Rheumatic Diseases: Selected Tests

Number: 0866

Table Of Contents

Applicable CPT / HCPCS / ICD-10 Codes


Scope of Policy

This Clinical Policy Bulletin addresses rheumatic diseases: selected tests.

  1. Medical Necessity

    Aetna considers the following tests medically necessary:

    1. Measurement of anti-cyclic citrullinated peptide (anti-CCP) antibodies medically necessary for diagnosis of rheumatoid arthritis (RA).  
    2. Myositis antibody panel for diagnosing persons with inflammatory myopathy.
    3. Human leukocyte antigen (HLA)-B27 testing for diagnosis of ankylosing spondylitis and Reiter’s syndrome (reactive arthritis).
  2. Experimental and Investigational

    Aetna considers the following procedures/tests experimental and investigational because their effectiveness has not been established (not an all-inclusive list):

    • Anti-CCP antibodies for indications other than rheumatoid arthritis
    • Anti-CEP-1 IgG and anti-Sa IgG for evaluation of inflammatory polyarthropathy
    • Anti-mutated citrullinated vimentin (MCV) antibodies
    • Avise CTD assay, Avise SLE, and Avise SLE+ tests for ANA-positive individuals referred to a rheumatologist for the differential diagnosis of systemic lupus erythematosus (SLE) versus other connective tissue diseases (CTDs) 
    • Avise MTX (formerly Avise PG) to monitor methotrexate levels in rheumatoid arthritis
    • Avise SLE Monitor Test to follow disease activity of SLE
    • Cytokine IL-10 -1082 G/A, -592 C/A, and -819 C/T gene polymorphisms for juvenile RA
    • Evaluation of telomere length for risk of development of RA
    • Measurements of anti-carbamylated protein (anti-CarP) antibodies, PAD4 , and peripheral T-cells as biomarkers for RA
    • Measurement of circulating insulin-like growth factor-1 (IGF-1) levels for management of RA
    • Measurement of isoforms of 14-3-3 protein (beta, gamma, epsilon, eta, sigma, theta, and zeta) as biomarkers of osteoarthritis and RA
    • Myositis antibody panel for indications other than inflammatory myopathy
    • SLE-key Rule Out Test to rule out a diagnosis of SLE
    • Vectra DA test for RA and other indications.


CPT Codes / HCPCS Codes / ICD-10 Codes

Code Code Description

Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":

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:

81490 Autoimmune (rheumatoid arthritis), analysis of 12 biomarkers using immunoassays, utilizing serum, prognostic algorithm reported as a disease activity score
83520 Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; quantitative, not otherwise specified [anti-mutated citrullinated vimetin (MCV) antibodies

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:

M08.00 - M08.99 Other juvenile arthritis [juvenile idiopathic arthritis]
M15.0 - M19.93 Osteoarthritis and allied disorders
M32.0 - M32.9 Systemic lupus erythematosus (SLE)

Human leukocyte antigen (HLA)-B27:

CPT codes covered for indications listed in the CPB:

81374 HLA Class I typing, low resolution (eg, antigen equivalents); one antigen equivalent (eg, B*27), each
86812 HLA typing; A, B, or C (eg, A10, B7, B27), single antigen

ICD-10 codes covered if selection criteria are met:

M45.0 - M45.9 Ankylosing spondylitis
M02.30 - M02.39 Reiter's disease

Anti-CEP-1 IgG and anti-Sa IgG:

CPT codes not covered for indications listed in the CPB:

Anti-CEP-1 IgG , anti-Sa IgG - no specific code:

ICD-10 codes not covered for indications listed in the CPB:

M06.4 Inflammatory polyarthropathy

Avise MTX test:

CPT codes not covered for indications listed in the CPB:

Avise MTX - no specific code

ICD-10 codes not covered for indications listed in the CPB:

M05.00 - M06.9 Rheumatoid arthritis with rheumatoid factor

Avise SLE Monitor Test, SLE-key Rule Out test:

CPT codes not covered for indications listed in the CPB:

SLE-key Rule Out test - no specific code:

0312U Autoimmune diseases (eg, systemic lupus erythematosus [SLE]), analysis of 8 IgG autoantibodies and 2 cell-bound complement activation products using enzyme-linked immunosorbent immunoassay (ELISA), flow cytometry and indirect immunofluorescence, serum, or plasma and whole blood, individual components reported along with an algorithmic SLE-likelihood assessment

ICD-10 codes not covered for indications listed in the CPB:

M32.10 - M32.9 Systemic lupus erythematosus (SLE)

Insulin-like growth factor-1 (IGF-1):

CPT codes not covered for indications listed in the CPB:

84305 Somatomedin [Insulin-like growth factor-1 (IGF-1)]

ICD-10 codes not covered for indications listed in the CPB:

M05.60 - M05.69 Rheumatoid arthritis with involvement of other organs and systems
M05.70 - M05.79 Rheumatoid arthritis with rheumatoid factor without organ or systems involvement
M05.80 - M05.89 Other rheumatoid arthritis with rheumatoid factor
M06.00 - M06.09 Rheumatoid arthritis without rheumatoid factor

Cytokine panel:

CPT codes not covered for indications listed in the CPB:

Cytokine panel - no specific code:

ICD-10 codes not covered for indications listed in the CPB::

M08.00 - M08.09 Unspecified juvenile rheumatoid arthritis
M08.20 - M08.29 Juvenile rheumatoid arthritis with systemic onset
M08.3 Juvenile rheumatoid polyarthritis (seronegative)
M08.40 - M08.48 Pauciarticular juvenile rheumatoid arthritis

Telomere length:

CPT codes not covered for indications listed in the CPB:

Evaluation telomere length - no specific code:

ICD-10 codes not covered for indications listed in the CPB:

M05.00 - M05.9 Rheumatoid arthritis with rheumatoid factor
M06.00 - M06.9 Rheumatoid arthritis without rheumatoid factor
M08.00 - M08.9A Unspecified juvenile rheumatoid arthritis

Measurements of anti-CarP antibodies, PAD4, and peripheral T-cells as biomarkers:

CPT codes not covered for indications listed in the CPB:

Anti-CarP antibodies, PAD4 - no specific code:

86359 T cells; total count

ICD-10 codes not covered for indications listed in the CPB:

M05.00 - M05.9 Rheumatoid arthritis with rheumatoid factor
M06.00 - M06.9 Rheumatoid arthritis without rheumatoid factor
M08.00 - M08.9A Unspecified juvenile rheumatoid arthritis


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 Avise MTX test is a test that measures methotrexate polyglutamate levels in the red blood cell.  It is used to assess whether rheumatoid arthritis patients are on adequate levels of drug (metabolite marker testing). However, there is inadequate reliable data demonstrating the clinical utility of this test in improving clinical outcomes.

Kilani et al (2007)
  1. examined if 14-3-3 proteins were detectable in synovial fluid (SF) of patients with inflamed joints, and if so, what isoform(s); and
  2. 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. Rheumatoid arthritis (RA) disease activity multianalyte assay (e.g,, Vectra DA) measures concentrations of twelve serum proteins purportedly associated with RA disease activity. These concentrations are then applied in an algorithm to estimate a disease activity score. The panel measures the following proteins: C-reactive protein (CRP), epidermal growth factor (EGF), interleukin 6 (IL-6), leptin, matrix metalloproteinase 1 (MMP-1), matrix metalloproteinase 3 (MMP-3), resistin, serum amyloid (SAA), tumor necrosis factor receptor, type 1 (TNF-R1), vascular cell adhesion molecule 1 (VCAM-1), vascular endothelial growth factor A (VEGF-A) and YKL-40. 

Epidermal growth factor (EGF) is a protein that stimulates cells to enter mitosis and cell division. EGF promotes cell growth and differentiation, is essential in embryogenesis and is important in wound healing. Interleukin 6 (IL-6) is a cytokine derived from fibroblasts, macrophages and tumor cells. Leptin is a protein hormone that affects feeding behavior and hunger in humans. Matrix metalloproteinases (MMPs) are zinc dependent endopeptidases that hydrolyze proteins of the extracellular matrix. Endopeptidases are a large group of enzymes that catalyze the hydrolysis of peptide bonds in the interior of a polypeptide (small protein) chain or protein molecule. The extracellular matrix (ECM) refers to any substance produced by cells and excreted to the extracellular space within the tissues, serving as a scaffolding to hold tissues together and helping to determine their characteristics. Resistin is a cytokine secreted by fat cells into the circulation; a cytokine is a general term for nonantibody proteins released by a specific type of cell as part of the body's immune response. Serum amyloid (SAA) is a protein whose plasma concentrations increase in response to tissue injury or to inflammation. Tumor necrosis factor (TNF) refers to a group of cytokines family that can cause cell death. Vascular cell adhesion molecule 1 (VCAM-1) is a class of membrane proteins located on the surface of endothelial and synovial cells involved with binding of other cells. Vascular endothelial growth factor (VEGF) is asignal protein produced by cells that stimulates the growth of new blood vessels; it is part of the system that restores the oxygen supply to tissues when blood circulation is inadequate. YKL-40 is a type of glycoprotein that may be found in higher than normal amounts in the blood of patients with certain types of cancer and inflammatory diseases; it is also referred to as human cartilage glycoprotein 39.

According to the manufacturer, the Vectra DA 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 (2016) evaluated the 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 to 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 greater than or eqaul to 1 year apart.  Patient characteristics included 67 % female, 66 %/67 % RF+/anti-CCP+; mean age of 55 years, MBDA score 43 (moderate = 30 to 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 greate than 5 with MBDA scores geate than or equal to 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 sero-positive 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 MBDA score to track clinical response in patients with 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 Disease Activity Score 28-erythrocyte sedimentation rate (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 3 TNF inhibitors. The relationships between ∆MBDA scores and ∆DAS28-ESR or ∆DAS28-CRP were consistent across the 3 TNF inhibitor groups.

Rech et al (2016) analyzed the role of MBDA score in predicting disease relapses in patients with RA in sustained remission who tapered DMARD therapy in the RETRO study.  MBDA scores (scale 1 to 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 multi-variate 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).  Multi-variate 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 (2016) 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 greater than or equal to 1 visit.  Descriptive statistics, associations between outcomes, and percentage agreement between DA categories were calculated.  A total of 78 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 Clinical Disease Activity Index (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.

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
  1. stiffness as an outcome in relation to other core set measures,
  2. development of a patient reported tool to measure stiffness, or
  3. 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:

  1. 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,
  2. local and widespread highlighted stiffness occurring not only in joints, but also over the whole body, being more widespread during the morning or flare,
  3. linked to behavior and environment illustrated factors that influence stiffness, including movement, medications and weather,
  4. highly variable captured the fluctuating nature of stiffness within and between patients and in relation to temporality, duration and intensity,
  5. 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
  6. 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.

Hambardzumyan et al (2016) previously showed that the MBDA score (Vectra) in patients with newly diagnosed RA identified patients at risk for radiographic progression (RP).  These researchers evaluated the MBDA score at multiple time-points as a predictor of RP during 2 years of follow-up.  A subset of patients with RA (n = 220) from the Swedish Farmacotherapy (SWEFOT) trial were analyzed for MBDA score, DAS28, CRP and ESR at baseline (BL), month 3 and year 1, for predicting RP based on modified Sharp/van der Heijde scores at BL, year 1 and year 2.  Patients with persistently low MBDA (less than 30) scores or those with a decrease from moderate (30 to 44) to low MBDA scores, did not develop RP during 2 years of follow-up.  The highest risk for RP during 2 years of follow-up (42 %) was observed among patients with persistently high (greater than 44) MBDA scores.  Among methotrexate (MTX)  non-responders with a high MBDA score at BL or month 3, significantly more of those who received triple therapy (TT) had RP at year 2 compared with those who received anti-TNF therapy.  The authors concluded that a low MBDA score at BL, month 3 and year 1, was predictive for low risk of rapid RP (RRP) over 2 years of follow-up in patients with early RA, and this association was stronger (non-significantly) than associations with low DAS28, CRP or ESR.  They stated that the MBDA score may thereby become a useful tool for guiding treatment choices in individual patients.  The  drawbacks of this study were:

  1. it was based on one cohort, which was not a full representation of the RA population,
  2. the MTX responder population lacked MBDA data at year 1, which limited the  ability to perform analyses,
  3. due to the lack of information about cut-offs for CRP and ESR categories, these researchers  had to use non-validated cut-offs for CRP and tertiles for ESR in order to define low/moderate/high levels of disease activity.  This rendered the comparison with MBDA score more limited and the health-economic point of view cannot be evaluated,
  4. changes in the treatment of patients from different arms could affect the radiographic outcome, and
  5. in comparison of proportion of RRP within each therapy group, some subgroups of patients were very small. 

Considering this and the fact of multiple testing, the results of significant difference of proportions of RRP between TT and anti-TNF therapy groups need to be confirmed.

Fleischmann and colleagues (2016) evaluated the ability of a MBDA test (Vectra DA score) to reflect clinical measures of disease activity in patients enrolled in the AMPLE (abatacept versus adalimumab comparison in bioLogic-naivE RA subjects with background methotrexate) trial (NCT00929864).  In the AMPLE trial, patients with active RA who were naive to biologic agents and had an inadequate response to methotrexate (MTX) were randomized (1:1) to subcutaneous (SC) abatacept (125 mg, weekly) or SC adalimumab (40 mg, every 2 weeks), with background MTX, for 2 years; MBDA score was analyzed in serum samples collected at baseline, month 3, and years 1 and 2.  Adjusted mean change from baseline in MBDA score was compared for abatacept and adalimumab treatment groups.  Cross-tabulation was used to compare MBDA score and clinical measures of disease activity: CDAI, Simplified Disease Activity Index (SDAI), DAS28-CRP, and Routine Assessment of Patient Index Data (RAPID)-3.  A total of 318 patients were randomized to abatacept and 328 to adalimumab; MBDA data were available for 259 and 265 patients, respectively.  There was no association between MBDA score and disease activity defined by CDAI, SDAI, DAS28-CRP, or RAPID-3 in the abatacept and adalimumab treatment groups.  The authors concluded that MBDA score did not reflect clinical disease activity in patients enrolled in AMPLE and should not be used to guide decision-making in the management of RA, particularly in those patients who receive abatacept or adalimumab as a first biologic therapy.

Antibodies to Mutated Citrullinated Vimentin (MCV)

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.

Mutated citrullinated vimentin (MCV) is 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. 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.

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.

Zhang et al (2015) compared the diagnostic value of antibodies to MCV and some associated autoantibodies in juvenile idiopathic arthritis (JIA) and analyzed the relation between antibodies and inflammatory markers.  Antibodies to CCP and anti-MCV antibodies were detected by enzyme-linked immunosorbent assay (ELISA), anti-perinuclear factor (APF) and anti-keratin antibody (AKA) by indirect immunofluorescent assay, as well as RF by latex agglutination test in serum samples from 113 patients with JIA and 56 children without RA.  The positive rate of anti-MCV antibodies, anti-CCP antibodies, and RF was 16.8 %, 14.2 %, and 21.2 % in the JIA.  In the other group, the positive rate was 2.2 %, 2.2 %, and 6.5 %.  There was a significant difference between the 2 groups (χ(2) = 8.105, 6.337, 7.036, p < 0.05).  The positive rate of AKA and APF were not significantly different.  The area under the ROC curve of anti-MCV antibodies, anti-CCP antibodies, RF, AKA, APF was 0.579, 0.561, 0.578, 0.539, 0.505.  The positive rate of anti-MCV antibodies and anti-CCP antibodies were higher than other antibodies.  In the RF-positive poly-articular disease patients, they were higher than those in the other subtypes (p < 0.05).  Antibody levels were not significantly different (p > 0.05) from other subtypes.  The swollen joint counts and tender joint counts had a low correlation to anti-MCV antibodies, anti-CCP antibodies, RF, AKA and APF.  No correlation was found between ESR, CRP and anti-MCV antibodies, anti-CCP antibodies, RF, AKA and APF.  The authors concluded that the diagnostic value of anti-MCV antibodies was low for JIA.  The positive rate of anti-MCV antibodies was higher than the other antibodies in the classification of JIA.  There was a low correlation between anti-MCV antibodies, anti-CCP antibodies, RF, AKA, APF and swollen joint counts, tender joint counts.

Lipinska et al (2016) evaluated the diagnostic and prognostic value of anti-MCV antibodies in JIA comparing to anti-CCP.  A total of 30 children with confirmed JIA diagnosis and 20 children as a control group were included into the study.  Serum and synovial fluid levels of anti-CCP, anti-MCV, and immunoglobulin M- RF (IgM-RF) antibodies were assessed.  Anti-MCV was positive in 11/30 (36.6 %), whereas anti-CCP positivity was found in 12/30 (40 %) children with JIA.  Among 11 children with JIA positive for anti-MCV, 5 (45.5 %) were also positive for anti-CCP and among 18 JIA children negative for anti-CCP, 6 (33.33 %) were also anti-MCV positive; 6 out of 30 JIA children were found to be IgM-RF positive.  In general, 2 out of all those 11 anti-MCV-positive patients demonstrated oligo-arthritis and 9/11 had poly-articular type of onset.  Anti-MCV serum concentration correlated positively with anti-CCP (p = 0.004).  Almost 60 % of children in early stage of JIA were anti-MCV positive.  Levels of anti-CCP antibodies correlated positively with the disease activity (p = 0.0014) and radiological outcome (p = 0.00017).  In all synovial fluid samples, the concentration of autoantibodies was under the cut-off values.  The authors concluded that the findings of this study indicated that anti-MCV as well as anti-CCP antibodies may be helpful in the diagnosis of JIA, especially in the early course of the disease.  They stated that anti-MCV antibodies could identify a group of children with JIA that is negative for anti-CCP antibodies and RF.  However, it appeared that in JIA, anti-CCP rather than anti-MCV antibodies have impact on radiographic changes.  These preliminary findings from a small study (n = 30) need to be validated by well-designed studies.

Furthermore, UpToDate reviews on “Systemic juvenile idiopathic arthritis: Clinical manifestations and diagnosis” (Kimura, 2016), “Polyarticular juvenile idiopathic arthritis: Clinical manifestations, diagnosis, and complications” (Weiss, 2016a), and “Oligoarticular juvenile idiopathic arthritis” (Weiss, 2016b) do not mention measurement of anti-mutated citrullinated vimentin antibodies as a diagnostic tool.

Measurements of Anti-CEP-1 IgG and Anti-Sa IgG for Evaluation of Inflammatory Polyarthropathy

Iwaszkiewicz et al (2015) evaluate the prevalence and diagnostic significance of the anti-Sa compared with the widely used anti-CCP in patients with RA.  A total of 169 patients hospitalized at the Department of Rheumatology and Internal Medicine, Poznan University of Medical Sciences, Poznan, Poland, were enrolled in a cross-sectional study and divided into 2 groups.  The RA group comprised 41 patients diagnosed as having RA. The non-RA control group included 128 individuals with a variety of rheumatic disorders.  Serum anti-Sa and anti-CCP measurements were performed by enzyme-linked immunosorbent assay (ELISA).  The sensitivity and specificity of anti-Sa for the diagnosis of RA was 36.6 % and 96.9 %, respectively.  For the anti-CCP test, the sensitivity was 65.9 % and the specificity was 95.3 %.  Concomitant presence of anti-Sa and anti-CCP was determined in 36.6 % of the patients with RA, whereas isolated positivity of anti-Sa was not observed.  Anti-Sa positive RA patients had significantly higher anti-CCP levels compared to anti-Sa negative subjects (p < 0.05).  The authors concluded that with regard to the relatively low diagnostic sensitivity and the lack of cases identified by anti-Sa alone, they were unable to demonstrate any additional diagnostic value of the anti-Sa autoantibody in comparison to the anti-CCP autoantibody.  To the authors' best knowledge, this was the 1st study among Polish patients verifying the clinical utility of anti-Sa in the diagnosis of RA.

In a meta-analysis, Lee and Bae (2017) evaluated the diagnostic accuracy of anti-Sa and anti-RA33 antibodies in RA.  PubMed, Embase, and Cochrane library databases were searched for relevant studies, and 2 meta-analyses were performed to determine the diagnostic accuracy of anti-Sa and anti-RA33 antibodies in patients with RA.  The meta-analysis included 17 studies.  Pooled sensitivity and specificity of anti-Sa antibody were 39.5 % (95 % confidence interval [CI]: 36.5 to 42.4) and 96.8 % (95 % CI: 95.9 to 97.4), respectively, and those of anti-RA33 antibody were 31.8 % (95 % CI: 28.7 to 35.0) and 90.1 % (95 % CI: 87.8 to 92.1), respectively.  Positive likelihood ratio, negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) of anti-Sa antibody were 14.11 (95 % CI: 7.076 to 28.13), 0.607 (95 % CI: 0.558 to 0.703), and 22.76 (95 % CI: 11.10 to 46.69), respectively, and those of anti-RA33 antibody were 3.429 (95 % CI: 2.039 to 5.765), 0.761 (95 % CI: 0.681 to 0.851), and 4.597 (95 % CI: 2.602 to 8.121), respectively.  Area under the receiver operating characteristic [ROC] curve (AUC) and Q* index (maximal joint sensitivity and specificity, is the point on the ROC curve that intersects with the line of symmetry) for anti-Sa antibody were 0.558 and 0.543, respectively, while those for anti-RA33 antibody were 0.501 and 0.500, respectively.  The authors concluded that this meta-analysis indicated that both anti-Sa and anti-RA33 antibodies were highly specific but not sensitive for diagnosing RA.

Alunno et al (2018) noted that rheumatoid arthritis (RA) is an articular chronic inflammatory disease that in a subgroup of patients can also present with extra-articular manifestations (EAMs).  Despite intense investigation on this topic, reliable biomarkers for EAMs are lacking.  In recent years several anti-citrullinated protein antibodies (ACPAs), including those targeting anti-citrullinated alpha enolase peptide-1 (anti-CEP-1), have been identified in patients with RA.  Data about the ability of anti-CEP-1 to predict the development of erosive disease are conflicting and no evidence concerning their possible association with EAMs in RA is currently available.  These investigators examined the prevalence and significance of anti-CEP-1 with regard to the association with erosive disease and EAMs in a large cohort of patients with RA.  Anti-CCP and anti-CEP-1 antibodies have been assessed on serum samples of RA patients, healthy donors and patients with spondyloarthritis (SpA) using commercially available ELISA kits.  Anti-CEP-1 antibodies were detectable in over 40 % of RA patients and were associated with erosive RA and with RA-associated interstitial lung disease (ILD).  The authors concluded that anti-CEP-1 antibodies may represent a useful biomarker for RA-associated ILD and erosive disease to be employed in clinical practice.

Meyer et al (2018) performed a retrospective comparison of the prevalence and diagnostic value of autoantibody against citrullinated vimentin (anti-Sa), anti-CEP-1, and anti-MCV autoantibodies relative to those of the established autoantibodies, composite rheumatoid factor (RF) and anti-CCP-IgG used routinely for RA diagnosis as a component of the ACR 2010 criteria, in a cohort of disease-modifying anti-rheumatic drug naive African RA patients (n = 75).  Serum concentrations of anti-Sa, anti-CEP-1 and anti-MCV autoantibodies were measured using ELISA procedures, while anti-CCP-IgG antibodies were determined by fluorescence enzyme immunoassay, and composite RF by latex-enhanced laser nephelometry.  The sero-positivity frequencies of anti-Sa, anti-CEP-1 and anti-MCV antibodies for the RA patients were 82, 72, 85 %, respectively, while that of anti-CCP-IgG and RF was 87 % for both.  Overall, anti-MCV demonstrated the best specificity, positive predictive value (PPV), odds ratio (ORs) and positive likelihood ratio (PLR) of all the types of autoantibody tested.  The authors concluded that these observations in this unique cohort of RA patients indicated novel associations of all 3 autoantibodies in regard to HLA-SE risk alleles, disease severity and tobacco use that were not reported before.  Elevated anti-Sa titers designated a propensity of higher disease and high-risk alleles in this cohort.  Anti-CEP-1 association with HLA-SE homozygosity and high-risk alleles is also novel in this group.  Of note, measurement of anti-MCV antibodies on presentation, either as an adjunctive or even as a stand-alone test, surpassed all other biomarkers investigated here and, therefore, may add value to clinical management.

The Avise Lupus to Aid in the Diagnosis of Systemic Lupus Erythematosus

Systemic lupus erythematosis (SLE) is an immune complex mediated disease, and because of symptom heterogeneity can be difficult to diagnose. Several other connective tissue diseases may present with similar symptoms, but are treated with different medications. AVISE Lupus (formerly AVISE SLE 2.0) is intended for use in persons with suspected lupus or related connective tissue diseases who are referred to a rheumatologist for evaluation of symptoms and a positive anti-nuclear antibody (ANA).

AVISE Lupus is a laboratory developed test (LDT) with a two-tiered testing method that is intended to increase the overall accuracy in diagnosing SLE. The test uses a proprietary algorithm that measures 10 markers to provide an index score that is indicative of presence or absence of SLE to aid in a timely and accurate diagnosis of SLE. AVISE Lupus contains the following 10 components: anti-double-stranded DNA (anti-dsDNA) by ELISA with Crithidia IFA confirmation; ANA by ELISA; erythrocyte-bound C4d (EC4d); B-lymphocyte bound CD4d (BC4d); anti-Smith; anti-SSB/La; anti-Jo-1; anti-CCP; anti-Centromere B; and anti-Scl70.

In addition to serological markers supporting the diagnosis of SLE, AVISE Lupus uses cell-bound complement activation products (CB-CAPs) to measure activation of the complement system. The test is composed of a two-tier testing method - tier-one tests for markers that are specific to SLE (i.e., anti-dsDNA, anti-Sm and erythrocyte-bound C4d (EC4d) or B-lymphocyte-bound C4d (BC4d) levels). If tier-one is negative, tier-two provides an index score and an assessment of three components: ANA, CB-CAPs, and autoantibody specificity components (anti-cyclic citrullinated peptide [CCP] for rheumatoid arthritis, anti-SS-B/La for Sjogren’s disease, centromere protein B [CENP] and scleroderma [Scl-70] for scleroderma, and Jo-1 for polymyositis/dermatomyositis). The test results are provided in a interpretive report.

AVISE CTD includes AVISE Lupus together with the following autoantibody markers to assist in differential diagnosis: anti-U1-RNP, anti-RNP70, anti-Ro52, anti-Ro60, anti-RNA polymerase III, rheumatoid factor IgM, rheumatoid factor IgA, anti-cardiolipin IgM, anti-cardiolipin IgG, anti-β2-glycoprotein I IgG, anti-β2-glycoprotein I IgM, anti-thyroglobulin IgG, and anti-thyroid peroxidase IgG. The intended use of the AVISE CTD is for use in ANA positive persons referred to a rheumatologist with signs and symptoms suggestive of connective tissue disease. 

Manzi et al (2004) stated that C4-derived activation fragments are the only complement ligands present on the surfaces of normal erythrocytes.  The significance of this observation is unknown, and the role of erythrocyte-bound C4 (E-C4) in human disease has not been explored.  More than any other human disease, the pathogenesis of systemic lupus erythematosus (SLE) has been characterized by defects in clearance of complement-bearing immune complexes via erythrocytes expressing complement receptor 1 (CR1).  These researchers examined if these functional defects might be reflected by abnormal patterns of E-C4 and E-CR1 expression on erythrocytes of patients with SLE.  They conducted a cross-sectional study of 100 patients with SLE, 133 patients with other diseases, and 84 healthy controls.  Erythrocytes were characterized by indirect immunofluorescence and by flow cytometry for determination of levels of C4d and CR1.  Patients with SLE had higher levels of E-C4d and lower levels of E-CR1 than did patients with other diseases (p < or = 0.001) or healthy controls (p < or = 0.001).  The test was 81 % sensitive and 91 % specific for SLE versus healthy controls and 72 % sensitive and 79 % specific for SLE versus other diseases, and it had an overall negative predictive value (NPV) of 92 %.  The authors concluded that this was the 1st report of abnormal levels of E-C4d in human disease.  They found that abnormally high levels of E-C4d and low levels of E-CR1 were characteristic of SLE, and combined measurement of the 2 molecules had high diagnostic sensitivity and specificity for lupus.  They stated that determination of E-C4d/E-CR1 levels may be a useful addition to current tests and criteria for SLE diagnosis.

Navratil et al (2006) noted that complement-activation product C4d is deposited on normal erythrocytes, while abnormal levels have been observed on the surface of erythrocytes of patients with SLE.  These investigators examined if C4d also deposited on human platelet surfaces, and whether platelet-bound C4d may provide a biomarker for SLE.  They conducted a cross-sectional study of 105 patients with SLE, 115 patients with other diseases, and 100 healthy controls.  Levels of C4d on the surface of platelets were examined by flow cytometry and scanning confocal microscopy.  Statistical analyses were performed to determine the clinical variables associated with platelet C4d.  Abnormal levels of platelet C4d were found to be highly specific for SLE.  Platelet C4d was detected in 18 % of patients with SLE, being 100 % specific for a diagnosis of SLE compared with healthy controls and 98 % specific for SLE compared with patients with other diseases (p < 0.0001).  In addition, platelet C4d was significantly associated with positivity for lupus anticoagulant (p < 0.0001) and anti-cardiolipin antibodies of the IgG (p = 0.035) or the IgM (P = 0.016) isotype.  Platelet C4d was also significantly associated with SLE disease activity according to the SLE Disease Activity Index (p = 0.039), low serum C4 (p = 0.046), an elevated erythrocyte sedimentation rate (p = 0.006), and abnormal levels of C4d on erythrocytes (p < 0.0001).  The authors concluded that this observation suggested that platelet-bound C4d may be a useful biomarker for SLE and may be a clue to the pathogenic mechanisms responsible for the myriad thrombotic and vascular complications of lupus associated with anti-phospholipid antibodies.

Putterman et al (2014) compared the performance characteristics of cell-bound complement (C4d) activation products (CBCAPS) on erythrocyte (EC4d) and B cells (BC4d) with antibodies to double-stranded DNA (anti-dsDNA) and complement C3 and C4 in patients with SLE.  The study enrolled 794 subjects consisting of 304 SLE and a control group consisting of 285 patients with other rheumatic diseases and 205 normal individuals.  Anti-dsDNA and other autoantibodies were measured using solid-phase immunoassays while EC4d and BC4d were determined using flow cytometry.  Complement proteins were determined using immuno-turbidimetry.  Disease activity in SLE was determined using a non-serological Systemic Lupus Erythematosus Disease Activity Index SELENA Modification.  A 2-tiered methodology combining CBCAPS with autoantibodies to cellular and citrullinated antigens was also developed.  Statistical analyses used area under receiver operating characteristic curves and calculations of area under the curve (AUC), sensitivity and specificity.  AUC for EC4d (0.82 ± 0.02) and BC4d (0.84 ± 0.02) was higher than those yielded by C3 (0.73 ± 0.02) and C4 (0.72 ± 0.02) (p < 0.01).  AUC for CBCAPS was also higher than the AUC yielded by anti-dsDNA (0.79 ± 0.02), but significance was only achieved for BC4d (p < 0.01).  The combination of EC4d and BC4d in multi-variate testing methodology with anti-dsDNA and autoantibodies to cellular and citrullinated antigens yielded 80 % sensitivity for SLE and specificity ranging from 70 % (Sjogren's syndrome) to 92 % (rheumatoid arthritis) (98 % versus normal).  A higher proportion of patients with SLE with higher levels of disease activity tested positive for elevated CBCAPS, reduced complement and anti-dsDNA (p < 0.03).  These researchers noted that the sensitivity of low complement, elevated CBCAPS, anti-dsDNA and their multi-variate 2-tiered method was compared between patients with various levels of disease activity as determined using the modified SELENA-SLEDAI sub-score (without the low complement and anti-dsDNA components).  Their analysis revealed that the sensitivity for elevated CBCAPS out-performed low complement among patients with active and inactive disease, and that higher sensitivity was observed using the multi-variate panel.  Therefore, elevated CBCAPS was more likely among patients with active disease, and these data suggested that CBCAPS could help monitor SLE disease activity.  Furthermore, the higher sensitivity of CBCAPS compared with reduced complement and anti-dsDNA was particularly significant in SLE having a modified SELENA-SLEDAI score of 0.  Thus, CBCAPS may be particularly important for diagnosing SLE in patients having less active disease, such as outpatients with early or mild SLE.  Whether the patients having inactive disease and complement activation will become clinically active is not known, and prospective study will help establish whether CBCAPS can predict disease flares.  The authors concluded that CBCAPS had a higher diagnostic sensitivity than reduced complement and anti-dsDNA; the assay panel combining CBCAPS with antibodies to cellular and citrullinated antigens is sensitive and specific for SLE and may be clinically useful to help diagnose SLE.

Ramsey-Goldman et al (2017) noted that diagnosis of SLE is based on clinical manifestations and laboratory findings.  Timely diagnosis and treatment are important to control disease activity and prevent organ damage.  However, diagnosis is challenging because of the heterogeneity in clinical signs and symptoms, and also because the disease presents with alternating periods of flare and quiescence.  As SLE is an autoimmune disease characterized by the formation of autoantibodies, diagnostic immunology laboratory tests for detecting and quantifying autoantibodies are commonly used for the diagnosis and classification of SLE.  These include ANA, anti-double-stranded DNA antibodies and anti-Smith antibodies, together with other antibodies such as antiphospholipid or anti-Cq1.  Complement proteins C3 and C4 are commonly measured in patients with SLE, but their serum levels do not necessarily reflect complement activation.  Cell-bound complement activation products (CB-CAPs) are fragments formed upon complement activation that bind covalently to hematopoietic cells.  These researchers focused on the complement system and, in particular, on CB-CAPs as biomarkers for the diagnosis and monitoring of SLE, vis-à-vis complement proteins and other biomarkers of complement activation.  They stated that a pilot study that enrolled patients with low disease activity and followed them prospectively showed that EC4d levels were higher at the visits when disease activity was higher, providing additional evidence that EC4d and/or other CB-CAPs, possibly in combination with other biomarkers, could be useful to monitor disease activity in patients with SLE.  The authors concluded that SLE is a heterogeneous disease with alternating periods of quiescence and exacerbation.  If not appropriately managed, SLE can lead to significant organ damage.  However, diagnosis and monitoring of disease are often challenging.  They stated that CB-CAPs, other biomarkers and assay panels may aid the diagnosis and monitoring of patients with this disease.

Wallace et al (2019) compared the physician-assessed diagnostic likelihood of SLE resulting from standard diagnosis laboratory testing (SDLT) to that resulting from multi-analyte assay panel (MAP) with cell-bound complement activation products (MAP/CB-CAPs), which reported a 2-tiered index test result having 80 % sensitivity and 86 % specificity for SLE.  Patients (n = 145) with a history of positive anti-nuclear antibody status were evaluated clinically by rheumatologists and randomized to SDLT arm (tests ordered at the discretion of the rheumatologists) or to MAP/CB-CAPs testing arm.  The primary end-point was based on the change in the physician likelihood of SLE on a 5-point Likert scale collected before and after testing.  Changes in pharmacological treatment based on laboratory results were assessed in both arms.  Statistical analysis consisted of Wilcoxon and Fisher's exact tests.  At enrolment, patients randomized to SDLT (n = 73, age = 48 ± 2 years, 94 % women) and MAP/CB-CAPs testing arms (n = 72, 50 ± 2 years, 93 % women) presented with similar pretest likelihood of SLE (1.42 ± 0.06 versus 1.46 ± 0.06 points, respectively; p = 0.68).  Post-test likelihood of SLE resulting from randomization in the MAP/CB-CAPs testing arm was significantly lower than that resulting from randomization to SDLT arm on review of test results (-0.44 ± 0.10 points versus -0.19 ± 0.07 points) and at the 12-week follow-up visit (-0.61 ± 0.10 points versus -0.31 ± 0.10 points) (p < 0.05).  Among patients randomized to the MAP/CB-CAPs testing arm, 2-tiered positive test results associated significantly with initiation of prednisone (p = 0.034).  The authors concluded that these findings suggested that MAP/CB-CAPs testing had clinical utility in facilitating SLE diagnosis and treatment decisions.

The authors stated that there were potential drawbacks in this trial.  First, long-term impact on health status and patient outcome was not collected after 12 weeks.  Second, the short time-frame of the study did not allow these researchers to collect long-term data, including fulfilment of the classification criteria for SLE or the impact of the diagnostic strategy on formal health outcome including healthcare utilization.  However, the improvement in the diagnostic certainty by rheumatologists with either a negative or positive MAP/CB-CAPs compared with SDLT, and the impact of the positive test results on the initiation of therapy and EQ-5D was likely to associate with changes in outcomes.  These researchers stated that in this 1st prospective, randomized, multi-center study, these findings suggested the clinical utility of a new diagnostic intervention to aid community-based rheumatologists and the challenging patients they manage.

Liang et al (2020) noted that the AVISE CTD test uses autoantibody, erythrocyte-bound C4d (EC4d) and B-cell-bound C4d (BC4d) levels to aid in diagnoses of SLE, other CTDs and fibromyalgia.  These researchers examined the utility of the AVISE CTD test in predicting SLE disease development and damage progression.  Patients who had undergone AVISE CTD testing were evaluated for SLE diagnosis by the Systemic Lupus International Collaborating Clinics (SLICC) and American College of Rheumatology (ACR) criteria and for SLE damage by the Systemic Lupus International Collaborating Clinics Damage Index (SDI) at the time of AVISE testing (t = 0) and 2 years later (t = 2).  Among 117 patients without a previous diagnosis of SLE, 65 % of patients who tested positive developed SLE at t = 2, compared with 10.3 % of patients who tested non-positive (p < 0.0001).  AVISE-positive patients fulfilled significantly more SLICC diagnostic criteria than AVISE-non-positive patients at both t = 0 (3.8 ± 2.1 versus 1.9 ± 1.1, p = 0.001) and t = 2 (4.5 ± 2.2 versus 2.1 ± 1.2, p < 0.0001).  AVISE-positive patients also had had significantly higher SDI at t = 2 (1.9 ± 1.3 versus 1.03 ± 1.3, p = 0.01).  BC4d levels correlated with the number of SLICC criteria at t = 0 (r = 0.33, p < 0.0001) and t = 2 (r = 0.34, p < 0.0001), as well as SDI at t = 0 (r = 0.25, p = 0.003) and t = 2 (r = 0.26, p = 0.002).  The authors concluded that the AVISE CTD test can aid in SLE evaluation by predicting SLE disease development and future damage progression.

The authors stated that the main drawbacks of this trial were the relatively small sample size, which was due to the recent introduction of AVISE testing into rheumatology practice and the observational nature of the study.  Nonetheless, this study was still able to detect significant differences and correlations at the p < 0.0001 level.  In addition, due to the retrospective nature of this study, these researchers were unable to exclude bias in the selection of therapeutic regimens in positive and non-positive patients; differences in treatment may have affected the probability of a changed diagnosis and damage score.  However, only mycophenolate mofetil (MMF) use was significantly different at t = 2 in AVISE positive patients, suggesting that more aggressive use of corticosteroids or other medications in the positive group was not a major contributor to clinical outcomes.  Another drawback of this trial was the use of the SDI to evaluate damage in non-SLE patients.  These investigators used it as a universal marker of damage in this study because the AVISE test was designed specifically for SLE.  Although SDI has not been validated for other rheumatological diagnoses, the authors still found significant differences in damage progression between positive and non-positive patients.  These researchers stated that future studies will evaluate the utility of AVISE in predicting longitudinal disease activity and flares.  These results will determine whether AVISE positivity can predict disease activity at shorter intervals.  In addition, a larger patient cohort would serve as a validation dataset to confirm these findings, allow generalization to a broader patient population and permit analysis of AVISE results with other sequelae of SLE, such as neurological or cardiovascular events.  Lastly, longer term follow-up of this patient cohort may reveal further associations between AVISE positivity and SLE development or progression.

Ramsey-Goldman et al (2020) examined the frequency of CB-CAPs as a marker of complement activation in patients with suspected SLE and the usefulness of this biomarker as a predictor of the evolution of probable SLE into SLE as classified by the ACR criteria.  Patients in whom SLE was suspected by lupus experts and who fulfilled 3 ACR classification criteria for SLE (probable SLE) were enrolled, along with patients with established SLE as classified by both the ACR and the SLICC criteria, patients with primary Sjogren's syndrome (SS), and patients with other rheumatic diseases.  Individual CB-CAPs were measured by flow cytometry, and positivity rates were compared to those of commonly assessed biomarkers, including serum complement proteins (C3 and C4) and autoantibodies.  The frequency of a positive MAP, which includes CB-CAPs, was also evaluated.  Probable SLE cases were followed-up prospectively.  The 92 patients with probable SLE were diagnosed more recently than the 53 patients with established SLE, and their use of anti-rheumatic medications was lower.  At the enrollment visit, more patients with probable SLE were positive for CB-CAPs (28 %) or MAP (40 %) than had low complement levels (9 %) (p = 0.0001 for each).  In probable SLE, MAP scores of greater than 0.8 at enrollment predicted fulfillment of a 4th ACR criterion within 18 months (hazard ratio [HR] 3.11, p < 0.01).  The authors concluded that complement activation occurred in some patients with probable SLE and could be detected with higher frequency by evaluating CB-CAPs and MAP than by assessing traditional serum complement protein levels.  A MAP score of above 0.8 predicted transition to classifiable SLE according to ACR criteria.

The authors stated that a main drawback of this study was the small cohort size of patients with probable SLE for whom these researchers had follow-up data, as well as the relatively short follow-up period.  However, 20 of the 69 patients (29 %) who had a follow-up visit within 18 months fulfilled a 4th ACR criterion in this time frame, confirming these investigators’ expectation that this cohort from academic lupus centers might be more likely to progress to classifiable SLE than prior studies have suggested.  Another drawback was that retrospective determination of whether patients fulfilled classification criteria was dependent on a comprehensive review of prior medical records and laboratory test results (including the presence of leukopenia or lymphopenia), and these may not have been available for all patients at every site.  The adjudicators often requested and received additional records.  However, the records the authors received may have been incomplete, as data on the lupus anti-coagulant test, for example, were rarely reported.  These researchers stated that the detection of complement activation in patients with probable lupus who do not fulfill ACR criteria or even SLICC criteria could have implications with regard to treatment, as early appropriate therapy in these patients may potentially slow the rate of disease progression.

In summary, there is insufficient evidence to support the use of the Avise Lupus Test.

The SLE-Key Rule Out Test to Rule Out a Diagnosis of Systemic Lupus Erythematosus

SLE-key is a blood test to measure a patient’s SLE-specific antibody fingerprint and immune system activity. SLE-key works by determining the pattern of circulating antibodies to an array of antigens which are printed on ImmunArray’s proprietary iCHIP. This pattern is compared to SLE affected and healthy control patterns. Analytic algorithms are then used to determine the likelihood of the patient being affected with SLE, along with a probability score.

Massenburg et al (2017) stated that a patient referred to a rheumatology clinic for workup of suspected Systemic Lupus Erythematosus (SLE) often presents a difficult diagnostic problem; until recently, there have been no objective tests validated to rule in or rule out SLE and the diagnosis is based on a list of criteria that may be open to interpretation. To approach this problem, a serologic rule out test for SLE was developed based on antigen microarray profiling of multiplex antibody reactivities. This SLE-key test was developed by ImmunArray and, using stored serum samples from recognized academic centers, was validated to rule out SLE with 94% sensitivity, 75% specificity and a negative predictive value (NPV) of 93%. In clinical practice, however, patients are referred one at a time from peripheral clinical units, often with incomplete documentation. The authors report here the usefulness of the SLE-key test in aiding the management of a cohort of suspected SLE patients in a large clinical rheumatology practice. The authors compared the diagnosis and disposition of 163 referrals in whom the SLE-key Rule-Out test was used to our typical experience with referrals before the test was available. This paper shows that the SLE-key test provided actionable clinical information and helped us with patient management in several ways; in some patients the authors were able to definitively rule out a diagnosis of SLE, saving time and evaluation costs; in other patients, the authors were able to accelerate the diagnosis of SLE and the initiation of therapy. The authors concluded that the SLE-key Rule-Out test increased efficiency in saving undue concern, time and resources both to the patient and to the healthcare system.

Measurement of Circulating Insulin-like Growth Factor-1 Levels for Management of Rheumatoid Arthritis

Zhao and colleagues (2019) noted that insulin-like growth factor-1 (IGF-1) levels have been examined in RA, however, produced inconsistent results.  In a meta-analysis , these researchers attempted to derive a more precise conclusion regarding serum / plasma IGF-1 levels in RA patients.  PubMed, Embase and the Cochrane Library databases were searched up to December 2018 in English, and the studies comparing serum / plasma IGF-1 levels between RA group and healthy control group were selected for analysis.  The Newcastle-Ottawa Scale (NOS) was used to assess the methodology quality of included studies.  Heterogeneity test was performed by the Cochrane Q statistic and I2 -statistic, publication bias was evaluated by funnel plot and Egger's test.  Standard mean difference (SMD) with 95 % CI were calculated by fixed-effects or random-effects model.  A total of 11 articles with 334 cases and 261 controls were finally included.  Compared with the healthy group, the RA group had lower circulating IGF-1 levels (pooled SMD = -0.936, 95 % CI: -1.382 to -0.489, p < 0.001).  Subgroup analysis showed that RA patients from Asia (SMD = -0.645, 95 % CI: -1.063 to -0.228, p = 0.002) and Europe (SMD = -1.131, 95%  CI: -1.767 to -0.495, p < 0.001) had lower circulating IGF-1 levels, no significant difference in plasma / serum IGF-1 levels was observed in RA patients from America.  Sensitivity analysis indicated the stability and credibility of the overall effect sizes.  The authors concluded that patients with RA had lower circulating IGF-1 level than healthy controls, particularly for patients from Asia and Europe.  These researchers stated that further studies are needed to elucidate the role of IGF-1 in the pathological process of RA.

Cytokine IL-10 -1082 G/A, -592 C/A, and -819 C/T Gene Polymorphisms for Juvenile Rheumatoid Arthritis

Harsini and colleagues (2018) noted that cytokine genes, including interleukin-10 (IL-10), are known to play important roles in the pathogenesis of juvenile idiopathic arthritis (JIA).  These researchers examined if the IL-10 polymorphisms confer susceptibility to JIA.  They carried out a meta-analysis on the associations between the IL-10 -1082 G/A, -592 C/A, and -819 C/T polymorphisms and JIA.  A total of 7 studies involving 1,785 patients and 6,142 controls were considered in the meta-analysis.  Meta-analysis of the IL-10 -592 C/A and -819 C/T polymorphisms showed no association with JIA in the study participants, or in Caucasian or Middle Eastern subjects.  Meta-analysis of the IL-10 -1082 A allele in all study participants, Caucasian and Middle Eastern, showed significant associations with RA (overall ORs were 1.17, 1.15, and 1.41, respectively).  Meta-analysis of the AA versus GG genotype of the IL-10 -1082 G/A polymorphism revealed significant associations with JIA (OR = 3.66, 95% CI = 1.44-9.29, P = 0.006) in participants from Middle Eastern countries. Additionally, meta-analysis of the GG versus AA+GA genotypes of the IL-10 -1082 G/A polymorphism revealed the GG genotype as the protective factor against JIA in the Middle Eastern subgroup (OR = 0.44, 95 % CI: 0.20 to 0.94, p = 0,04).  Moreover, meta-analysis of the IL-10 -1082 A allele in 4 studies on Hardy-Weinberg equilibrium showed a significant association with JIA (OR = 1.17, 95 % CI: 1.07 to 1.28, p = 0.0009).  No association was found between the IL-10 (-1082, -819, -592) ACC, ATA, and GCC haplotypes and JIA.  The authors concluded that these results suggested that the IL-10 -1082 G/A polymorphism conferred susceptibility to JIA.

Furthermore, an UpToDate review on “Systemic juvenile idiopathic arthritis: Clinical manifestations and diagnosis” (Kimura, 2019) does not mention testing for these cytokine gene polymorphisms as a management tool.

Evaluation of Telomere Length for Risk of Development of Rheumatoid Arthritis

In a systematic review and meta-analysis, Zheng and colleagues (2020) evaluated the telomere length (TL) in patients with RA relative to that in controls and examined if TL is causally associated with risk of RA.  These researchers evaluated relevant literature to examine the association between TL and RA; SMDs with 95 % CIs of TL in RA patients relative to controls were pooled using fixed or random-effects models.  TL-related single-nucleotide polymorphisms (SNPs) were selected from a genome-wide association (GWA) study of 37,684 individuals, and summary statistics of RA were obtained from a GWA study meta-analysis including 14,361 RA patients and 43,923 controls.  Mendelian randomization was performed using the inverse-variance weighted, weighted-median and likelihood-based methods.  Sensitivity analyses were performed to test the robustness of the association.  In the meta-analysis of 911 RA patients and 2,498 controls, these investigators found that patients with RA had a significantly shorter TL compared with controls (SMD = -0.50; 95 % CI: -0.88 to -0.11; p = 0.012).  In the Mendelian randomization analysis, these researchers found that genetically predicted longer TL was associated with a reduced risk of RA [OR = 0.68; 95 % CI: 0.54 to 0.86; p = 0.002 using the inverse-variance weighted method].  Sensitivity analyses using alternative Mendelian randomization approaches yielded similar findings, suggesting the robustness of the causal association.  The authors concluded that the findings of this study provided evidence for a negative causal association of TL with risk of RA.  Moreover, these researchers stated that further studies are needed to elucidate the underlying mechanism for the role of telomeres in the development of RA.

Measurements of Anti-Carbamylated Protein (Anti-CarP) Antibodies, PAD4, and Peripheral T-Cells as Biomarkers for Rheumatoid Arthritis

Challener et al (2016) noted that the presence of anti-citrullinated protein antibodies (ACPA) in RA indicates a breach in immune tolerance.  Recent studies indicated that this breach extends to homo-citrullination of lysines with the formation of anti-carbamylated protein (anti-CarP) antibodies.  These researchers analyzed the clinical and serologic relationships of anti-CarP in 2 RA cohorts.  Circulating levels of immunoglobulin G anti-CarP antibodies were determined by ELISA in established (Dartmouth-Hitchcock Medical Center) and early (Sherbrooke University Hospital Center) cohorts and evaluated for anti-CCP, specific ACPA, and RF levels using the Student t-test and correlation analysis.  These investigators identified elevated anti-CarP antibodies titers in 47.0 % of sero-positive patients (Dartmouth, n = 164), with relationships to anti-CCP (p < 0.0001) and IgM-RF (p = 0.001).  Similarly, 38.2 % of sero-positive patients from the Sherbrooke cohort (n = 171) had elevated anti-CarP antibodies; titers correlated to anti-CCP (p = 0.01) but not IgM-RF (p = 0.09).  A strong correlation with anti-Sa was observed: 47.9 % anti-Sa+ patients were anti-CarP antibodies+ versus only 25.4 % anti-Sa- in the Sherbrooke cohort (p = 0.0002), and 62.6 % anti-Sa+ patients versus 26.9 % anti-Sa- were anti-CarP antibodies+ in Dartmouth (p < 0.0001).  These researchers found a more variable response for reactivity to citrullinated fibrinogen or to citrullinated peptides from fibrinogen and α enolase.  The authors concluded that in 2 North American RA cohorts, they observed a high prevalence of anti-CarP antibody positivity.  These investigators also described a surprising and unexpected association of anti-CarP with anti-Sa antibodies that could not be explained by cross-reactivity.  Further, considerable heterogeneity exists between anti-CarP reactivity and other citrullinated peptide reactivity, raising the question of how the pathogenesis of antibody responses for carbamylated proteins and citrullinated proteins may be linked in-vivo.

An UpToDate review on “Diagnosis and differential diagnosis of rheumatoid arthritis” (Venables, 2020) does not mention anti-carbamylated protein (anti-CarP) antibodies, PAD4, or peripheral T-cells as biomarkers for RA.

Furthermore, an UpToDate review on “Biologic markers in the diagnosis and assessment of rheumatoid arthritis” (Taylor, 2020) does not mention CarP or PAD4 as biomarkers for rheumatoid arthritis.  Moreover it states that “Other immune changes investigated as potential biomarkers in RA include enhanced expression of CD40 ligand (CD154) on activated T cells and markers of complement activation, including the concentration of covalently linked C1q-C4 complement components”.

Human Leukocyte Antigen (HLA)-B27 Testing for Diagnosis of Ankylosing Spondylitis and Reiter’s Syndrome (Reactive Arthritis)

In an observational, cohort study, Glintborg and associates (2017) compared baseline disease activity and treatment effectiveness in biologic-naive patients with non-radiographic axial spondyloarthritis (nr-axSpA) and ankylosing spondylitis (AS) who initiated tumor necrosis factor inhibitor (TNFi) treatment and examined the role of potential confounders (e.g., Human leukocyte antigen (HLA)-B27 status).  This study was based on prospectively registered data in the nationwide DANBIO registry.  These researchers used Kaplan-Meier plots, Cox, and logistic regression analyses to examine the effect of diagnosis (nr-axSpA vs AS) and potential confounders (sex/age/start year/HLA-B27/disease duration/TNFi-type/smoking/baseline disease activity) on TNFi adherence and response [e.g., Bath Ankylosing Spondylitis Activity Index (BASDAI) 50%/20 mm].  This trial included 1,250 TNFi-naive patients with axSpA (29 % nr-axSpA, 50 % AS, 21 % lacked radiographs of sacroiliac joints).  Patients with nr-axSpA were more frequently women (50 %/27 %) and HLA-B27-negative (85/338 = 25 %), compared to AS (81/476 = 17 %; p < 0.01).  At TNFi start, patients with nr-axSpA had higher visual analog scale (VAS) scores [median (quartiles)] for pain: 72 mm (55 to 84)/65 mm (48 to 77); global: 76 mm (62 to 88)/68 mm (50 to 80); fatigue: 74 mm (55 to 85)/67 mm (50 to 80); and BASDAI: 64 (54 to 77)/59 (46 to 71); all p < 0.01.  However, patients with nr-axSpA had lower CRP: 7 mg/L (3 to 17)/11 mg/L (5 to 22); and BAS Metrology Index: 20 (10 to 40)/40 (20 to 50); all p < 0.01.  Median (95 % CI) treatment adherence was poorer in nr-axSpA than in AS: 1.59 years (1.15 to 2.02) versus 3.67 years (2.86 to 4.49), p < 0.0001; but only in univariate and not confounder-adjusted analyses (p > 0.05).  Response rates were similar in AS and nr-axSpA (p > 0.05).  HLA-B27 negativity was associated with poorer treatment adherence [HLA-B27 negative/positive, nr-axSpA: HR 1.74 (1.29 to 2.36), AS: HR 2.04 (1.53 to 2.71), both p < 0.0001]; and lower response rates (nr-axSpA: 18/61 = 30 % versus 93/168 = 55 %; AS: 17/59 = 29 % versus 157/291 = 54 %, both p < 0.05).  The authors concluded that in this nationwide cohort, patients with nr-axSpA had higher subjective disease activity at start of 1st TNFi treatment, but similar outcomes to patients with AS after confounder adjustment; HLA-B27 positivity was associated with better outcomes irrespective of axSpA sub-diagnosis.

Hwang and colleagues (2021) noted that AS is a chronic immune-mediated arthritis characterized by inflammation of the axial skeleton, peripheral joints, and entheses.  It is estimated that 1 in every 200 individuals are affected by AS.  In a systematic review, these investigators examined the current understanding of AS risk factors and provided a comprehensive update.  Multiple search strings were used to identify articles of interest published in PubMed between January 1, 2013, and February 1, 2021.  On the basis of the literature review and analysis, these researchers presented up-to-date information on the risk factors of developing AS and their views on disease onset and progression.  Multiple genetic and non-genetic risk factors have been suggested in the onset of AS.  HLA-B27 is known to have a strong association with the disease; however, other genes have been implicated in disease development.  In addition to genetics, other factors are thought to be involved; up to 70 % of patients with AS have subclinical intestinal inflammation, suggesting that the origin of the disease may be in the gut.  The exact mechanism by which AS onset begins is most likely complex and multi-factorial, which will require much future research.

An UpToDate review on “Diagnosis and differential diagnosis of axial spondyloarthritis (ankylosing spondylitis and nonradiographic axial spondyloarthritis) in adults” (Yu and van Tubergen, 2021a) states that “… the diagnosis of AS is very likely in a patient presenting before age 45 with a > 3-month history of back pain, positive testing for human leukocyte antigen (HLA)-B27, and obvious erosions and/or fusion of sacroiliac (SI) joints on plain radiographs”.

Furthermore, an UpToDate review on “Reactive arthritis” (Yu and van Tubergen, 2021b) states that “The prevalence of HLA-B27, which is increased in patients with the various forms of spondyloarthritis (SpA), including reactive arthritis, is generally estimated at 30 to 50 % in patients with reactive arthritis, although values range widely.  In hospital-based studies with more severely affected patients, frequencies as high as 60 to 80 % have been reported; however, estimates in population-based studies and analyses of disease outbreaks are generally much lower and occasionally have shown no increase in HLA-B27 prevalence compared with the general population … We obtain HLA-B27 testing in patients with an intermediate likelihood of reactive arthritis.  The prevalence of HLA-B27 in patients with reactive arthritis is generally estimated at 30 to 50 %.  Thus, a positive test would increase the likelihood of reactive arthritis being the correct diagnosis, rather than a different form of arthritis, other than another type of SpA.  A negative HLA-B27 test does not exclude reactive arthritis … Some patients with chronic reactive arthritis later develop features characteristic of another of the spondyloarthritides, e.g., psoriatic arthritis, ankylosing spondylitis, or the arthritis associated with inflammatory bowel disease.  Human leukocyte antigen (HLA)-B27 testing has been associated with a worse prognosis in some, but not all studies, with findings suggesting that patients who are HLA-B27-positive are more likely to develop a chronic spondyloarthropathy with radiographic changes”.


The above policy is based on the following references:

  1. Alunno A, Bistoni O, Pratesi F, et al. Anti-citrullinated alpha enolase antibodies, interstitial lung disease and bone erosion in rheumatoid arthritis. Rheumatology (Oxford). 2018;57(5):850-855.
  2. Amato AA, Barohn RJ. Idiopathic inflammatory myopathies. Neurol Clin. 1997;15(3):615-648.
  3. 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.
  4. Buyon J, Furie R, Putterman C, et al. Reduction in erythrocyte-bound complement activation products and titres of anti-C1q antibodies associate with clinical improvement in systemic lupus erythematosus. Lupus Sci Med. 2016;3(1):e000165.
  5. 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.
  6. Challener GJ, Jones JD, Pelzek AJ, et al. Anti-carbamylated protein antibody levels correlate with anti-Sa (citrullinated vimentin) antibody levels in rheumatoid arthritis. J Rheumatol. 2016;43(2):273-281.
  7. Clarke AE, Weinstein A, Piscitello A, et al. Evaluation of the economic benefit of earlier systemic lupus erythematosus (SLE) diagnosis using a multivariate assay panel (MAP). ACR Open Rheumatol. 2020;2(11):629-639.
  8. 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.
  9. Dervieux T, Conklin J, Ligayon JA, et al. Validation of a multi-analyte panel with cell-bound complement activation products for systemic lupus erythematosus. J Immunol Methods. 2017;446:54-59.
  10. 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.
  11. Fleischmann R, Connolly SE, Maldonado MA, Schiff M. Estimating disease activity using multi-biomarker disease activity scores in patients with rheumatoid arthritis treated with abatacept or adalimumab. Arthritis Rheumatol. 2016;68(9):2083-2089.
  12. Glintborg B, Sorensen IJ, Ostergaard M, et al. Ankylosing spondylitis versus nonradiographic axial spondyloarthritis: Comparison of tumor necrosis factor inhibitor effectiveness and effect of HLA-B27 status. An observational cohort study from the nationwide DANBIO registry. J Rheumatol. 2017;44(1):59-69.
  13. 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
  14. 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.
  15. Hambardzumyan K, Bolce RJ, Saevarsdottir S, et al. Association of a multibiomarker disease activity score at multiple time-points with radiographic progression in rheumatoid arthritis: Results from the SWEFOT trial. RMD Open. 2016;2(1):e000197.
  16. Harsini S, Saghazadeh A, Nedjat S, Rezaei N. Associations between interleukin-10 polymorphisms and susceptibility to juvenile idiopathic arthritis: A systematic review and meta-analysis. Eur Cytokine Netw. 2018;29(1):16-26.
  17. 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.
  18. 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.
  19. Hwang MC, Ridley L, Reveille JD. Ankylosing spondylitis risk factors: A systematic literature review. Clin Rheumatol. 2021;40(8):3079-3093.
  20. Iwaszkiewicz C, Puszczewicz M, Białkowska-Puszczewicz G. Diagnostic value of the anti-Sa antibody compared with the anti-cyclic citrullinated peptide antibody in rheumatoid arthritis. Int J Rheum Dis. 2015;18(1):46-51.
  21. Kao AH, Navratil JS, Ruffing MJ, et al. Erythrocyte C3d and C4d for monitoring disease activity in systemic lupus erythematosus. Arthritis Rheum. 2010;62(3):837-844.
  22. 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.
  23. Kimura Y. Systemic juvenile idiopathic arthritis: Clinical manifestations and diagnosis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2016; July 2019.
  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;74(10):911-918.
  25. Lee YH, Bae SC. Diagnostic accuracy of anti-Sa and anti-RA33 antibodies in rheumatoid arthritis: A meta-analysis. Z Rheumatol. 2017;76(6):535-538.
  26. 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.
  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). 2016;55(2):357-366.
  28. Liang E, Taylor M, McMahon M. Utility of the AVISE connective tissue disease test in predicting lupus diagnosis and progression. Lupus Sci Med. 2020;7(1):e000345.
  29. Lipinska J, Lipinska S, Kasielski M, Smolewska E. Anti-MCV and anti-CCP antibodies-diagnostic and prognostic value in children with juvenile idiopathic arthritis (JIA). Clin Rheumatol. 2016;35(11):2699-2706.
  30. Manzi S, Navratil JS, Ruffing MJ, et al. Measurement of erythrocyte C4d and complement receptor 1 in systemic lupus erythematosus. Arthritis Rheum. 2004;50(11):3596-3604.
  31. 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.
  32. Massenburg D, Oldenberg J, Sell A, et al. Using the SLE-key Rule-Out Test in Clinical Practice. Lupus Open Access. 2017;2(126):1-5.
  33. Mastaglia FL, Phillips BA. Idiopathic inflammatory myopathies: Epidemiology, classification, and diagnostic criteria. Rheum Dis Clin North Am. 2002;28(4):723-741.
  34. Meyer PWA, Ally MTM, Hodkinson B, et al. Comparison of the diagnostic potential of three anti-citrullinated protein antibodies as adjuncts to rheumatoid factor and CCP in a cohort of South African rheumatoid arthritis patients. Rheumatol Int. 2018;38(6):993-1001.
  35. Mossell J, Goldman JA, Barken D, Alexander RV. The Avise Lupus Test and cell-bound complement activation products aid the diagnosis of systemic lupus erythematosus. Open Rheumatol J. 2016;10:71-80.
  36. Navratil JS, Manzi S, Kao AH, et al. Platelet C4d is highly specific for systemic lupus erythematosus. Arthritis Rheum. 2006;54(2):670-674.
  37. Peabody JW, Strand V, Shimkhada R, et al. Impact of rheumatoid arthritis disease activity test on clinical practice. PLoS One. 2013;8(5):e63215. 
  38. 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.
  39. Putterman C, Furie R, Ramsey-Goldman R, et al. Cell-bound complement activation products in systemic lupus erythematosus: Comparison with anti-double-stranded DNA and standard complement measurements. Lupus Sci Med. 2014;1(1):e000056.
  40. Ramsey-Goldman R, Alexander RV, Massarotti EM, et al. Complement activation in patients with probable systemic lupus erythematosus and ability to predict progression to American College of Rheumatology-classified systemic lupus erythematosus. Arthritis Rheumatol. 2020;72(1):78-88.
  41. Ramsey-Goldman R, Li J, Dervieux T, Alexander RV. Cell-bound complement activation products in SLE. Lupus Sci Med. 2017;4(1):e000236.
  42. 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. 2016;75(9):1637-1644.
  43. 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. 2016;36(2):295-300.
  44. 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
  45. 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
  46. 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.
  47. Targoff IN. Laboratory testing in the diagnosis and management of idiopathic inflammatory myopathies. Rheum Dis Clin North Am. 2002;28(4):859-890.
  48. Taylor PC. Biologic markers in the diagnosis and assessment of rheumatoid arthritis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed August 2020.
  49. 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.
  50. 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
  51. Venables PJW, Maini RN. Clinical manifestations of rheumatoid arthritis. UpToDate [serial online]. Waltham, MA: UpToDate; reviewed August 2014b.
  52. Venables PJW, Maini RN. Diagnosis and differential diagnosis of rheumatoid arthritis. UpToDate [serial online]. Waltham, MA: UpToDate; reviewed August 2014a; August 2020.
  53. Wallace DJ, Alexander RV, O'Malley T, et al. Randomised prospective trial to assess the clinical utility of multianalyte assay panel with complement activation products for the diagnosis of SLE. Lupus Sci Med. 2019;6(1):e000349.
  54. Weiss PF. Oligoarticular juvenile idiopathic arthritis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2016b.
  55. Weiss PF. Polyarticular juvenile idiopathic arthritis: Clinical manifestations, diagnosis, and complications. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2016a.
  56. Yang DH, Chang DM, Lai JH, et al. Usefulness of erythrocyte-bound C4d as a biomarker to predict disease activity in patients with systemic lupus erythematosus. Rheumatology (Oxford). 2009;48(9):1083-1087.
  57. Yu DT, van Tubergen A. Diagnosis and differential diagnosis of axial spondyloarthritis (ankylosing spondylitis and nonradiographic axial spondyloarthritis) in adults. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2021a.
  58. Yu DT, van Tubergen A. Reactive arthritis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed July 2021b.
  59. Zeng Z, Zhang W, Qian Y, et al. Association of telomere length with risk of rheumatoid arthritis: A meta-analysis and Mendelian randomization. Rheumatology (Oxford). 2020;59(5):940-947.
  60. Zhang L, Ru J, Jie X, et al. Antibodies to mutated citrullinated vimentin and some associated autoantibodies in juvenile idiopathic arthritis. Zhonghua Er Ke Za Zhi. 2015;53(12):948-951.
  61. 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.
  62. Zhao YL, Wu J, Zhang TP, et al. Circulating insulin-like growth factor-1 levels in patients with rheumatoid arthritis: A meta-analysis. Curr Pharm Des. 2019;25(10):1091.