Rheumatic Diseases: Selected Tests

Number: 0866


Aetna considers measurement of anti-cyclic citrullinated peptide (anti-CCP) antibodies medically necessary for diagnosis of rheumatoid arthritis (RA).  Aetna considers measurement of anti-CCP antibodies experimental and investigational for all other indications.

Aetna considers the myositis antibody panel medically necessary for diagnosing persons with inflammatory myopathy.  Aetna considers the myositis antibody panel experimental and investigational for all other indications.

Aetna considers measurement of anti-mutated citrullinated vimentin (MCV) antibodies (e.g., the Avise MCV test) experimental and investigational in diagnosing RA and for all other indications because there is insufficient clinical evidence to support the use of this test in the diagnosis of RA.

Aetna considers the Avsie PG, Avise SLE, and Avise SLE+ tests experimental and investigational.

Aetna considers the Vectra DA test experimental and investigational for rheumatoid arthritis and other indications.

Aetna considers measurement of isoforms of 14-3-3 protein (beta, gamma, epsilon, eta, sigma, theta, and zeta) experimental and investigational as biomarkers of osteoarthritis and RA.


Rheumatoid arthritis (RA) is a chronic syndrome characterized by nonspecific, usually symmetric inflammation of the peripheral joints, potentially resulting in progressive destruction of articular and periarticular structures, with or without generalized manifestations

Avise MCV measures antibodies to mutated citrullinated vimentin (MCV), a protein found in the inflamed synovium of patients with RA. Elevated levels of anti-MCV indicate an increased likelihood of having rheumatoid arthritis, and also identify those who may develop more severe forms of RA.

The Avise MCV test is a proprietary personalized medicine test of Cypress Biosciences. No information on the Avise MCV test was found on the U.S. Food and Drug Administration website.

In patients with undiagnosed early inflammatory arthritis or established RA, the diagnostic and prognostic value of adding anti-MCV antibody testing to anti-cyclic citrullinated peptide (anti-CCP) and rheumatoid factor (RF) testing, or substituting anti-MCV for other tests, remains uncertain. Further study is required to more clearly define its role in routine clinical practice.

The idiopathic inflammatory myopathies are a group of systemic rheumatological diseases of unknown etiology, characterized by a chronic inflammatory myositis resulting in muscular weakness with or without organ system dysfunction. The three disorders that comprise this group of muscle disorders are polymyositis, dermatomyositis, and a more recently defined disorder called inclusion body myositis.

The Myositis Antibody Panel Plus is a test for autoantibodies commonly present in the sera of patients with idiopathic inflammatory myopathies, a type of autoimmune disorder. The autoantibodies measured in the test include Jo-1, PL-7, PL-12, EJ, OJ, SRP, KU and Mi2.  The detection of specific autoantibodies can differentiate polymyositis and dermatomyositis from other autoimmune disorders.

Use of the Myositis Antibody Panel Plus aids in the detection of specific autoantibodes that differentiate the idiopathic inflammatory myopathies; therefore, it is a recommended test for the diagnosing of an idiopathic inflammatory myopathy.

The AviseE SLE test is a blood test for the diagnosis of systemic lupus erythematosus (SLE); it involves a group of proteins called complement (including C4d).  The Avise SLE test has a 78 % sensitivity and 87 % specificity.  The Avise SLE+ Connective Tissue™ is a diagnostic test that is offered in addition to the Avise SLE test.  It is made up of 14 common connective tissue diagnostic markers.  It includes markers for extractable nuclear autoantibodies (ENAs), rheumatoid arthritis and anti-phospholipid syndrome autoantibodies -- cardiolipin IgG, cardiolipin IgM, beta2-glycoprotein 1 IgG, and beta2-glycoprotein 1 IgM -- that supposedly help to differentiate lupus from other connective tissue diseases.  The Avise PG test is a blood test used for measuring methotrexate polyglutamates for rheumatoid arthritis (metabolite marker testing).  However, there is a lack of evidence regarding the effectiveness of these tests.

Kilani et al (2007) (i) examined if 14-3-3 proteins were detectable in synovial fluid (SF) of patients with inflamed joints, and if so, what isoform(s); and (ii) examine if there was a correlation between the levels of these proteins and those of matrix metalloproteinase 1 (MMP-1) and matrix metalloproteinase 3 (MMP-3) in the same samples.  In general, 2 sets of synovial and serum samples were analyzed.  The first set of 17 SF -samples from patients with inflamed joints were analyzed for 14-3-3 eta isoform by Western blot.  The second set of 12 matching serum and SF samples were analyzed for 14-3-3 eta, gamma, MMP-1, and MMP-3 by the same procedure.  The MMP-1 stimulatory effect of various concentrations of 14-3-3 eta in cultured fibroblasts was then evaluated.  These researchers found that of the 7 14-3-3 isoforms tested (beta, gamma, epsilon, eta, sigma, Theta, and zeta), the levels of only 2 isoforms, eta and gamma, were easily detectable in SF samples from patients with inflammatory joint diseases.  The levels of these proteins were significantly higher in inflammatory SF and serum samples relative to controls.  The values of these proteins correlated strongly with the levels of MMP-1 and MMP-3, 2 biomarkers for RA, detected in sera.  Furthermore, the level of 14-3-3 eta was significantly higher in a pool of 12 serum samples from patients with inflammatory joint disease than those from healthy individuals.  The authors concluded that detection of only 2 (14-3-3 eta and gamma) out of 7 different isoforms in SF suggested they are specific to the site of inflammation, and that distinguishes them from barely detectable levels of these isoforms found in normal serum.  The MMP-1 stimulatory effect of the eta isoform explained its correlation with MMP-1 levels seen in these samples.  These preliminary findings from a small study (n = 17) need to be validated by well-designed studies.

UpToDate reviews on “Diagnosis and differential diagnosis of rheumatoid arthritis” (Venables and Maini, 2014a) and “Clinical manifestations of rheumatoid arthritis” (Venables and Maini, 2014b) do not mention isoform of 14-3-3 protein (eta and gamma) as biomarkers for RA.

Priam et al (2013) stated that mechanical stress plays an important role in cartilage degradation and subchondral bone remodeling in osteoarthritis (OA).  The remodeling of the subchondral bone could initiate cartilage loss in OA through the interplay of bone and cartilage.  These researchers identified soluble mediators released by loaded osteoblasts/osteocytes that could induce the release of catabolic factors by chondrocytes.  Murine osteoblasts/osteocytes were subjected to cyclic compression, and then conditioned medium from either compressed (CCM) or uncompressed (UCM) cells was used to stimulate mouse chondrocytes.  Chondrocyte expression of MMP-3, matrix metalloproteinase 13 (MMP-13), type II collagen, and aggrecan was assessed by reverse transcription-polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assay.  Soluble mediators released by compressed osteoblasts/osteocytes were identified using iTRAQ (isobaric tags for relative and absolute quantification), a differential secretome analysis.  Subchondral bone and cartilage samples were isolated from OA patients, and culture medium conditioned with OA subchondral bone or cartilage was used to stimulate human chondrocytes.  Stimulation of mouse chondrocytes with CCM strongly induced the messenger RNA (mRNA) expression and protein release of MMP-3 and MMP-13 and inhibited the mRNA expression of type II collagen and aggrecan.  Differential secretome analysis revealed that 10 proteins were up-regulated in compressed osteoblasts/osteocytes.  Among them, soluble 14-3-3 epsilon (s14-3-3e) dose-dependently induced the release of catabolic factors by chondrocytes, mimicking the effects of cell compression.  Addition of a 14-3-3e blocking antibody greatly attenuated the CCM-mediated induction of MMP-3 and MMP-13 expression.  Furthermore, in human OA subchondral bone, s14-3-3e was strongly released, and in cultures of human OA chondrocytes, s14-3-3e stimulated MMP-3 expression.  The authors concluded that the results of this study identified s14-3-3e as a novel soluble mediator critical in the communication between subchondral bone and cartilage in OA.  Thus, s14-3-3e may be a potential target for future therapeutic or prognostic applications in OA.

The Vectra DA is a multi-biomarker disease activity (MBDA) test to measure disease activity in adults diagnosed with rheumatoid arthritis. According to the manufacturer, test results are intended to aid in the assessment of disease activity in rheumatoid arthritis patients and help inform patient management decisions when used in conjunction with standard clinical assessment. The manufacturer states that the test is not intended or validated to diagnose rheumatoid arthritis or to guide therapy selection. The Vectra DA is a laboratory developed test that is not subject to U.S. Food and Drug Administration review. Studies of the Vectra DA test have focused on its ability to predict disease progression, its impact on clinical decisions in simulated cases, and the frequency of changes in management with MBDA results in a clinical practice. Current guidelines on rheumatoid arthritis from the American College of Rheumatology or the European League Against Rheumatism have no recommendation for the MBDA test.

Centola et al (2013) described the development of the Vectra DA multi-biomarker disease activity (MBDA) test for RA. Candidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data.  Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera.  Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g., the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multi-variate models.  Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low versus moderate/high disease activity.  The effect of co-morbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing.  The authors reported that 130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training.  Multi-biomarker statistical models out-performed individual biomarkers at estimating disease activity.  Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low versus moderate/high clinical disease activity.  Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography.  The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100.  The authors reported that no significant effects on the MBDA score were found for common co-morbidities. 

Eastman et al (2012) stated that accurate and frequent assessment of RA disease activity is critical to optimal treatment planning.  The authors explained that a novel algorithm has been developed to determine a multi-biomarker disease activity (MBDA) score based upon measurement of the concentrations of 12 serum biomarkers in multiplex format.  Biomarker assays from several different platforms were used in feasibility studies to identify biomarkers of potential significance.  These assays were adapted to a multiplex platform for training and validation of the algorithm.  In this study, the analytical performance of the underlying biomarker assays and the MBDA score was evaluated.  Quantification of 12 biomarkers was performed with multiplexed sandwich immunoassays in three panels.  Biomarker-specific capture antibodies were bound to specific locations in each well; detection antibodies were labeled with electrochemiluminescent tags.  Data were acquired with a Sector Imager 6000, and analyte concentrations were determined.  Parallelism, dynamic range, cross-reactivity, and precision were established for each biomarker as well as for the MBDA score.  Interference by serum proteins, heterophilic antibodies, and common RA therapies was also assessed.  The individual biomarker assays had 3 to 4 orders of magnitude dynamic ranges, with good reproducibility across time, operators, and reagent lots; the MBDA score had a median coefficient of variation of less than 2 % across the score range.  Cross-reactivity as well as interference by serum rheumatoid factor (RF), human anti-mouse antibodies (HAMA), or common RA therapies, including disease-modifying anti-rheumatic drugs (DMARDs) and biologics, was minimal.   The same MBDA score was observed in different subjects despite having different biomarker profiles, supporting prior literature reports that multiple pathways contribute to rheumatoid arthritis.

Bakker et al (2012) reported that the Vectra DA MBDA test performed well in the assessment of disease activity in RA patients in the Computer Assisted Management in Early Rheumatoid Arthritis CAMERA study.  However, neither the MBDA score nor clinical variables were predictive of radiographic progression.  Investigators measured 20 biomarkers in the CAMERA cohort, in which patients were treated with either intensive or conventional methotrexate-based treatment strategies.  The MBDA score was calculated using the concentrations of 12 biomarkers (SAA, IL-6, TNF-RI, VEGF-A, MMP-1, YKL-40, MMP-3, EGF, VCAM-1, leptin, resistin and CRP) according to a previously trained algorithm.  The performance of the scores was evaluated relative to clinical disease activity assessments.  Change in MBDA score over time was assessed by paired Wilcoxon rank sum test.  Logistic regression was used to evaluate the ability of disease activity measures to predict radiographic progression.  The investigators stated that the MBDA score had a significant correlation with the disease activity score based on 28 joints-C reactive protein (DAS28-CRP) (r = 0.72; p < 0.001) and an area under the receiver operating characteristic curve (AUC) for distinguishing remission/low from moderate/high disease activity of 0.86 (p < 0.001) using a DAS28-CRP cut-off of 2.7.  In multi-variate analysis the MBDA score, but not CRP, was an independent predictor of disease activity measures.  Additionally, mean (SD) MBDA score decreased from 53 (18) at baseline to 39 (16) at 6 months in response to study therapy (p < 0.0001).  The authors found that neither MBDA score nor clinical variables were predictive of radiographic progression.

Curtis and colleagues (2012) validated the Vectra DA MBDA test relative to clinical disease activity in RA.  Serum samples were obtained from the Index for Rheumatoid Arthritis Measurement, Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study, and Leiden Early Arthritis Clinic cohorts.  Levels of 12 biomarkers were measured and combined according to a prespecified algorithm to generate the composite MBDA score.  The relationship of the MBDA score to clinical disease activity was characterized separately in sero-positive and sero-negative patients using Pearson's correlations and the area under the receiver operating characteristic curve (AUROC) to discriminate between patients with low and moderate/high disease activity.  Associations between changes in MBDA score and clinical responses 6 to 12 weeks after initiation of anti-tumor necrosis factor (TNF) or methotrexate treatment were evaluated by the AUROC.  The investigators found that the MBDA score was significantly associated with the Disease Activity Score in 28 joints using the C-reactive protein level (DAS28-CRP) in both sero-positive (AUROC 0.77, p < 0.001) and sero-negative (AUROC 0.70, p < 0.001) patients.  In subgroups based on age, sex, body mass index, and treatment, the MBDA score was associated with the DAS28-CRP (p < 0.05) in all sero-positive and most sero-negative subgroups.  These investigators reported that changes in the MBDA score at 6 to 12 weeks could discriminate both American College of Rheumatology (ACR) criteria for 50 % improvement responses (p = 0.03) and DAS28-CRP improvement pP = 0.002).  Changes in the MBDA score at 2 weeks were also associated with subsequent DAS28-CRP response (p = 0.02).

Hirata et al (2013) reported that Vectra DA MBDA score reflects current clinical disease activity and can track changes in rheumatoid arthritis disease activity over 1 year.  The investigators studied 125 patients with RA from the Behandel Strategieën study.  Clinical data and serum samples were available from 179 visits, 91 at baseline and 88 at year 1.  In each serum sample, 12 biomarkers were measured by quantitative multiplex immunoassays and the concentrations were used as input to a pre-specified algorithm to calculate MBDA scores.  The investigators found that MBDA scores had significant correlation with DAS28-ESR (Spearman's ρ = 0.66, p < 0.0001) and also correlated with simplified disease activity index, clinical disease activity index and HAQ Disability Index (all p < 0.0001).  Changes in MBDA between baseline and year 1 were also correlated with changes in DAS28-ESR (ρ = 0.55, p < 0.0001).  Groups stratified by European League Against Rheumatism disease activity (DAS28-ESR less than or equal to 3.2, 3.2 to 5.1 and greater than 5.1) had significantly different MBDA scores (p < 0.0001) and MBDA score could discriminate ACR/EULAR Boolean remission with an AUROC of 0.83 (p < 0.0001).

Markusse et al (2014) reported that the Vectra DA MBDA score predicts radiographic damage progression over 1 year in patients with early RA.  For this study, these investigators used 180 serum samples from the BeSt study: 91 at baseline (84 with radiographs available) and 89 at 1-year followup (81 with radiographs available).  Radiographs were assessed using the Sharp/van der Heijde Score (SvdH); 12 serum biomarkers were measured to determine MBDA scores using a validated algorithm.  Receiver-operating curves and Poisson regression analyses were performed, with Disease Activity Score (DAS) and MBDA score as independent variables, and radiographic progression as dependent variable.  The investigators reported that, at baseline, MBDA scores discriminated more between patients who developed radiographic progression (increase in SvdH greater than or equal to 5 points) and patients who did not [AUC 0.767, 95 % confidence interval [CI]: 0.639 to 0.896] than did DAS (AUC 0.521, 95 % CI: 0.358 to 0.684).  At 1 year, MBDA score had an AUC of 0.691 (95 % CI: 0.453 to 0.929) and DAS had an AUC of 0.649 (95 % CI: 0.417 to 0.880).  Adjusted for anti-citrullinated protein antibody status and DAS, higher MBDA scores were associated with an increased risk for SvdH progression [relative risk (RR) 1.039, 95 % CI: 1.018 to 1.059 for baseline MBDA score; 1.037, 95 % CI: 1.009 to 1.065 for Year 1 MBDA score].  Categorized high MBDA scores were also correlated with SvdH progression (RR for high MBDA score at baseline 3.7; low or moderate MBDA score as reference).  At 1 year, high MBDA score gave a RR of 4.6 compared to low MBDA score.

van der Helm-van Mil et al (2013) reported that the Vectra DA MBDA score predicts limited radiographic progression over 1 year, so that it can potentially be a useful adjunct to clinical assessment to identify progression-free remission and to assess subclinical disease.  The study examined 271 visits for 163 RA patients in the Leiden Early Arthritis Cohort.  The MBDA score and other variables from each visit were evaluated for prediction of progression [change in Sharp-van der Heijde Score (ΔSHS) greater than 3] over the ensuing 12 months.  Positive likelihood ratios (PLRs) for non-progression were calculated for remission based upon DAS based on 28-joint counts and CRP (DAS28-CRP less than 2.32), EULAR/ACR Boolean criteria and MBDA score (less than or equal to 25).  The investigators reported that 93 % of patients in MBDA-defined remission did not experience progression, compared with 70 % of patients not in MBDA remission (p = 0.001).  The investigators reported that there were no significant differences in the fraction of non-progressers between patients in remission and those not in remission using either DAS28-CRP or EULAR/ACR criteria.  The PLR for non-progression over 12 months for MBDA remission was 4.73 (95 % CI: 1.67 to 15.0).  Among patients in DAS28-CRP remission, those with a high MBDA score were 2.3 times as likely (95 % CI: 1.1 to 3.7) to have joint damage progression during the next year.

Hambardzumyan et al (2015) evaluated the Vectra DA multi-biomarker disease activity (MBDA) score as a baseline predictor for 1-year radiographic progression in early rheumatoid arthritis.  Baseline disease activity score based on erythrocyte sedimentation rate (DAS28-ESR), disease activity score based on C-reactive protein (DAS28-CRP), CRP, MBDA scores and DAS28-ESR at 3 months were analyzed for 235 patients with early RA from the Swedish Farmacotherapy (SWEFOT) clinical trial.  Radiographic progression was defined as an increase in the Van der Heijde-modified Sharp score by more than 5 points over 1 year.  Associations between baseline disease activity measures, the MBDA score, and 1-year radiographic progression were evaluated using univariate and multivariate logistic regression, adjusted for potential confounders.  Among 235 patients with early RA, 5 had low and 29 moderate MBDA scores at baseline.  None of the former and only 1 of the latter group (3.4 %) had radiographic progression during 1 year, while the proportion of patients with radiographic progression among those with high MBDA score was 20.9 % (p = 0.021).  Among patients with low/moderate CRP, moderate DAS28-CRP or moderate DAS28-ESR at baseline, progression occurred in 14 %, 15 %, 14 % and 15 %, respectively.  MBDA score was an independent predictor of RP as a continuous (odds ratio [OR] = 1.05, 95 % CI: 1.02 to 1.08) and dichotomized variable (high versus low/moderate, OR = 3.86, 95 % CI: 1.04 to 14.26).  The authors concluded that, in patients with early RA, the MBDA score at baseline was a strong independent predictor of 1-year radoiographic progression.

Li et al (2013) assessed how use of Vectra CA affects treatment decisions made by health care providers (HCPs) in clinical practice.  At routine office visits, 101 patients with RA were assessed by their HCPs (n = 6), and they provided blood samples for MBDA testing.  HCPs completed surveys before and after viewing the MBDA test result, recording dosage and frequency for all planned RA medications and physician global assessment of disease activity.  Frequency and types of change in treatment plan that resulted from viewing the MBDA test result were determined.  The primary outcome measure was the percentage of cases in which the HCP changed the planned treatment after viewing the MBDA test result.  Prior to HCP review of the MBDA test, DMARD use by the 101 patients included methotrexate in 62 % of patients; hydroxychloroquine 29 %; TNF inhibitor 42 %; non-TNF inhibitor biologic agent 19 %; and other drugs at lower frequencies.  Review of MBDA test results changed HCP treatment decisions in 38 cases (38 %), of which 18 involved starting, discontinuing or switching a biologic or non-biologic DMARD.  Other changes involved drug dosage, frequency or route of administration.  The total frequency of use of the major classes of drug therapy changed by less than 5 %.  Treatment plans changed 63 % of the time when the MBDA test result was perceived as being not consistent or somewhat consistent with the HCP assessment of disease activity.  The authors stated that study limitations include limited sample size, lack of control group, and no longitudinal follow-up.  This study did not report on whether the changes in clinical management with the MBDA test resulted in improved clinical outcomes.

Peabody et al (2013) reported on the use of the Vectra DA MBDA test in assessment and treatment decisions for simulated cases of RA.  Board-certified rheumatologists without prior experience with the MBDA test (n =  81) were randomized into an intervention or control group as part of a longitudinal randomized-control study.  All physicians were asked to care for 3 simulated RA patients, using Clinical Performance and Value (CPV) vignettes, in a before and after design.  CPV vignettes have been validated to assess the quality of clinical practice and identify variation in care.  The vignettes covered all domains of a regular patient visit; scores were determined as a percentage of explicit predefined criteria completed.  Three vignettes, representing typical RA cases, were administered each round.  In the first round, no physician received information about the MBDA test.  In the second round, only physicians in the intervention group were given educational materials about the test and hypothetical test results for each of the simulated patients.  The outcome measures were the overall quality of care, disease assessment and treatment.  The investigators reported that the overall quality scores in the intervention group improved by 3 % (p = 0.02) post-intervention compared with baseline, versus no change in the control group.  The greatest benefit in the intervention group was to the quality of disease activity assessment and treatment decisions, which improved by 12 percent (p < 0.01) compared with no significant change in the control group.  The intervention was associated with more appropriate use of biologic and/or combination DMARDs in the co-morbidity case type (p < 0.01).

Segurado and Sasso (2014) stated that quantitative and regular assessment of disease activity in RA is needed to achieve treatment targets such as remission and to optimize clinical outcomes. To assess inflammation accurately, predict joint damage and monitor treatment response, a measure of disease activity in RA should reflect the pathological processes resulting in irreversible joint damage and functional disability. The Vectra DA blood test supposedly provides an accurate, reproducible score on a scale of 1 to 100 based on the concentrations of 12 biomarkers that reflect the pathophysiologic diversity of RA. The analytical validity, clinical validity, and clinical utility of Vectra DA have been evaluated for patients with RA in registries and prospective and retrospective clinical studies. As a biomarker-based instrument for assessing disease activity in RA, the Vectra DA test can help monitor therapeutic response to methotrexate and biologic agents and assess clinically challenging situations, such as when clinical measures are confounded by non-inflammatory pain from fibromyalgia. Vectra DA scores correlate with imaging of joint inflammation and are predictive for radiographic progression, with high Vectra DA scores being associated with more frequent and severe progression and low scores being predictive for non-progression. The authors concluded that the Vectra DA score has the potential to complement conventional clinical and laboratory measures and optimize clinical decision-making.

Li, et al. (2015) evaluated the multi-biomarker disease activity (MBDA) score as a predictor of radiographic progression and compared it with other risk factors among patients with established RA receiving non-biologic DMARDs. For 163 patients with RA in the Leiden Early Arthritis Cohort, investigators retrospectively assessed 271 visits for MBDA score (scale of 1−100), clinical data and subsequent 1-year radiographic progression (change in Sharp−van der Heijde score [SHS]). Scatter plot and non-parametric quantile regression curves evaluated the relationship between the MBDA score and change in SHS. Changes in joint space narrowing and erosions were compared among MBDA categories with Wilcoxon rank-sum tests. The ability of The MBDA score to independently predict progression was determined by multivariate models and cross-classification of MBDA score with other risk factors. Generalized estimating equation methodology was used in model estimations to adjust for same-patient visits, always ≥1 year apart. Patient characteristics included 67% female, 66%/67% RF+/anti-CCP+; mean age 55 years, MBDA score 43 (moderate = 30−44); median disease duration 4.6 years, SHS 23. Radiographic progression was infrequent for low MBDA scores. Relative risk for progression increased continuously as the MBDA score increased, reaching 17.4 for change in SHS >5 with MBDA scores ≥60.  Joint space narrowing and erosion progression were associated with MBDA score. The investigators found that MBDA score was associated with radiographic progression after adjustments for other risk factors. The investigators stated that MBDA score significantly differentiated risk for progression when swollen joint count, CRP or DAS28–CRP was low, and among seropositive patients. The investigators concluded that MBDA score enhanced the ability of conventional risk factors to predict radiographic progression in patients with established RA receiving non-biologic DMARDs.

Hirata, et al. (2015) assessed the ability of a multi-biomarker disease activity (MBDA) score to track clinical response in patients with rheumatoid arthritis (RA) treated with different TNF inhibitors. The retrospective observational study included 147 patients who had received adalimumab, etanercept, or infliximab for a year or more, during routine clinical care at the University Hospital of Occupational and Environmental Health, Japan. MBDA scores and clinical measures of disease activity were evaluated at baseline and, after 24 weeks (N = 84) and 52 weeks of treatment. Relationships between the changes (∆) in MBDA score and changes in clinical measures or EULAR response categories were evaluated.  The median disease activity was 5.7 by DAS28-ESR and 64 by MBDA score at baseline, and decreased significantly with treatment. ∆MBDA scores over 1 year correlated with ∆DAS28-ESR (r = 0.48) and ∆DAS28-CRP (r = 0.46). Linear relationships between ∆MBDA scores and ∆DAS28-ESR or ∆DAS28-CRP were not significantly different between TNF inhibitors. The MBDA scores declined significantly more in good responders (median change: –29) than moderate (–21), and more in moderate than in non-responders (+ 2), by the EULAR criteria. The investigators concluded that MBDA scores tracked disease activity and treatment response in patients with RA treated with three TNF inhibitors. The relationships between ∆MBDA scores and ∆DAS28-ESR or ∆DAS28-CRP were consistent across the three TNF inhibitor groups.

Rech, et al. (2015) analyzed the role of multibiomarker disease activity (MBDA) score in predicting disease relapses in patients with rheumatoid arthritis (RA) in sustained remission who tapered disease modifying antirheumatic drug (DMARD) therapy in the RETRO study. MBDA scores (scale 1-100) were determined based on 12 inflammation markers in baseline serum samples from 94 patients of the RETRO study. MBDA scores were compared between patients relapsing or remaining in remission when tapering DMARDs. Demographic and disease-specific parameters were included in multivariate logistic regression analysis for defining predictors of relapse. Moderate-to-high MBDA scores were found in 33% of patients with RA overall. Twice as many patients who relapsed (58%) had moderate/high MBDA compared with patients who remained in remission (21%). Baseline MBDA scores were significantly higher in patients with RA who were relapsing than those remaining in stable remission (N=94; p=0.0001) and those tapering/stopping (N=59; p=0.0001). Multivariate regression analysis identified MBDA scores as independent predictor for relapses in addition to anticitrullinated protein antibody (ACPA) status. Relapse rates were low (13%) in patients who were MBDA-/ACPA-, moderate in patients who were MBDA+/ACPA- (33.3%) and MBDA-ACPA+ (31.8%) and high in patients who were MBDA+/ACPA+ (76.4%). The investigators concluded that MBDA improved the prediction of relapses in patients with RA in stable remission undergoing DMARD tapering. The investigators commented that, if combined with ACPA testing, MBDA allowed prediction of relapse in more than 80% of the patients.

Reiss, et al. (2015) evaluated the relationship between DA and MBDA scores and changes in MBDA component biomarkers in tocilizumab (TCZ)-treated patients. Patients from the ACT-RAY study were included in this analysis if they had DA measures and serum collected at pre-specified time points with sufficient serum for MBDA testing at ≥1 visit. Descriptive statistics, associations between outcomes, and percentage agreement between DA categories were calculated. Seventy-eight patients were included and were similar to the ACT-RAY population. Correlations between MBDA score and DAS28-CRP were ρ = 0.50 at baseline and ρ = 0.26 at week 24. Agreement between low/moderate/high categories of MBDA score and DAS28-CRP was observed for 77.1 % of patients at baseline and 23.7 % at week 24. Mean changes from baseline to weeks 4, 12, and 24 were proportionately smaller for MBDA score than DAS28-CRP. Unlike some other MBDA biomarkers, interleukin-6 (IL-6) concentrations increased in most patients during TCZ treatment. Correlations and agreement between MBDA and DAS28-CRP or CDAI scores were lower at week 24 versus baseline. The proportionately smaller magnitude of response observed for MBDA score versus DAS28-CRP may be due to the influence of the increase in IL-6 concentrations on MBDA score. The investigators concluded that MBDA scores obtained during TCZ treatment should be interpreted cautiously and in the context of available clinical information.


Safi and colleagues (2015) evaluated the prevalence of anti-MCV antibodies and RF and examined their association in RA patients, both Saudi and non-Saudi. These investigators retrospectively studied 280 RA patients, at King Abdulaziz University Hospital. The antibodies were measured by enzyme linked immunosorbent assay and RF by nephelometry. The 280 patients included 196 Saudis and 84 non-Saudis, 88 % females and 12 % males, and the mean age was 45.3 years (SD = 14.3). Prevalence of RF was 141/280 (50 %) -- 93/196 (47.5 %) Saudis and 48/84 (57 %) non-Saudis -- with no significant differences (p > 0.05). Prevalence of mutated citrullinated vimentin antibodies was 165/280 (58.2 %) -- 121/196 (61.7 %) Saudis and 44/84 (52.4 %) non-Saudis -- with no significant differences (p > 0.05). Among RF-negative patients, considerable numbers were anti-MCV positive, and vice versa. Also, among the anti-MCV negative patients, considerable numbers were RF-positive, and vice versa. In all cohorts and in Saudi and non-Saudi patients, anti-MCV positivity was significantly associated with RF positivity (OR 3.15; 95 % CI: 1.9 to 5.19, p = 0.000); ESR and CRP were high with significant correlation (p < 0.005) with each other, with RF positivity but not with anti-MC positivity. Anti-MC positivity showed no significant correlation with age and gender. The authors concluded that in this cohort of patients, anti-MCV antibodies are a useful diagnostic tool for RA, but its combination with RF is essential; both markers are significantly associated. They stated that larger scale studies are recommended; correlation of anti-MCV with treatment and with disease activity still has to be published.

Lee and colleagues (2015) compared the diagnostic performance of anti-MCV and anti-CCP antibodies in RA. These investigators searched the Medline, Embase, and Cochrane library databases and performed 2 meta-analyses on the diagnostic accuracy of anti-MCV and anti-CCP in patients with RA compared to healthy controls. They identified 12 studies that included a total of 2,003 RA patients and 831 healthy controls for the meta-analysis. The pooled sensitivity and specificity of anti-MCV were 68.6 % [95 % CI: 66.6 to 79.7] and 94.2 % (95 % CI: 92.4 to 96.7) and those of anti-CCP were 61.7 % (95 % CI: 59.5 to 63.8) and 97.1 % (95 % CI: 96.7 to 98.1), respectively. Anti-MCV PLR, NLR, and DOR were 12.99 (95 % CI: 8.013 to 21.27), 0.297 (95 % CI: 0.238 to 0.369), and 47.78 (95 % CI: 28.59 to 79.84), and those for anti-CCP were 16.71 (95 % CI: 11.42 to 24.47), 0.378 (95 % CI: 0.325 to 0.439), and 54.20 (95 % CI: 31.65 to 92.82), respectively. The AUC of anti-MCV was 0.886, and its Q* index was 0.817, indicating modest accuracy, while the AUC of anti-CCP was 0.946, and its Q* index was 0.885. The sensitivity of anti-MCV was significantly higher than that of anti-CCP in the diagnosis of RA (difference 0.069, 95 % CI: 0.039 to 0.098, p < 0.0001), but the specificity of anti-MCV was lower than that of anti-CCP (difference - 0.029, 95 % CI: - 0.051 to - 0.006, p = 0.012). The Q* index of anti-MCV was significantly lower than that of anti-CCP (difference - 0.068, 95 % CI: - 0.070 to - 0.065, p < 0.0001). The authors concluded that the findings of this meta-analysis demonstrated that anti-MCV is more sensitive but less specific, and has lower diagnostic accuracy than anti-CCP in RA, although anti-MCV and anti-CCP showed comparable high PLRs.

van Tuyl and Lems (2014) stated that recent qualitative research has shown that stiffness is an important symptom for patients with RA to identify remission. However, it is unclear how to measure stiffness in low disease activity. These researchers summarized the existing literature on validity of patient reported outcomes to measure stiffness in RA low disease activity states to aid the choice for a measurement instrument. An extensive PubMed search was undertaken, identifying measurement instruments for patient perceived stiffness used in low disease activity. Eligible studies reported on (i) stiffness as an outcome in relation to other core set measures, (ii) development of a patient reported tool to measure stiffness, or (iii) comparison of 2 different tools to measure aspects of stiffness, all in low disease activity. Of 788 titles, only 2 studies report on validity of stiffness measures within low disease activity. Morning stiffness (MS) is reported in 44 to 80 % of patients in low disease activity. A difference of 40 to 60 minutes in duration until maximum improvement is observed between active and inactive patients. Severity of MS might discriminate better between high and low disease activity compared to measurement of duration of MS. The authors concluded that re is insufficient data on measurement of stiffness in the spectrum of low disease activity or remission.

Halls et al (2015) noted that stiffness is internationally recognized as an important indicator of inflammatory activity in RA but is poorly understood and difficult to measure. These investigators explored the experience of stiffness from the patient perspective. Semi-structured interviews conducted with 16 RA patients were analyzed independently by researchers and patient partners using inductive thematic analysis. Six themes were identified: (i) Part of having RA identified stiffness as a normal consequence of RA, perceived as associated with disease-related aspects such as fluctuating disease activity, other RA symptoms and disease duration, (ii) local and widespread highlighted stiffness occurring not only in joints, but also over the whole body, being more widespread during the morning or flare, (iii) linked to behavior and environment illustrated factors that influence stiffness, including movement, medications and weather, (iv) highly variable captured the fluctuating nature of stiffness within and between patients and in relation to temporality, duration and intensity, (v) impacts on daily life emphasized the effect of stiffness on a range of domains, including physical function, quality of life, psychological well-being, activities of daily living and participation in work and leisure activities, and (vi) requires self-management detailed self-management strategies targeting both the symptom and its consequences. The authors concluded that patients' experiences of stiffness were varied, complex and not exclusive to the morning period. Importantly, stiffness was reported in terms of impact rather than the traditional measurement concepts of severity or duration. They stated that based on these findings, further research is needed to develop a patient-centered measure that adequately reflects inflammatory activity.

CPT Codes / HCPCS Codes / ICD-10 Codes
Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":
ICD-10 codes will become effective as of October 1, 2015:
There are no specific codes for Vectra DA multibiomarker disease activity (MBDA) test for rheumatoid arthritis, Avsie PG, Avise SLE, Avise SLE+ , or measurement of isoforms of 14-3-3 protein (beta, gamma, epsilon, eta, sigma, theta, and zeta) as biomarkers of osteoarthritis and rheumatoid arthritis.
Cyclic Citrullinated Peptide (CCP):
CPT codes covered for indications listed in the CPB:
86200 Cyclic citrullinated peptide (CCP), antibody
ICD-10 codes covered if selection criteria are met:
M05.00 - M14.89 Rheumatoid arthritis and other inflammatory polyarthropathies
Myositis Antibody Panel:
CPT codes covered for indications listed in the CPB:
83516 Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; qualitative or semiquantitative, multiple step method [Myositis Antibody Panel]
CPT codes not covered for indications listed in the CPB:
83520 Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; quantitative, not otherwise specified [anti-mutated citrullinated vimetin (MCV) antibodies (e.g., the Avise MCV test)]
ICD-10 codes covered if selection criteria are met:
G72.49 Other inflammatory and immune myopathies, NEC
ICD-10 codes not covered for indications listed in the CPB:
M15.0 - M19.93 Osteoarthritis and allied disorders
M32.0 - M32.9 Systemic lupus erythematosus (SLE)

The above policy is based on the following references:
    1. Amato AA, Barohn RJ. Idiopathic inflammatory myopathies. Neurol Clin. 1997;15(3):615-648.
    2. Mastaglia FL, Phillips BA. Idiopathic inflammatory myopathies: Epidemiology, classification, and diagnostic criteria. Rheum Dis Clin North Am. 2002;28(4):723-741.
    3. Targoff IN. Laboratory testing in the diagnosis and management of idiopathic inflammatory myopathies. Rheum Dis Clin North Am. 2002;28(4):859-890.
    4. Kilani RT, Maksymowych WP, Aitken A, et al. Detection of high levels of 2 specific isoforms of 14-3-3 proteins in synovial fluid from patients with joint inflammation. J Rheumatol. 2007;34(8):1650-1657.
    5. Priam S, Bougault C, Houard X, et al. Identification of soluble 14-3-3∊ as a novel subchondral bone mediator involved in cartilage degradation in osteoarthritis. Arthritis Rheum. 2013;65(7):1831-1842.
    6. Venables PJW, Maini RN. Diagnosis and differential diagnosis of rheumatoid arthritis. UpToDate [serial online]. Waltham, MA: UpToDate; reviewed August 2014a.
    7. Venables PJW, Maini RN. Clinical manifestations of rheumatoid arthritis. UpToDate [serial online]. Waltham, MA: UpToDate; reviewed August 2014b.
    8. Sun J, Zhang Y, Liu L, Liu G. Diagnostic accuracy of combined tests of anti cyclic citrullinated peptide antibody and rheumatoid factor for rheumatoid arthritis: A meta-analysis. Clin Exp Rheumatol. 2014;32(1):11-21.
    9. Zhang WC, Wu H, Chen WX. Meta-analysis: Diagnostic accuracy of anti-cyclic citrullinated peptide 2 antibody and anti-cyclic citrullinated peptide 3 antibody in rheumatoid arthritis. Clin Chem Lab Med. 2014;52(6):779-790.
    10. Hambardzumyan K, Bolce R, Saevarsdottir S, et al. Pretreatment multi-biomarker disease activity score and radiographic progression in early RA: Results from the SWEFOT trial. Ann Rheum Dis. 2015;74(6):1102-1109.
    11. van der Helm-van Mil AH, Knevel R, et al. An evaluation of molecular and clinical remission in rheumatoid arthritis by assessing radiographic progression. Rheumatology (Oxford). 2013;52(5):839-846.
    12. Markusse IM, Dirven L, van den Broek M, et al. A multibiomarker disease activity score for rheumatoid arthritis predicts radiographic joint damage in the BeSt Study. J Rheumatol. 2014;41(11):2114-2119.
    13. Hirata S, Dirven L, Shen Y, et al. A multi-biomarker score measures rheumatoid arthritis disease activity in the BeSt study. Rheumatology (Oxford). 2013;52(7):1202-1207.
    14. Curtis JR, van der Helm-van Mil AH, Knevel R, et al. Validation of a novel multibiomarker test to assess rheumatoid arthritis disease activity. Arthritis Care Res (Hoboken). 2012;64(12):1794-1803.
    15. Li W, Sasso EH, Emerling D, et al. Impact of a multi-biomarker disease activity test on rheumatoid arthritis treatment decisions and therapy use. Curr Med Res Opin. 2013;29(1):85-92.
    16. Bakker MF, Cavet G, Jacobs JW, et al. Performance of a multi-biomarker score measuring rheumatoid arthritis disease activity in the CAMERA tight control study. Ann Rheum Dis. 2012;71(10):1692-1697.
    17. Centola M, Cavet G, Shen Y, et al. Development of a multi-biomarker disease activity test for rheumatoid arthritis. PLoS One. 2013;8(4):e60635.
    18. Eastman PS, Manning WC, Qureshi F, et al. Characterization of a multiplex, 12-biomarker test for rheumatoid arthritis. J Pharm Biomed Anal. 2012;70:415-424.
    19. Peabody JW, Strand V, Shimkhada R, et al. Impact of rheumatoid arthritis disease activity test on clinical practice. PLoS One. 2013;8(5):e63215. 
    20. Segurado OG, Sasso EH. Vectra DA for the objective measurement of disease activity in patients with rheumatoid arthritis. Clin Exp Rheumatol. 2014;32(5 Suppl 85):S29-S34
    21. van Tuyl LH, Lems WF, Boers M. Measurement of stiffness in patients with rheumatoid arthritis in low disease activity or remission: A systematic review. BMC Musculoskelet Disord. 2014;15:28
    22. Halls S, Dures E, Kirwan J, et al. Stiffness is more than just duration and severity: A qualitative exploration in people with rheumatoid arthritis. Rheumatology (Oxford). 2015;54(4):615-622
    23. Safi MA, Attar SM, Fathaldin OA, Safi OM. Anti-mutated citrullinated vimentin antibody and rheumatoid factor (prevalence and association) in rheumatoid arthritis patients; Saudi and non-Saudi. Clin Lab. 2015;61(3-4):259-267
    24. Lee YH, Bae SC, Song GG. Diagnostic accuracy of anti-MCV and anti-CCP antibodies in rheumatoid arthritis : A meta-analysis. Z Rheumatol. 2015 Jun 27 [Epub ahead of print].
    25. Rech J, Hueber AJ, Finzel S, et al. Prediction of disease relapses by multibiomarker disease activity and autoantibody status in patients with rheumatoid arthritis on tapering DMARD treatment. Ann Rheum Dis. 2015 Oct 19. [Epub ahead of print].
    26. Reiss WG, Devenport JN, Low JM, et al. Interpreting the multi-biomarker disease activity score in the context of tocilizumab treatment for patients with rheumatoid arthritis. Rheumatol Int. 2015 May 31. [Epub ahead of print].
    27. Li W, Sasso EH, van der Helm-van Mil AH, Huizinga TW. Relationship of multi-biomarker disease activity score and other risk factors with radiographic progression in an observational study of patients with rheumatoid arthritis. Rheumatology (Oxford). 2015 Sep 18. [Epub ahead of print].
    28. Hirata S, Li W, Defranoux N, Cavet G, et al. A multi-biomarker disease activity score tracks clinical response consistently in patients with rheumatoid arthritis treated with different anti-tumor necrosis factor therapies: A retrospective observational study. Mod Rheumatol. 2015;25(3):344-349.


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