Coverage Policy Manual
Policy #: 2011060
Category: Laboratory
Initiated: August 2011
Last Review: April 2018
  Biomarker Test (Vectra™ DA) for Monitoring Disease Activity in Rheumatoid Arthritis

Description:
Assessment of disease activity in rheumatoid arthritis (RA) is an important component of treatment management, as one of the main goals of treatment is to maintain low disease activity or remission. There are a variety of available instruments for measuring RA disease activity. One potential approach is the use of a multibiomarker disease activity (MBDA) score. The Vectra DA test is a commercially available MBDA blood test that uses 12 biomarkers to construct a disease activity score ranging from 0 to 100.
 
Background
 
RA is a disorder characterized by chronic joint inflammation leading to painful symptoms, progressive joint destruction and loss of function. The disorder is relatively common and is associated a high burden of morbidity for affected patients.
 
Treatment of RA has undergone a shift from symptom management to a more proactive strategy of reducing disease activity and delaying disease progression (Upchurch, 2012). The goal of treatment is to reduce irreversible joint damage that occurs from ongoing joint inflammation and synovitis by keeping disease activity as low as possible. The availability of an increasing number of effective disease modifying antirheumatic drugs has made achievement of remission, or sustained low disease activity, a feasible goal in a large proportion of patients with RA. This treatment strategy has been called a “tight control” approach.
 
The concept of “tight control” in the management of RA has gained wide acceptance as evidence from clinical trials have demonstrated that outcomes are improved with a tight control strategy. In a tight control strategy, treatment targets are used that are mainly based on measures of disease activity. In a systematic review published in 2010, Schoels et al identified 7 trials that evaluated the efficacy of tight control (Schoels, 2010). Four of these trials randomized patients to either a tight control using treatment targets or routine management. The treatment targets used were heterogeneous, including symptom-based measures, joint scores on exam, validated treatment activity measures, lab values, or combinations of these factors. In all 4 trials, there was a significant decrease in the Disease Activity Score (DAS) and in the likelihood of achieving remission for patients in the tight control group.
 
For a strategy of tight control to be successful, a reliable and valid measurement of disease activity is important. There are numerous disease activity measurements that can be used in clinical care. Composite measures include information from multiple sources, including patient self-report, physician examination and/or biomarker measurement. Composite measures are the most comprehensive but have the disadvantage of being more cumbersome and difficult to complete. Patient reported measures are intended to be simpler, and rely only on information that patients can provide expeditiously, but have the disadvantage of being more subjective. Measurements that rely only on biomarkers are objective and do not require patient input but do involve the cost and inconvenience of laboratory tests.
 
The most widely used and validated in clinical research is the DAS28 score. This is a composite measure that includes examination of 28 joints for swelling and tenderness, combined with a patient report of disease activity and measurement of C-reactive protein (CRP) (or erythrocyte sedimentation rate). This score has been widely validated and used for both research and clinical care and is often considered the criterion standard for measuring disease activity. However, it requires a thorough joint examination, information obtained from the patient, and laboratory testing. Therefore, there have been many attempts to create a valid disease activity measure that is simpler. Some measures include only patient self-report and thus can be completed quickly in the setting of an office visit. An example of this type of measure is the simplified disease activity index (SDAI). Another approach is to use only serum biomarkers, which only requires a blood draw. The Vectra DA is this type of biomarker-based measure. Proponents of a biomarker approach have argued that this is simpler and avoids the subjectivity of physical examination and patient report.
 
There is a fairly large body of evidence comparing the performance of different disease activity measures in clinical care, including a number of systematic reviews. In a systematic review of disease activity measures sponsored by the American College of Rheumatology in 2012, more than 60 measurement instruments were identified (Andeson, 2012). Through a 5-stage process that included review by an expert advisory panel in RA disease activity and detailed evaluation of psychometric properties, the workgroup selected 6 that were most useful and feasible for point-of-care clinical care. These were the Clinical Disease Activity Index (CDAI), Disease Activity Score with 28 joints (DAS28), Patient Activity Scale (PAS), Patient Activity Scale II (PAS-II), Routine Assessment of Patient Index data with 3 measures (RAPI), and the Simplified Disease Activity Index (SDAI).
 
In another systematic review, Gaujoux-Viala et al compared 4 composite indices, DAS, DAS28, SDAI, and CDAI (Gaujoux, 2012). In general, the concordance between measures was good, with kappa values in the range of 0.7. An exception to this level of concordance was in the definition of remission, for which the DAS28 had lower levels of concordance with other measures, with kappa values ranging from 0.48 to 0.63. All of the measures had fair-to-good correlations with an independent health status measure, the Health Assessment Questionnaire (HAQ) and with radiologic examination of joint structural damage.
 
Salaffi et al compared the responsiveness of numerous disease activity measures, including patient self-report measures and composite indices, over a 6-month period of treatment with disease modifying drugs (Salaffi, 2012). The composite indices evaluated were DAS28, SDAI, CDAI, and the Mean Overall Index for RA. The patient-reported measures evaluated were the Clinical Arthritis index, the Rheumatoid Disease Activity Index, the Routine Assessment of Patient Index Data (RAPID3), and PAS. Across all measures, there was wide variability in internal responsiveness, with the highest value obtained for the DAS28 measure. There were some differences in responsiveness between the measures, but all were considered suitable for use in clinical care. When comparing the patient-reported measures with the composite measures, there was no difference in internal or external responsiveness.
 
Vectra DA test
The Vectra DA test (Crescendo Bioscience, South San Francisco, CA) consists of 12 individual biomarkers. These are (Curtis, 2012):
  • Interleukin-6 (IL-6)
  • Tumor necrosis factor receptor type I (TNFRI)
  • Vascular cell adhesion molecule 1 (VCAM-1)
  • Epidermal growth factor (EGF)
  • Vascular endothelial growth factor A (VEGF-A)
  • YKL-40
  • Matrix metalloproteinase 1 (MMP-1)
  • Matrix metalloproteinase 3 (MMP-3)
  • CRP
  • Serum amyloid A (SAA)
  • Leptin
  • Resistin
 
There are no U.S. Food and Drug Administration (FDA)-approved MBDA tests for measuring disease activity in RA. Commercially available tests are laboratory-developed tests that are not subject to FDA approval. Clinical laboratories may develop and validate tests in-house (“home-brew”) and market them as a laboratory service; such tests must meet the general regulatory standards of the Clinical Laboratory Improvement Act.
 
Coding
 
Effective 3/2015, CPT published a specific CPT code for this service:
 
81490 Autoimmune (rheumatoid arthritis), analysis of 12 biomarkers using immunoassays, utilizing serum, prognostic algorithm reported as a disease activity score. CPT 81490 should not be reported in conjunction with 86140.
 
Prior to 3/2015, there was no specific CPT code for this test and it may have been submitted using 11 units of code 83520 (Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; quantitative, not otherwise specified) and 1 unit of 86140 (C-reactive protein).
 

Policy/
Coverage:
The use of biomarker testing using the Vectra™ DA test to monitor disease activity in patients diagnosed with rheumatoid arthritis does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness in improving health outcomes.
 
For contracts without primary coverage criteria, the use of biomarker testing using the Vectra™ DA test to monitor disease activity in patients diagnosed with rheumatoid arthritis is considered investigational.  Investigational services are specific contract exclusions in most member benefit certificates of coverage.
 

Rationale:
Preliminary evidence on the utility of this test has begun to appear in the form of meeting abstracts and presentations. No full-length articles in the peer-reviewed literature have yet been published.  An abstract presented at the American College of Rheumatology annual meeting in November 2010 (Curtis, 2010) reported on the accuracy of this test compared to the DAS28CRP.  The Vectra™ DA test was found to have a moderate correlation with scores on the DAS28CRP (r=0.56, 95% confidence interval [CI] 0.46-0.64, p<0.001).  The area under the curve for Vectra DA, when using a DAS28CRP upper threshold of 2.67 to define low activity, was 0.77 (95% CI 0.70-0.83, p<0.001).  In other abstracts (Fleischman, 2010) (Cavet, 2010), the Vectra score was shown to be correlated with clinical response to treatment, to predict future joint damage, and to differentiate between patients with remission and low disease activity.
 
This preliminary evidence establishes that the Vectra™ DA test has validity in measuring disease activity.  However, the clinical utility of this test depends on whether actionable, clinically meaningful, thresholds can be identified for this test. In order to determine that this test has a role in the clinical management of rheumatoid arthritis, it needs to be shown that changes in management that result from the test subsequently lead to improved health outcomes.
 
2014 Update
A literature search conducted through March 2014 did not reveal any new information that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
Multibiomarker disease activity (MBDA) tests for disease activity in rheumatoid arthritis (RA) are best evaluated in the framework of a prognostic test, as they provide prognostic information that assists in treatment decisions. Assessment of a prognostic tool typically focuses on 3 categories of evidence: 1) technical performance; 2) clinical validity (ie, statistically significant association between the test result and health outcomes); and 3) clinical utility (ie, demonstration that use of the prognostic information clinically can alter clinical management and/or improve health outcomes compared with patient management without use of the prognostic tool). In some cases, it is important to evaluate whether the test provides incremental information above the standard workup to determine whether the test has utility in clinical practice.
 
Technical Performance
Eastman et al described aspects of the technical performance of the MBDA Vectra test in 2012 (Eastman, 2012). The 12 individual biomarkers in the Vectra test were measured using multiplexed sandwiched immunoassays with biomarker-specific capture antibodies. The total MBDA score had good reproducibility over time, with a coefficient of variation of less than 2%. Crossreactivity by serum rheumatoid factor, other RA antibodies, and/or common RA therapies, was minimal.
 
Centola et al published a study on the development of the Vectra DA test in 2013 (Centola, 2013). This publication described a multistage process for development and validation of the score. In the first phase, the screening phase, proteins were identified that could be readily measured and had the potential to be associated with RA disease activity. A comprehensive total of 130 candidate biomarkers were selected. In the second phase, 4 separate patient cohorts were utilized to refine the biomarkers by their correlations with multiple measures of disease activity. In the final phase, assay optimization and training, the biomarkers with the greatest predictive ability were optimized for multiplex assay. In addition, the combined cohorts of patients were used for algorithm training using a number of statistical techniques. The final model included 12 individual biomarkers and an algorithm that generated a score between 0 and 100.
 
Clinical Validity
Curtis et al used blood samples from 3 cohorts of arthritis patients (Index for Rheumatoid Arthritis Measurement, Brigham and Women’s Hospital Rheumatoid Arthritis Sequential Study, Leiden EarlyArthritis Clinic) to validate the Vectra DA MBDA against the Disease Activity Score with 28 joints (DAS28)-CRP (C-reactive protein) and other known markers of disease activity (Curtis, 2012). There was a positive correlation of the Vectra Score with the DAS28-CRP score, with a Pearson product-moment correlation coefficient ( r) of 0.56 in seropositive RA patients and 0.43 in seronegative patients. The area under the curve (AUC) for discriminating low disease activity from moderate to high disease activity was 0.77 in seropositive patients and 0.70 in seronegative patients, using the DAS28-CRP as the criterion standard. The Vectra score was also correlated with other measures of disease activity, including the Simplified Disease Activity Index (SDAI), the Clinical Disease Activity Index (CDAI), and the Routine Assessment of Patient Index Data (RAPID3), with r values ranging from 0.47 to 0.55 for seropositive patients and 0.21 to 0.29 for seronegative patients.
 
Hirata et al studied the correlation of the Vectra DA score with other validated measures of disease activity in 125 patients from the Behandel Strategieen study. Blood samples were available from 179 visits, 91 baseline visits and 88 visits at 1-year follow-up. Validated disease activity measures were DAS28, SDAI, CDAI, and the HAQ disability index. The Vectra DA scores were significantly correlated with the DAS28 measure (Spearman correlation coefficient (p) = 0.66, p<0.001), as were the changes in scores between baseline and 1 year (Spearman correlation (p) = 0.55, p<0.001). The Vectra scores were also significantly correlated with the SDAI, CDAI, and HAQ disability index at the p<0.001 level.
 
Bakker et al examined the correlation of the MBDA score (Vectra DA score) with the DAS28 score and response to therapy, in a subset of patients from the CAMERA trial. (9) In the larger CAMERA trial, 299 patients were randomized to standard or intensive management of RA. For the Bakker substudy, 74 of 299 patients (24.7%) had blood drawn for measurement of the 20 biomarkers, including the 12 comprising the MBDA test. There were 72 samples collected at baseline and 48 samples collected at 6 months. The total test score was a number between 0 and 100, calculated through use of a proprietary algorithm.
 
The MBDA score was significantly correlated with the DAS28 score at baseline (Pearson ® = 0.72, p<0.001). When using the DAS28-CRP cutoff of 2.7 as the criterion standard, the MBDA score discriminated between remission/low disease activity and moderate/high disease activity with an AUC of 0.86,. The kappa score for agreement with the DAS28-CRP for classifying disease activity was 0.34 (95% confidence interval [CI], 0.19 to 0.49). The MBDA score decreased following therapy, from a baseline of 53 (standard deviation, SD18) to 39 (SD16) at 6 months
 
Overall, evidence for the clinical validity of the Vectra DA test consists of studies that correlate the score with other measures of disease activity, including the DAS28 score. These studies show a positive correlation that is in the moderate range, with reported r values ranging from 0.5 to 0.7. One study reported a kappa value of 0.34 for the DAS28 and Vectra DA, indicating a moderate level of agreement above chance. There is also some evidence that the Vectra DA score correlates with response to treatment. For discriminating levels of disease activity, 2 studies that used the DAS28 as the criterion standard reported an AUC in the moderate to high range, with values ranging from 07 to 0.86 for different populations.
 
Clinical Utility
To demonstrate clinical utility, there should be evidence that the MBDA score is at least as good a measure of disease activity as other available measures. This could be demonstrated directly by an randomized controlled trial (RCT) that compared a management strategy using Vectra DA score with an alternate management strategy using another measure of disease activity, and that reported clinical outcomes such as symptoms, functional status, quality of life, or disease progression on radiologic imaging. Indirect measures of clinical utility could be obtained from high-quality evidence that clinical validity of the MBDA is equivalent to other measures used in clinical care, together with guidance on the optimal use of the score in decision making, ie, evidence linking management changes to specific results on the MBDA score.
 
One RCT was identified that tested the impact of the Vectra DA score on simulated decision making by experienced rheumatologists. (10) A total of 81 rheumatologists without previous experience with the Vectra DA test were randomized to decision making with and without the Vectra DA score, using 3validated clinical vignettes representing typical clinical care in RA. A quality score for each vignette was calculated using predefined criteria. Quality scores in the group receiving the Vectra DA score improved by 3% compared with the control group (p=0.02). The largest benefits in the Vectra DA group were improvements in the quality of disease activity and treatment decisions of 12% (p<0.01), and more appropriate use of biologics and disease modifying drugs (p<0.01).
In a study using physician surveys, Li et al examined the impact of a MBDA score on treatment decisions for patients with RA. (11) This study examined the treatment decisions made by 6 health care providers, all who had shown previous interest in using the MBDA score. A total of 108 patients were enrolled who were at least 18 years-old, had a diagnosis of RA, completed a MBDA test, and had a survey completed by a physician. Surveys of treatment decisions were done before and after the results of the MBDA score was provided. After receiving the MBDA score, treatment plans were changed in 38/101 cases (38%, 95% CI, 29% to 48%). Changes in treatment decisions were a change in the type of drug in 21/38 cases, and a change in the dose or route of administration of a drug in 17/38 cases. There was no data collected on outcomes associated with the different treatment decisions.
 
Overall, there is some evidence that treatment decisions can be influenced by the Vectra DA score. This evidence comes from simulated cases and/or surveys of physician behavior. There are no RCTs that compare use of the Vectra DA score to an alternative method of measuring disease activity, and as a result there is no direct evidence that Vectra DA improves outcomes. Other disease activity measures have been associated with improvements in health outcomes in clinical trials. Thus, the evidence from RCTs on other measures, together with the correlation of Vectra DA with these measures is indirect evidence that outcomes may be improved with use of the Vectra DA test. However, there is insufficient evidence to determine whether Vectra DA is as good as other more established disease activity measures in improving outcomes.
 
Clinical Input Received through Physician Specialty Societies and Academic Medical Centers
None
 
In conclusion the Vectra DA is a biomarker-based measurement of disease activity in rheumatoid arthritis (RA) that uses results of 12 serum biomarkers to construct a score ranging from 0 to 100. It is one of numerous disease activity measures that are available for use in clinical care, and there are other disease activity scores (eg, Disease Activity Score with 28 joints, DAS28) that have been more extensively validated. Evidence of validity for the measure consists of several studies that correlate Vectra DA with other previously validated measures such as the DAS28. These studies show moderate correlations of Vectra with the DAS28. A small number of studies evaluate clinical utility by examining changes in decision making associated with use of Vectra, but these are limited by the design of using simulated cases or physician surveys and do not report any outcome data. This limited body of evidence on the Vectra DA test is not sufficient to determine whether it is as good as or better than other disease activity measures, and it is possible that it is not as accurate as the DAS28. As a result, the Vectra DA test is considered investigational for use as a measure of disease activity in the patients with RA.
 
2015 Update
A literature search conducted through February 2015 did not reveal any new information that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
Clinical Validity
Evidence on clinical validity consists primarily of studies that correlate the Vectra DA score with other disease activity measures, markers of disease progression, and/or response to therapy. These are either observational cohort studies, or post-hoc analyses of RCTs that were performed for a different purpose and in which serum samples were available to retrospectively evaluate the Vectra DA test.
 
Post-hoc analyses of completed RCTs
Two publications report on the evaluation of the Vectra DA score from the BeST trial, which was a multicenter RCT of 508 patients with early RA, randomized to 4 different treatment strategies. For both of these studies, a subset of patients who had serum samples available were included, Of the 508 patients, there were 125 patients with serum samples, 91 baseline samples, 89 from 1-year follow-up, and 55 patients who had both baseline and follow-up serum available. Comparison of patients who had samples available and those who did not revealed that the population with serum available differed from those who did not on gender (75% vs 65% female, p=0.04), the median number of tender joints (11 vs 14, p<0.001), and the median number of erosions seen on imaging (1.0 vs 2.0, p=0.005).
 
In the first study, Hirata and colleagues studied the correlation of the Vectra DA score with other validated measures of disease activity. Validated disease activity measures were DAS28, SDAI, CDAI, and the HAQ Disability Index (DI). The Vectra DA scores were significantly correlated with the DAS28 measure (Spearman correlation coefficient ρ=0.66, p<0.001), as where the changes in scores between baseline and 1 year (Spearman ρ=0.55, p<0.001). The Vectra scores were also significantly correlated with the SDAI, CDAI, and HAQ-DI at the p<0.001 level. The second study by Marcuse and colleagues, evaluated how well the Vectra DA score predicted the progression of radiographic joint damage, and compared the predictive ability of Vectra DA with the DAS28 score (Markusse, 2014). Radiographic progression was defined as a change of at least 5 points on the Sharp van der Heijde Score over a one year period. ROC analysis was performed, with an AUC for the Vectra DA test of 0.77 (95% CI 0.64-0.90), which was higher than the AUC for the DAS28 (0.52, 95% CI 0.39-0.66).
 
Hambardsumyan and colleagues performed a post-hoc analysis from the Swedish Farmocotherapy (SWEFOT) trial, which was a RCT that randomized 487 patients to two different treatment regimens (Hambardzumyan, 2014). There were a total of 235 patients (48% of total) who had serum samples available and complete clinical and radiographic data. The authors evaluated the Vectra DA score as a predictor of radiographic progression, defined as a change of at least 5 points on the Sharp van der Heijde Score. The Vectra DA score was a univariate predictor of radiographic progression (odds ratio 1.05 per unit increase, 95% CI 1.02 to 1.08, p<0.001), and was an independent predictor of progression in a variety of multivariate models. For patients with a low or moderate Vectra DA score (<44), radiographic progression was uncommon, occurring in 1/40 (2/5%) patients.
 
Hirata and colleagues reported in 2014 on the correlation between the Vectra DA score and response to treatment in 147 patients treated with anti-TNF medications for at least a year (Hirata, 2014). The relationship between baseline scores and response to treatment was measured for the Vectra DA test and for a number of other disease activity scores (DAS28, SDAI, CDAI). A good response, as defined by the European League Against Rheumatism clinical criteria, was achieved by 56% of patients. The mean Vectra DA score decreased from 64 to 34 over the course of the study, and 37% of patients met the threshold for low activity (Vectra Score <30). The Vectra DA score decreased more in patients with a good clinical response (-29 points) compared to those with a moderate response (-21 points, p<0.001), and decreased more in patients with a moderate response compared to non-responders (+2 points, p<0.007). There was a positive correlation of the Vectra DA score with the DAS28- CRP (r=0.46) and the DAS28-ESR (r=0.48), but not with the SDAI or the CDAI.
 
Another study compared the discriminatory ability of Vectra DA versus the DAS28 using radiographic disease progression as the reference standard, and reported that the AUC was higher for Vectra DA compared to DAS28.
 
Ongoing and Unpublished Clinical Trials
A search of ClinicalTrials.gov  March 2015 did not identify any ongoing or unpublished trials that would likely influence this policy.
 
Evidence of validity for the Vectra DA measure consists of numerous studies that correlate Vectra DA with disease progression, response to therapy, and/or other previously validated disease activity measures such as the DAS28. These studies establish that the Vectra DA score is a predictor of disease progression and that decreases in the score are correlated with disease response. They also show moderate correlations of Vectra with the DAS28 score. A smaller number of studies evaluate clinical utility by examining changes in decision making associated with use of Vectra, but these are limited by the design of using simulated cases or physician surveys and do not report any outcome data. This limited body of evidence on the Vectra DA test is not sufficient to determine whether it is as good as or better than other disease activity measures, and it is possible that it is not as accurate as the DAS28. As a result, the Vectra DA test is considered investigational for use as a measure of disease activity in the patients with RA.
 
2016 Update
A literature search conducted through March 2016 did not reveal any new information that would prompt a change in the coverage statement.  There were no new published clinical trials since the last policy update. No ongoing clinical trials were identified.
 
2017 Update
A literature search conducted through March 2017 did not reveal any new information that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
SWEFOT trial. Hambardzumyan et al performed a post hoc analysis from the Swedish Farmacotherapy (SWEFOT) trial, which was an RCT that randomized 487 patients to 2 different treatment regimens (Hambardzumyan, 2015). A total of 235 (48%) patients had serum samples available and complete clinical and radiographic data. The authors evaluated the Vectra DA score as a predictor of radiographic progression, defined as a change of at least 5 points on the Sharp van der Heijde Score. The Vectra DA score was a univariate predictor of radiographic progression (odds ratio, 1.05 per unit increase; 95% CI, 1.02 to 1.08; p<0.001), and was an independent predictor of progression in a variety of multivariate models. For patients with a low or moderate Vectra DA score (<44), radiographic progression was uncommon, occurring in 1 (2.5%) in 40 patients.
 
A second publication from the SWEFOT reported repeat scores at multiple time points (Hambardzumyan, 2016), Of 487 patients enrolled in the SWEFOT trial, 220 had baseline Vectra DA scores (45.2%), 205 had scores at 3 months (42.1%), and 133 had scores at 1 year (27.3%). Patients with low initial scores, or with a decrease in scores over time into the low range, had the lowest rate of radiographic progression at 1 year. Cross tabulation of Vectra DA results with the DAS28, ESR, and CRP values was presented, but no statistics that addressing the comparative accuracy of the different measures were reported.
 
AMPLE trial. The AMPLE trial randomized patients with active rheumatoid arthritis and an inadequate response to methotrexate to abatacept or adalimumab and followed patients for 2 years (Fleischmann, 2016). Eligibility criteria included a DAS28-CRP score of at least 3.2 and a positive test for antibodies to either CCP or RF. Vectra DA scores were analyzed form stored serum samples at baseline, 3 months, 1 year and 2 years, and correlated with other measures of disease activity (DAS28-CRP and CDAI). There were a total of 646 patients enrolled and 524 (81%) had results for Vectra DA. The concordance of disease activity states was examined between the different measures. There was not a high concordance of classification into high, moderate and low disease categories, but there were no quantitative measures of association reported. The VECTRA DA score was not a significant predictor of radiographic progression, while the CDAI score was a significant predictor.
 
RETRO trial. The RETRO trial enrolled patients treated with DMARDs in clinical remission, and randomized participants to tapering DMARD or standard maintenance care (Rech, 2015).  Eligibility criteria included a DAS28-ESR score lower than 2.6 for at least 6 months and follow-up was for 12 months. Of 101 patients enrolled in RETRO, Vectra DA data was available for 94 (93%). The Vectra DA score was higher in patients experiencing a relapse (32.0±2.3) compared with patients who did not experience a relapse (22.6±1.2, p=0.0001). On multivariate analysis, the Vectra DA score was a significant predictor of relapse (odds ratio 8.54, 95% CI 2.0-36.4), along with treatment arm (odds ratio 5.94, 95% CI 1.3-26.7) and anti-CCP status (odds ratio 24.5, 95% CI 3.1-194.0).
 
A publication from the Leiden Early Arthritis Clinic Cohort was published in 2016 (Li, 2016). This study used the Vectra DA score and other measures of disease activity to predict radiologic progression of disease at 1 year. There were 163 patients in this cohort that had complete information on Vectra DA and other disease activity measures. The proportion of patients with radiographic progression increased as Vectra DA scores increased. For patients with a score of less than 29, 2% met criteria for radiographic progression, and for patients with a score of 60 or greater, 41% met criteria for radiographic progression. Vectra DA scores and other measures of disease activity (DAS28-CRP, swollen joint count, CRP) were predictors of radiographic progression on univariate analysis. On multivariate analysis, only the Vectra DA score was a significant predictor of progression at 1 year (p=0.005).
 
Ongoing and Unpublished Clinical Trials
A search of ClinicalTrials.gov on April 13, 2017 did not identify any ongoing or unpublished trials that would likely influence this review.
 
2018 Update
Annual policy review completed with a literature search using the MEDLINE database through March 2018. No new literature was identified that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
The post hoc analysis by Fleischmann et al was accompanied by an editorial by Davis (Davis, 2016). Davis summarized the evidence for the validity of the MBDA test:
    • The test measures biologic pathways and therefore provides unique information that complements clinical assessments (face validity).
    • Relevant biomarker components were chosen for the test (content validity).
    • Correlation with other measures of disease is inconsistent, ranging from discordant to strong, because there is a lack of a criterion standard in measuring RA disease activity (criterion validity).
    • Sensitivity to change following different RA treatments was inconsistent with other disease activity measures (discriminant validity).
    • High MBDA scores were predictive of radiographic progression, despite clinical measures showing no disease progression (construct validity).
 
Davis concluded that the clinical value of MBDA remains unclear. He pointed out that the manufacturer does not propose that MBDA replace current tests, but rather the test should be used as a complement to clinical evaluations.
 
In 2017, Curtis et al published a response to the Fleishmann study. The authors explained that one of the reasons for discordance between MBDA and the other RA disease activity measures in the Fleischmann study was the use of incorrect cutoff points defining low, moderate, and high disease activity
(DAS28-ESR cutoff points were used to compare MBDA and DAS28-CRP measures) (Curtis, 2017). Also, Curtis et al proposed looking at the relation between radiographic outcomes and MBDA by evaluating radiographic progressors rather than nonprogressors, which is how Fleishmann conducted the  analysis. In a rebuttal, Fleischmann et al  justified their use of nonprogressors on 2 bases: (1) nonprogressors are important patient-level assessments of therapeutic response; and (2) nonprogressors were much more common in the AMPLE database (after 1-year follow-up, there were 327 nonprogressors and 40 progressors) (Fleischmann, 2017).
 
ACT-RAY Trial of Patients With Active RA
Reiss et al conducted a post hoc analysis on patients from the ACT-RAY trial in which patients who did not respond to methotrexate therapy were randomized to add-on tocilizumab therapy or placebo (Reiss, 2016). Patients were included in the analysis if they had DAS28-CRP and CDAI scores at baseline and 24-week follow-up and sufficient serum for MBDA testing at the same time points. Disease activity level (low, moderate, high) agreement between the DAS28-CRP and MBDA at baseline was 77%; however, the agreement between the 2 measures at 24 weeks of follow-up was 24%. Agreement between the MBDA and CDAI followed a similar pattern: 72% agreement at baseline and 22% agreement after 24 weeks of tocilizumab therapy. DAS28-CRP and CDAI had high levels of agreement, both at
baseline and 24 weeks (87% and 85%, respectively).
 
 
Hambardzumyan et al analyzed a subset of data from the SWEFOT trial to investigate the use of MBDA as a predictor of optimal treatment in patients with early RA who did not respond to methotrexate Therapy (Hambardzumyan, 2017). Patients (N=157) in the SWEFOT trial were randomized to 2 groups: triple therapy (methotrexate, sulfasalazine, plus hydroxychloroquine) or to double therapy methotrexate plus infliximab). MBDA categories were defined as low disease activity (<30), moderate disease activity (30-44), and high disease activity (>44). Responders after 1 year of follow-up were defined as patients with DAS28 score of 3.2 or less. The investigators compared MBDA scores at 3 months with DAS28-ESR scores at 1 year to determine whether MBDA scores at 3 months could accurately predict patient response to therapy at the 1-year follow-up. Among patients with low MBDA scores at 3 months, 88% (7/8) subsequently had a clinical response to triple therapy, and 18% (2/11) had a clinical response to methotrexate plus infliximab at the 1-year follow-up. Among patients with high MBDA scores at 3 months, 35% (15 of 43) subsequently responded to triple therapy, and 58% (26/46) responded to methotrexate plus infliximab. The 3-month low and high MBDA scores were better predictors of clinical response to therapy than clinical and inflammatory markers. The authors concluded that 3-month MBDA scores have the potential to inform decisions on which type of therapy to recommend to patients who do not respond to initial methotrexate therapy.
 
 

CPT/HCPCS:
81479Unlisted molecular pathology procedure
81490Autoimmune (rheumatoid arthritis), analysis of 12 biomarkers using immunoassays, utilizing serum, prognostic algorithm reported as a disease activity score
83520Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; quantitative, not otherwise specified
86140C-reactive protein;

References: “Studies show Crescendo Biosciences Vectra™ DA can track early response to rheumatoid arthritis therapy and predict joint damage at the molecular level - Data presented at EULAR Annual European Congress of Rheumatology.” Company press release available online at http://www.crescendobio.com/crescendo/clinical/pdf/05-24-11-Crescendo-Bioscience-EULAR-PR.pdf

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