrolled and followed up for one year. In the model development cohort (n=192), a model predicting TNFi discontinuation was built using Cox-regression with backward selection (p<0.05). The parameters of the model were tested again in a model refinement cohort (n=60), for significance (p<0.05) and consistency of effect. In addition, we performed a systematic review to put our study results into perspective. RESULTS: Of the 252 patients who initiated TNFi treatment, 103 (41%) had to discontinue treatment. Discontinuation was predicted at baseline by female gender, current smoking, high visual analogue scale of general health, and higher number of previously used biological disease-modifying anti-rheumatic drugs (bDMARDs). At refinement, smoking status and number of previously used bDMARDs remained with re-estimated hazard ratios (HRs) in the total cohort of 1.74 (95%-CI 1.15-2.63, p<0.01) and 1.40 (95%-CI 1.1-1.68, p<0.01), respectively. Using these two predictors, we developed a simple score predicting discontinuation (PPV=72.3%). From literature, predictors were pack years of smoking, number of previously used bDMARDs, lack of any concomitant DMARD therapy and in particular lack of concomitant methotrexate (MTX). CONCLUSIONS: TNFi discontinuation is predicted by current smoking and number of previously used bDMARDs, as well as by pack years of smoking and lack of any concomitant DMARD/MTX therapy.

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.

Personalized biological treatment for rheumatoid arthritis: a systematic review with a focus on clinical applicability.

OBJECTIVES: To review studies that address prediction of response to biologic treatment in RA and to explore the clinical utility of the studied (bio)markers. METHODS: A search for relevant articles was performed in PubMed, Embase and Cochrane databases. Studies that presented predictive values or in which these could be calculated were selected. The added value was determined by the added value on prior probability for each (bio)marker. Only an increase/decrease in chance of response ⩾15% was considered clinically relevant, whereas in oncology values >25% are common. RESULTS: Of the 57 eligible studies, 14 (bio)markers were studied in more than one cohort and an overview of the added predictive value of each marker is presented. Of the replicated predictors, none consistently showed an increase/decrease in probability of response ⩾15%. However, positivity of RF and ACPA in case of rituximab and the presence of the TNF-α promoter 308 GG genotype for TNF inhibitor therapy were consistently predictive, yet low in added predictive value. Besides these, 65 (bio)markers studied once showed remarkably high (but not validated) predictive values. CONCLUSION: We were unable to address clinically useful baseline (bio)markers for use in individually tailored treatment. Some predictors are consistently predictive, yet low in added predictive value, while several others are promising but await replication. The challenge now is to design studies to validate all explored and promising findings individually and in combination to make these (bio)markers relevant to clinical practice.

Contribution of the subjective components of the disease activity score to the response to biologic treatment in rheumatoid arthritis.

OBJECTIVE: A significant proportion of patients with rheumatoid arthritis do not respond adequately to biologic treatment. We hypothesized that lack of response to (biologic) disease-modifying antirheumatic drugs (DMARDs) is high in patients in whom the subjective, patient-reported component of the Disease Activity Score 28 (DAS28) is high at baseline. The primary aim of our present study was to investigate the contribution of the more subjective versus the objective components of the DAS28 to response to biologic agents in RA patients, as well as the changes in this contribution over time. The secondary aim was to examine whether the value of this subjective contribution at baseline affects the response to treatment. METHODS: The DAS28-P (the subjective components of the DAS28 relative to the total DAS28) was calculated. Patients were derived from the computer-assisted Management in Early Rheumatoid Arthritis Trial-II and the Biologicals and Outcome Compared and Predicted in Utrecht Region in Rheumatoid Arthritis Study. Ordinal logistic regression analyses were performed. RESULTS: The DAS28-P score at baseline was not associated with the level of response according to European League Against Rheumatism criteria at 3 months. Overall, a significant reduction in the DAS28-P score was observed 3 months after start of treatment, showing a greater reduction of the combined subjective components in good responders. CONCLUSION: The results reject the hypothesis that the lack of response to biologic DMARDs is especially high in patients in whom the patient-reported component of the DAS28 is high at baseline; these subjective components are not linked to treatment response.

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

OBJECTIVE: Expression of osteoarthritis (OA) varies significantly between individuals, and over time, suggesting the existence of different phenotypes, possibly with specific etiology and targets for treatment. Our objective was to identify phenotypes of progression of radiographic knee OA using separate quantitative features. METHODS: Separate radiographic features of OA were measured by Knee Images Digital Analysis (KIDA) in individuals with early knee OA (the CHECK cohort: Cohort Hip & Cohort Knee), at baseline and at 2-year and 5-year followup. Hierarchical clustering was performed to identify phenotypes of radiographic knee OA progression. The phenotypes identified were compared for changes in joint space width (JSW), varus angle, osteophyte area, eminence height, bone density, for Kellgren-Lawrence (K-L) grade, and for clinical characteristics. Logistic regression analysis evaluated whether baseline radiographic features and demographic/clinical characteristics were associated with each of the specific phenotypes. RESULTS: The 5 clusters identified were interpreted as "Severe" or "No," "Early" or "Late" progression of the radiographic features, or specific involvement of "Bone density." Medial JSW, varus angle, osteophyte area, eminence height, and bone density at baseline were associated with the Severe and Bone density phenotypes. Lesser eminence height and bone density were associated with Early and Late progression. Larger varus angle and smaller osteophyte area were associated with No progression. CONCLUSION: Five phenotypes of radiographic progression of early knee OA were identified using separate quantitative features, which were associated with baseline radiographic features. Such phenotypes might require specific treatment and represent relevant subgroups for clinical trials.