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2018-2931

Research Proposal

Project Title: 
Influence of demographic and environmental factors on anti-TNF efficacy in rheumatoid arthritis: a systematic review and meta-analysis of RCT
Scientific Abstract: 

Background : Rheumatoid arthritis is a relatively frequent immune mediated disease with a prevalence of 3 to 8/1,000 patients. RA alters quality of life and increases cardiovascular, infectious and other morbidity risks. Anti-TNF drugs are efficient, yet primary or secondary failure is still a problem for one patient out of 3, even if exposition to anti-TNF drugs is correct. Therefore, searching for determinants of treatment response is essential. 

Objective : To study the influence of demographics and disease-related factors on anti-TNF drugs’ efficacy in randomized controlled trials (RCT) in rheumatoid arthritis.
Study design : Systematic review and meta-analysis of published RCT following the PRISMA recommendations.
Participants : Adults (≥18 years of age) with RA according to ACR 1987 or ACR/EULAR 2010 criteria.
Outcomes: primary: ACR20, secondary: ACR50, ACR70, DAS28-CRP, DAS28-ESR, CDAI, SDAI.
Statistical analysis: A meta-analysis of aggregate data will be performed. A fixed effect model will be performed first, with addition of a random effect model in case of significant heterogeneity.
Prospero registration number: CRD42018071079.

Brief Project Background and Statement of Project Significance: 

Rheumatoid arthritis (RA) is a relatively frequent immune mediated disease with a prevalence of 3 to 8/1,000 patients. RA alters quality of life and increases cardiovascular, infectious and other morbidity risks. Anti-TNF drugs are efficient, yet primary or secondary failure is still a problem for one patient out of 3, even if exposition to anti-TNF drugs is correct. Therefore, searching for determinants of treatment response is essential. 
Description of the treatment effect modifiers considered in this review and how these factors could influence the response to anti-TNF drugs
We considered the following demographic and environmental factors that could modify the response to anti-TNF drugs:
- Age and gender. Elderly patients with RA have an increased risk of serious infection. Female gender was shown to be independently associated with a lower rate of remission and a lower response rate to anti-TNF drugs.
- Disease duration. A long disease duration was associated with a poor response rate to anti-TNF.
- Disease activity and/or severity markers such as disease activity score on 28 joints status (DAS28), CRP level, rheumatoid factor (RF) and anti-citrullinated protein antibody (ACPA). In a prospective register, ACPA positivity was negatively related to clinical response and ACPA and RF-positivity was predictive of poor response. High disease activity at baseline was directly associated with favorable response as measured by ACR50 and ACR70 but GISEA study confirmed that high disease activity at baseline was associated with lower response.
- Smoking status. Current cigarette smoking was shown to be associated with a lower response rate to infliximab, and cigarette smoking is a well-recognized risk factor for the development of RA and has also been associated with a more severe disease, disability and extra-articular manifestations, particularly nodules
- Weight or body mass index (BMI). Baseline BMI was shown to be positively correlated with DAS28, indicating a more-active disease in overweight patients. Further, a higher BMI was shown to be associated with a decreased clinical response to infliximab.
- Physical activity. Findings indicate that RA patients who participate in appropriate exercises programs may lessen fatigue levels and experience other positive effects without worsening their condition but we did not found studies that showed that physical activity could influence the response to anti-TNF drugs.
Why it is important to do this review
The treat-to-target strategy in RA has been proposed to increase the therapeutic efficacy while minimizing the risk of adverse events. Therefore, it is important to assess if demographics and environmental factors listed above could influence anti-TNF treatment effect and the direction of this influence. These factors could therefore be considered when initiating anti-TNF in RA in order to increase response rate and anticipate and avoid failure. That is why we are studying the treatment effect in subgroups of interest (eg treatment effect measured by age or gender.

Specific Aims of the Project: 

To study the influence of demographics and disease-related factors on anti-TNF drugs’ efficacy in randomized controlled trials (RCT) in rheumatoid arthritis.

What is the purpose of the analysis being proposed? Please select all that apply.: 
Confirm or validate previously conducted research on treatment effectiveness
Summary-level data meta-analysis
Summary-level data meta-analysis pooling data from YODA Project with other additional data sources
Participant-level data meta-analysis pooling data from YODA Project with other additional data sources
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: 

In a first step, we searched CENTRAL, PubMed and EMBASE and selected eligible studies.
- Inclusion criteria: randomized controlled trials comparing an anti-TNF drug (infliximab, adalimumab, golimumab, certolizumab pegol or etanercept) versus placebo or conventional DMARDs, in rheumatoid arthritis (RA) patients and reported efficacy data by subgroups of demographic and disease related factors of interest.
The following factors of interest will be considered: age, sex, BMI, smoking status, disease duration, DAS28, CRP, ACPA, RF, and physical activity.
- Exclusion criteria: non-randomized controlled trials, observational studies, randomized trials comparing 2 anti-TNF drugs without a control group.

Main Outcome Measure and how it will be categorized/defined for your study: 

Primary outcome : ACR20
The ACR20 is reported as ≥20% improvement, comparing disease activity at baseline and post-baseline comparison.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: 

- Age : <50 or ≥ 50 years
- gender : woman or man
- Disease duration : < 2 or ≥ 2 years
- DAS28 : ≤3,2, between 3,2 and 5,1, ≥ 5,1
- CRP : < 10 mg/l or ≥ 10 mg/l
- rheumatoid factor (RF) positivity or negativity
- anti-citrullinated protein antibody (ACPA) positivity or negativity
- Smoking status : current, non-current smoker
- Body Mass Index (BMI) : ≤ 25 or > 25
- physical activity < 30 min/week or ≥ 30 min/week

We created an excel extraction sheet if you need for collecting the data.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: 

none

Statistical Analysis Plan: 

A meta-analysis of aggregate data will be performed, following appropriate methods (relative risks or standardized mean difference) depending of the nature of the outcome considered. A fixed effect model will be performed first, with addition of a random effect model in case of significant heterogeneity. Heterogeneity will be considered significant if the m-value of the heterogeneity test is <0.10 or I² is higher than 50%.

Narrative Summary: 

The treat-to-target strategy in RA has been proposed to increase the therapeutic efficacy while minimizing the risk of adverse events. Therefore, it is important to assess if demographics and environmental factors (age, gender, disease duration, disease activity, CRP/ESR levels, RF and ACPA status, smoking status, BMI, physical activity) could influence anti-TNF treatment effect and the direction of this influence. These factors could therefore be considered when initiating anti-TNF in RA in order to increase response rate and anticipate and avoid failure.
Prospero registration number: CRD42018071079

Project Timeline: 

Estimation dates :
project start date : november 2017
analysis completion date : july 2018
date manuscript drafted : october 2018
first submitted : october-november 2018
date results reported back to the YODA Project : october-november 2018

Dissemination Plan: 

EULAR congress 2019
JBS, Rheumatology, Arthritis & care, BMJ, Journal of Rheumatology, ARD.

Bibliography: 

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- Hetland ML, Christensen IJ, Tarp U, Dreyer L, Hansen A, Hansen IT, et al. Direct comparison of treatment responses, remission rates, and drug adherence in patients with rheumatoid arthritis treated with adalimumab, etanercept, or infliximab: results from eight years of surveillance of clinical practice in the nationwide Danish DANBIO registry. Arthritis Rheum. 2010 Jan;62(1):22–32.
- Sugihara T, Harigai M. Targeting Low Disease Activity in Elderly-Onset Rheumatoid Arthritis: Current and Future Roles of Biological Disease-Modifying Antirheumatic Drugs. Drugs Aging. 2016 Feb;33(2):97–107.
- Hyrich KL, Watson KD, Silman AJ, Symmons DPM, British Society for Rheumatology Biologics Register. Predictors of response to anti-TNF-alpha therapy among patients with rheumatoid arthritis: results from the British Society for Rheumatology Biologics Register. Rheumatol Oxf Engl. 2006 Dec;45(12):1558–65.
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- Stoffer MA, Schoels MM, Smolen JS, Aletaha D, Breedveld FC, Burmester G, et al. Evidence for treating rheumatoid arthritis to target: results of a systematic literature search update. Ann Rheum Dis. 2016 Jan;75(1):16–22.
- Anderson JJ, Wells G, Verhoeven AC, Felson DT. Factors predicting response to treatment in rheumatoid arthritis: the importance of disease duration. Arthritis Rheum. 2000 Jan;43(1):22–9.
- Canhão H, Rodrigues AM, Mourão AF, Martins F, Santos MJ, Canas-Silva J, et al. Comparative effectiveness and predictors of response to tumour necrosis factor inhibitor therapies in rheumatoid arthritis. Rheumatol Oxf Engl. 2012 Nov;51(11):2020–6.
- Gibbons LJ, Hyrich KL. Biologic therapy for rheumatoid arthritis: clinical efficacy and predictors of response. BioDrugs Clin Immunother Biopharm Gene Ther. 2009;23(2):111–24.
- Kristensen LE, Kapetanovic MC, Gülfe A, Söderlin M, Saxne T, Geborek P. Predictors of response to anti-TNF therapy according to ACR and EULAR criteria in patients with established RA: results from the South Swedish Arthritis Treatment Group Register. Rheumatol Oxf Engl. 2008 Apr;47(4):495–9.
- Lannone F, Gremese E, Gallo G, Sarzi-Puttini P, Botsios C, Trotta F, et al. High rate of disease remission in moderate rheumatoid arthritis on etanercept therapy: data from GISEA, the Italian biologics register. Clin Rheumatol. 2014 Jan;33(1):31–7.
- Mattey DL, Brownfield A, Dawes PT. Relationship between pack-year history of smoking and response to tumor necrosis factor antagonists in patients with rheumatoid arthritis. J Rheumatol. 2009 Jun;36(6):1180–7.
- Söderlin MK, Petersson IF, Geborek P. The effect of smoking on response and drug survival in rheumatoid arthritis patients treated with their first anti-TNF drug. Scand J Rheumatol. 2012 Feb;41(1):1–9.
- Klaasen R, Wijbrandts CA, Gerlag DM, Tak PP. Body mass index and clinical response to infliximab in rheumatoid arthritis. Arthritis Rheum. 2011 Feb;63(2):359–64.
- Ottaviani S, Gardette A, Tubach F, Roy C, Palazzo E, Gill G, et al. Body mass index and response to infliximab in rheumatoid arthritis. Clin Exp Rheumatol. 2015 Aug;33(4):478–83.
- Eurenius E, Stenström CH. Physical activity, physical fitness, and general health perception among individuals with rheumatoid arthritis. Arthritis Rheum. 2005 Feb 15;53(1):48–55.
- Stenström CH, Minor MA. Evidence for the benefit of aerobic and strengthening exercise in rheumatoid arthritis. Arthritis Rheum. 2003 Jun 15;49(3):428–34.
- Smolen JS, Landewé R, Bijlsma J, Burmester G, Chatzidionysiou K, Dougados M, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2016 update. Ann Rheum Dis. 2017 Jun;76(6):960–77.
- Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009 Oct;62(10):1006–12.
- Arnett FC, Edworthy SM, Bloch DA, McShane DJ, Fries JF, Cooper NS, et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum. 1988 Mar;31(3):315–24.
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