Table 3.
Results of published literature on the multi-biomarker disease activity score in rheumatoid arthritis
| Study (reference) | Primary Aim | Secondary Aim(s) | Summary of Results Reported |
|---|---|---|---|
| Studies included in both systematic review and meta-analysis | |||
| Bakker, 2012 | Correlation with disease activity measures | Contribution of non-CRP biomarkers, MBDA response to treatment, ability to predict RP | MBDA correlated with DAS28-CRP and discriminated between remission/low and moderate/high DAS28-CRP disease activity categories (AUROC 0.86) Non-CRP biomarkers were independently associated with SJC28, TJC28, and VAS-GH. MBDA decreased with 6 months of treatment (53[18] -> 39[16]), more significantly in intensive treatment arm. MBDA did not predict radiographic progression. |
| Curtis, 2012 | Establish criterion and discriminant validity | Contribution of non-CRP MBDA biomarkers Characterize performance in seropositive vs. seronegative patients | MBDA correlated with DAS28-CRP, CDAI, SDAI, and RAPID3, and discriminated low vs. mod/high DAS28-CRP (AUROC 0.70-0.77 across cohorts studied). ∆MBDA correlated with ∆DAS28-CRP, ACR response criteria and discriminated clinical response (DAS28-CRP AUROC 0.77; ACR50 AUROC 0.69). MBDA better correlated with disease activity measures in seropositive (vs. seronegative) patients; SDAI (r 0.55 vs. 0.29), CDAI (r 0.48 vs. 0.21), RAPID3 (r 0.47 vs. 0.26). Non-CRP MBDA biomarkers predicted DAS28-CRP. |
| Hirata, 2013 | Correlation with disease activity measures | Ability to discriminate EULAR disease activity categories | MBDA significantly correlated with DAS28-ESR, SDAI, CDAI, and HAQ-DI. MBDA correlated with change in DAS28-ESR and SDAI (not CDAI) over one-year follow-up. Remission by MBDA associated with ACR/EULAR (AUROC 0.83), DAS28-ESR, CDAI, and SDAI remission criteria. |
| Hambardzumyan, 2015 | Ability of baseline MBDA to predict radiographic progression (∆SHS>5) | Baseline MBDA higher (p<0.001) in patients with RP. MBDA independent predictor of RP as continuous (OR 1.05, 95% CI 1.02-1.08) or categorical variable (OR 3.86, 95% CI 1.04-14.26 for high vs. low/mod MBDA). |
|
| Hirata, 2015 | Correlation with change in disease activity measures | Comparison between anti-TNF therapies | ∆MBDA correlated with ∆DAS28-ESR and ∆DAS28-CRP, but not ∆CDAI or ∆SDAI. No difference in correlations between anti-TNF therapies. |
| Fleischmann, 2016 | Correlation with disease activity measures | Correlation with radiographic progression | Not associated with DAS28-CRP, CDAI, SDAI, RAPID3, or radiographic progression over 2-year follow up. |
| Reiss, 2016 | Effect of TCZ on correlation of MBDA with disease activity | Effect of TCZ on individual biomarkers in MBDA | Correlation of MBDA with DAS28-CRP decreased (Spearman’s p=0.50 at baseline -> p 0.19-0.33) between weeks 4-24, and agreement between low/mod/high MBDA and DAS28-CRP categories decreased (77.1% -> 23.7%) with 24 weeks of TCZ treatment. Individual analyte changes following TCZ treatment included an increase in IL-6 and a decrease in CRP and serum amyloid A. |
| Krabbe, 2017 | Correlation with imaging measures of inflammation | Correlation with DAS28-CRP | MBDA did not correlate with imaging inflammation at baseline or week 52, and in general did not predict change in imaging inflammation. Correlated modestly with MRI synovitis (r=0.43), MRI bone marrow edema (r=0.36), and ultrasound (US) power Doppler score (r=0.35) at week 26. MRI/US were concordant with MBDA in detecting disease activity for patients in DAS28-CRP remission. MBDA correlated with DAS28-CRP at baseline and week 26. ∆MBDA correlated with ∆DAS28-CRP from baseline to 26 weeks, but not baseline to 52 weeks. |
| Studies included only in systematic review | |||
| Eastman, 2012 | Analytical performance of MBDA multiplex assay. | MBDA biomarker assays were precise, with minimal interference or cross-reactivity | |
| Centola, 2013 | Development of MBDA score | Impact of comorbidities on MBDA | MBDA algorithm developed through biomarker screening, feasibility studies, and assay optimization. Co-morbidities assessed (hypertension, osteoarthritis, osteoporotic bone fractures, degenerative joint disease, diabetes, asthma) were not associated with the MBDA. |
| Li, 2013 | Effect on provider treatment choices | Effect on overall drug use, correlation with PGA | Treatment plans changed in 38% of patients with MBDA. No effect on overall drug use. Modest correlation with PrGA (r=0.35). |
| Peabody, 2013 | Impact on quality scores using Clinical Performance and Value vignettes | Appropriate use of DMARDs, number of labs or imaging tests ordered, use of other resources | Quality scores improved 12% with MBDA. Appropriate use of DMARDS improved with comorbid patients. No effect on number of labs or imaging tests ordered, or use of health care resources. |
| van der Helm-van Mil, 2013 | Frequency of radiographic progression (∆SHS>3) in MBDA remission | Detection of subclinical disease activity | Greater rate of non-progression in MBDA remission vs. non-remission (93% vs 70%). +LR of non-progression in MBDA remission 4.73 (95% CI 1.67-15.0). High MBDA score in DAS28-CRP remission increased risk of radiographic progression (RR 2.28, 95% CI 1.13-3.68). |
| Markusse, 2014 | Ability to predict radiographic progression (∆SHS>5) | MBDA at baseline discriminates radiographic progressors vs. non-progressors better than DAS (AUROC 0.767, 95% CI 0.639–0.896) and predicts RP based on MBDA at baseline (RR 1.039, 95% CI 1.018–1.059) and 1 year (RR 1.037, 95% CI 1.009–1.065) associated with increased RP. | |
| Michaud, 2015 | Outcomes and cost when used in RA management | Decreased HAQ scores (0.09 in 1 year, 0.02 over 10 years), increase quality-adjusted life years 0.08, and decreased overall cost US$457. | |
| van Vollenhoven, 2015 | Impact on recruitment to clinical trials based on data from SWEFOT trial | High MBDA (>44) enhanced recruitment in low CRP (<10) patients – additional 24% MTX-naïve patients and 47% MTX-incomplete responders included. | |
| Hambardzumyan, 2016 | Ability of MBDA at multiple time points to predict radiographic progression (∆SHS>5) | Ability of MBDA to predict RP in Triple Therapy (TT) vs. anti-TNF treated patients | Persistently low/mod MBDA was predictive of less RP. MBDA was numerically (but not statistically) superior to CRP, ESR, and DAS28 for identifying RP. Patients with high MBDA scores on TT had increased risk of RP compared to anti-TNF therapy (45% vs. 25% at baseline and 57% vs. 32% at month 3). |
| Hirata, 2016 | Correlation with radiographic progression | MBDA correlated with ∆SHS (r=0.47 at week 24; AUROC 0.44 over 52 weeks). High MBDA increased risk of ∆SHS >3 (RR 14.3[2.5-85.5]) at week 24 compared to low MBDA. In patients with low or mod/high DAS28, MBDA further discriminated risk of radiographic progression. | |
| Lee, 2016 | Correlation with disease activity measures | Utility in RA patients with fibromyalgia (FM). | MBDA correlated with CRP in RA patients with (r=0.89) or without (r=0.73) concomitant FM. Composite indices (DAS28-CRP, SDAI, CDAI, RAPID3) all greater in patients with concomitant FM, though no difference in MBDA between these groups. |
| Li, 2016 | Correlation with radiographic progression | High MBDA increased risk of ∆SHS>3 (RR 3.4 if MBDA 45-51; 4.3 if MBDA 52-59; 5.2 if MBDA ≥60) and ∆SHS>5 (RR 12.4, 12.0, and 17.4) compared with low MBDA. MBDA independent risk factor for radiographic progression after adjustment for SJC28, DAS28-CRP, CRP, and pre-existing joint damage. |
|
| Rech, 2016 | Ability to predict disease relapse in patients tapering DMARDs | Baseline MBDA scores significantly (p=0.0001) higher in patients with subsequent relapse. MBDA and Anti-CCP independent predictors of disease relapse. Able to predict >80% of relapses using anti-CCP plus MBDA. |
|
| Hambardzumyan, 2017 | Predicting response to Triple therapy (TT) vs. anti-TNF | More patients with low MBDA responded to TT vs. anti-TNF (88% vs 18%); more patients with high MBDA responded to anti-TNF (35% vs 58%). | |
Abbreviations: ACR50, American College of Rheumatology response criteria - 50% improvement; AUROC, area under the receiver operating characteristic curve; CDAI, clinical disease activity index; HAQ-DI, health assessment questionnaire without disability index; IL-6, interleukin-6; MTX, methotrexate; PrGA, provider global assessment of disease activity; RAPID3, routine assessment of patient index data 3; RF, rheumatoid factor; RP, radiographic progression; RR, relative risk; SDAI, simple disease activity index; SHS, sharp score as modified by van der Heijde; SJC28, swollen 28-joint count; TJC28, tender 28-joint count; TCZ, tocilizumab; VAS-GH, visual analogue scale of patients’ assessment of general health; +LR, positive likelihood ratio.