International Journal of Rheumatic Diseases 2014; 17: 716–724
REVIEW ARTICLE
Tuberculosis risk and anti-tumour necrosis factor agents in rheumatoid arthritis: a critical appraisal of national registry data Rossana SCRIVO1 and Orlando ARMIGNACCO2 1
Department of Internal Medicine and Medical Specialties, Rheumatology, Sapienza University of Rome, Rome, and 2Infectious Diseases Unit, Belcolle Hospital, Viterbo, Italy
Abstract Tuberculosis (TB) remains a major global health problem. In patients with rheumatoid arthritis (RA), the risk of reactivation of latent TB infection (LTBI) is increased and treatment with tumour necrosis factor (TNF) antagonists further increases this risk. However, interpretation of results describing comparative TB risk during therapy with different TNF antagonists is difficult. This is not only a result of different patient ethnic groups and background TB rates, but also because of differing methods of data acquisition. This paper offers a critical appraisal of registry data pertaining to RA patients treated with different anti-TNF agents, focusing on methodological approaches that may limit the generalizability of findings or invalidate the direct comparison of TB risk between different national registries. Underlying factors that can make data interpretation challenging are discussed, including differences in methods for TB diagnosis or data collection and reporting, as well as background TB risk. The introduction of special monitoring systems, such as prospective multinational registries, to strengthen surveillance and better quantify the extent of under-reporting is required, especially in countries where the background TB risk is high. Key words: anti-TNF agents, latent tuberculosis infection, registry data, rheumatoid arthritis, tuberculosis, tuberculosis prevention.
INTRODUCTION Tuberculosis (TB) is an infectious disease caused by the bacillus Mycobacterium tuberculosis, which typically manifests in the lungs (pulmonary TB) but can also affect other sites (extrapulmonary TB).1 In 2012, there were an estimated 8.6 million incident cases of TB globally (13% co-infected with HIV), which is equivalent to 122 cases per 100 000 population (Fig. 1).1 The majority of people infected with M. tuberculosis are asymptomatic, a condition known as latent TB infection (LTBI).2 It is well established that only a Correspondence: Dr Rossana Scrivo, Dipartimento di Medicina Interna e Specialita Mediche, Reumatologia – Sapienza Universita di Roma – viale del Policlinico 155, 00161 Rome, Italy. Email: [email protected]
minority of individuals infected with M. tuberculosis will go on to ever develop TB.2 In the general population, the lifetime risk of progression from LTBI to active, symptomatic disease is around 5–10%. Nevertheless, LTBI makes a significant contribution to the future pool of active TB cases, making diagnosis and treatment crucial, especially in high-risk groups.2 The introduction of biological agents, especially the blockers of tumor necrosis factor (TNF), for the treatment of immune-mediated diseases has renewed interest in the risk of developing TB in the setting of immunosuppression. Indeed, the use of anti-TNF agents may favor reactivation of TB infection via neutralization of TNF, which protects the host against M. tuberculosis.3 This analysis provides a critical appraisal of registry data pertaining to patients treated with anti-TNF agents,
© 2014 Asia Pacific League of Associations for Rheumatology and Wiley Publishing Asia Pty Ltd
Tuberculosis risk with anti-TNF agents
Figure 1 Estimated tuberculosis incidence rates, 2012. Reproduced, with the permission of the publisher, from the Global Tuberculosis Report 2013. Geneva, World Health Organization, 2013: (Fig. 2.5, Page 14 http://apps.who.int/iris/bitstream/10665/ 91355/1/9789241564656_eng.pdf, accessed 04 April 2014).1
focusing on differences that may limit the generalizability of findings or invalidate direct comparison of TB risk between different national registries. In addition to methodological bias, other underlying factors that can result in inappropriate data interpretation are examined.
RISK OF TB IN RHEUMATOID ARTHRITIS (RA) PATIENTS TREATED WITH TNF ANTAGONISTS The risk of active TB is increased in RA patients, mainly as a consequence of reactivation of LTBI.4 Several studies have reported a two- to 10-fold increase in the risk of TB in TNF antagonist-na€ıve RA patients and a two- to 30-fold risk increase in those exposed to TNF antagonists, both compared with the general population.5–8 These data include the results of a Swedish study showing that the risk of TB was increased by four-fold in RA patients treated with anti-TNF agents compared with those who were not.7 With TB occurring during anti-TNF therapy, the majority of cases emerged soon after initiation of treatment, suggesting reactivation of LTBI, although delayed-onset cases consistent with new
International Journal of Rheumatic Diseases 2014; 17: 716–724
infection have occasionally been reported.9 Such data prompted the rapid development of recommendations on screening and treatment of LTBI in patients considered for treatment with anti-TNF therapy. However, there is still a lack of consensus between countries as to how best to screen for and treat LTBI,10–21 partially due to different TB backgrounds (Table 1). Since 2000, active surveillance registries have been established in several countries, providing additional information on the TB risk associated with TNF antagonist therapy in a real-world practice setting7,9,15,22,23 (Table 2). Despite surveillance efforts, interpretation of data on the risk of developing TB after initiation of TNF antagonists is challenging. This is due to a number of factors, including the influence of patient ethnic groups and background TB rates, along with methodological differences in the way data were collected. In general, the risk of TB reactivation appears lower with the soluble TNF receptor etanercept compared with anti-TNF monoclonal antibodies, suggesting that membrane-bound and soluble TNF could play different roles in the control of TB infection.24 Without taking this into account, a Spanish study estimated TB rates only after the establishment of recommendations to prevent reactivation
717
718
Spain
Switzerland
Carmona et al. (2005)14 and Gomez-Reino et al. (2007)15
Beglinger et al. (2007)16
Ireland
Kavanagh et al. (2008)12
Portugal
Germany
Diel et al. (2009)11
Fonseca et al. (2008)13
France
Country
Mariette et al. (2003)10
Study
All patients
All patients
All patients
All patients
All patients
All patients
Risk assessment examination CXR
Not recommended
All patients
All patients
Only if discrepancy between strong epidemiological evidence of prior TB exposure and negative IGRA All patients
All patients
TST
5 mm
> 5 mm
Yes
No
No
If available
Yes
> 5 mm
10, 5 mm for IS, no change for BCG vaccinated
No
IGRA testing
10 mm
Positive TST
IGRA+, abnormal CXR suggestive of past TB inadequately treated; history of exposure
TST+, consider prophylactic treatment in TST-negative patients TST+, abnormal CXR suggestive of past TB inadequately treated; history of exposure
TST+
TST+, history of TB treated before 1970 or not treated for at least 6 months; CXR lesions larger than 1 cm3 with no certainty of treatment IGRA+, abnormal CXR suggestive of past TB inadequately treated; history of exposure
Who should receive prophylaxis?
9H or 4R
9H
9H
9H 4R 4RH
9H or 4R
2RZ 3RH 9H
LTBI Treatment†
Table 1 Summary of national guidelines for tuberculosis (TB) screening and prophylaxis in patients scheduled for biologic treatment
1 month, but consider days after or at same time as starting prophylaxis 1 month after completion of prophylaxis
As long as possible after starting prophylaxis 1 month on prophylaxis
On completion of TB treatment > 2 months on TB treatment
> 2 months after completion of TB treatment
> 3 weeks after starting prophylaxis
1–2 months after starting prophylaxis
Active TB
LTBI
Time delay before anti-TNF therapy
R. Scrivo and O. Armignacco
International Journal of Rheumatic Diseases 2014; 17: 716–724
International Journal of Rheumatic Diseases 2014; 17: 716–724
USA
Italy
Canada
Mazurek et al. (2010)19
Favalli et al. (2011)20
Bombardier et al. (2012)21 High-risk groups
All patients
All patients
All patients
Risk assessment examination CXR
All patients
All patients
All patients
Not for patients on IS as unreliable
TST
≥ 5 mm
5 mm if IS, 10 mm if risks, e.g. new immigrant, drug users 15 mm if low risk > 5 mm
5 mm in unvaccinated, 15 mm in vaccinated
Positive TST
In selected patients (low-grade TST positivity and previous BCG vaccination) IGRAs may be useful to complete the screening program In selected patients (to identify falsepositive TST in patients who have received BCG and have no epidemiologic r isk factors)
Yes
No (update due 2010 by NICE)
IGRA testing
Any RA patient with LTBI
TST+, abnormal CXR suggestive of past TB; history of exposure or prior partially treated
TST+ in presence of clinical suspicion, TST if clinical or epidemiological risks
TST+ stratified for risk; previous TB inadequately treated or abnormal CXR; IS patients stratified for risk
Who should receive prophylaxis?
9H
9H
9H
6H or 3HR
LTBI Treatment†
1–2 months after the initiation of prophylaxis
1 month on prophylaxis
If abnormal CXR or history of TB, complete prophylaxis. If normal CXR or IS can start concurrently Preferably complete prophylaxis
LTBI
Preferably complete TB t reatment
> 2 months on TB treatment
Active TB
Time delay before anti-TNF therapy
CXR, chest radiograph; TST, tuberculin skin test; IGRA, interferon-c release assay; LTBI, latent tuberculosis infection; TNF, tumour necrosis factor; TB, tuberculosis; IS, immunosuppressed/ immunosuppression; BCG, bacille Calmette-Guerin; NICE, National Institute for Clinical Excellence; min, minimum; RZ, rifampicin plus pyrazinamide; RH, rifampicin plus isoniazid; H, isoniazid: R, rifampicin. †The number denotes the number of months of LTBI treatment.
UK
Country
BTS (2005)17 and NICE (2011)18
Study
Table 1 (continued)
Tuberculosis risk with anti-TNF agents
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Table 2 Risk of active tuberculosis in patients treated with anti-tumour necrosis factor therapy: synopsis of registry data Study
Country
Reference period
ADA
ETN
INF
Notes
IR 80† (16–232)
IR 145† (3.3–719)
The difference observed between etanercept and infliximab was based only on a few cases. Infliximab (highest incidence rate) vs. etanercept (lowest incidence rate) P = 0.2264 69 TB cases developed in RA patients who did not receive correct prophylaxis as recommended Includes patients prior to TB screening and those following screening recommendations
Askling et al. (2005)7
Sweden (ARTIS)
1999–2004
Gomez-Reino et al. (2007)15
Spain (BIOBADASER)
From September 2003 (after TB recommendations dissemination)
IR 176‡ (24–1254)
IR 114‡ (28–459)
IR 383‡ (159–921)
Tubach et al. (2009)22
France (RATIO)
2004–2007
IR 215.0‡ (0.0–521.7)
IR 9.3‡ (0.0–9.4)
IR 187.5‡ (0.1–374.8
Dixon et al. (2010)30
UK (BSRBR)
2001–2008
IR 144‡ (72–258)
IR 39‡ (13–92)
IR 136‡ (68–244)
Because the table only includes national registries, the category of evidence and strength of the recommendation according to the category of evidence are IIIC for all of them.36 ADA, adalimumab; ETN, etanercept; INF, infliximab; IR, incidence rate; TB, tuberculosis. †IR values are given 100 000 person-years. ‡IR values are given per 100 000 people.
of LTBI, predictably resulting in no significant difference in risk between the three TNF antagonists examined (the monoclonal antibodies adalimumab and infliximab, and etanercept); the study was also underpowered to detect significant differences between agents in TB rates15 Swedish registry data also suggest that the difference in risk between infliximab and etanercept may not be as marked as previously thought.7 This should be taken into consideration when analyzing TB risk data from RA patients not receiving appropriate TB prophylaxis, as was the case in the 69 TB cases included in the French RATIO registry.22 Apart from the risk of LTBI reactivation associated with biological agents, another matter of concern pertains to newly acquired TB infection and development of active disease during the course of treatment with biological agents. Interestingly, using a Markov model to describe transitions between clinical states (Fig. 2),
720
both the monoclonal antibodies and the soluble receptor were equivalent with respect to the rate of newly acquired TB infection.25 Therefore, regardless of the anti-TNF agent used, patients receiving this therapy should be closely monitored throughout the treatment period. It has been suggested that clinical information should guide appropriate management and treatment decisions in patients with anti-TNF-related TB infection, because diagnostic tests are not always reliable.26,27
COMPARABILITY BETWEEN REGISTRIES: KEY POINTS By definition, registry data are heterogeneous and are subject to several selection and methodological biases, even in large, comprehensive data sets.28 Nevertheless, comparability between different registry populations is actually quite important, particularly when trying to
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Tuberculosis risk with anti-TNF agents
number of doses of each anti-TNF agent sold in France during the 3-year study, both according to the national French agency of drugs and the three pharmaceutical company estimations. Therefore, the denominator of the incidence rate is based only on estimates.22 Low numbers of events in observational studies should also be considered as a limiting factor of such analyses.7,15 Nationwide registries that have the ability to assess comorbidity rates via linking to other registries, such as those in Sweden, increase the likelihood of obtaining high-quality data. Figure 2 Progression of Mycobacterium tuberculosis infection. Modified from Wallis (2008).25
Units of measure for TB rate
identify rare events that do not have standardized recording protocols. As a result, it would be helpful to analyze local administrative databases in order to get high-quality data from populations that effectively represent the setting and events under investigation. Issues related to the methods of data analysis are often ignored, but these must be taken into consideration when interpreting data, even from each individual registry.28 When considering TB risk associated with the use of biological agents, we believe that all sources of variability should be carefully considered. In the subsequent sections we discuss methodological bias and other underlying factors that may result in inappropriate data interpretation when comparing registry information.
Methods commonly used to report data (i.e. events per patient-years, 1000 or 100 000 patient-years, standardized incidence ratio, relative risk) differ from study to study. Furthermore, the risk of TB with anti-TNF therapy may vary over time. Different average follow-up durations for treatment with different drugs may influence estimates if the risk of TB is non-linear. In a US registry report, excess TB cases were observed during the first 3 months of infliximab treatment compared with etanercept treatment, in both time-to-onset and timeto-report-date analyses.25 However, the median time to onset of TB was 6 weeks for infliximab, 3–8 months for adalimumab and 11.2 months for etanercept.32 Adverse events occurring soon after initiating a new therapy may be more likely to be reported, introducing a potential source of bias.
METHODOLOGICAL BIAS
UNDERLYING FACTORS
Criteria used to define and determine TB cases
Timing of anti-TNF agent commercialization
Final analyses should take into account whether TB was identified on the basis of indicative radiological findings, clinical suspicion or patient self-report,29 positive bacterial culture,14 isoniazid prescription30 or discharge diagnosis.7 Each method has its limitations.
Disparity in anti-TNF agent usage prior to the institution of TB screening and prophylaxis guidelines could explain the differences in TB rates among patients taking different TNF antagonists. Etanercept was not commercially available before screening tests were recommended, resulting in fewer patients taking etanercept compared with infliximab under these conditions. Furthermore, adalimumab was licensed later than both infliximab and etanercept, meaning that patients receiving adalimumab may have been more likely to have received previous therapy with other anti-TNF drugs. This is important because sequential drug use may multiply the risk of developing TB.23
TB data source Estimates of rates in spontaneous pharmacovigilance studies can be imprecise as a result of under-reporting of cases, because physician reporting is voluntary or the denominator may be unknown. ‘Unknown denominator’ refers to uncertainties with respect to the sample size corresponding to the reported number of adverse events.31 The French RATIO study, for example, attempted to examine drug-specific risk by calculating event rates from spontaneously reported cases of TB and estimates of national anti-TNF drug use across all indications. Such estimates were based on the total
International Journal of Rheumatic Diseases 2014; 17: 716–724
Single versus multiple indications When interpreting TB risk data in the setting of antiTNF therapy, it is important to note whether rates are for patients with one disease or whether data are pooled
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across a number of different indications. This can influence TB risk comparison between agents as a result of different approved indications (e.g. etanercept is not indicated in Crohn’s disease), different timing of drug development (infliximab was the first biologic drug to be approved in Crohn’s disease and then in RA, when no TB prophylaxis guidelines existed) and the potential differences between underlying disease states with respect to TB risk. For example, looking at adalimumab, TB risk decreases from 0.5/100 patient-years in patients with RA33 to 0.2/100 patient-years when patients with psoriasis or Crohn’s disease are added.34
Pooling data for different drugs Pooling TB risk data across different anti-TNF drugs is not accurate and does not provide information that can be readily translated into clinical practice. Available data suggest that, although TB rates appear higher in patients receiving anti-TNF monoclonal antibodies, treatment with the soluble TNF receptor etanercept is not completely without risk. Therefore, clinicians should always be alert for the possibility of TB reactivation or development during etanercept therapy.23 Compliance with preventive isoniazid appears to dramatically reduce TB reactivation risk in patients treated with any anti-TNF agent. The Spanish registry is the only prospective cohort study to date. It showed an 80% reduction in TB rate after implementation of the official recommendations for 9 months of isoniazid therapy in patients with either positive tuberculin skin test (TST) (or two-step TST), past history of untreated TB or chest radiograph suggestive of past TB.15 The benefit was only apparent in patients who were compliant with prophylactic therapy. Conversely, TB rates in the French RATIO registry pertain to patients who had never received correct chemoprophylaxis according to the French recommendations.22 LTBI reactivation and new TB cases have been detected during treatment with golimumab, a newer anti-TNF agent, in patients with negative screening tests at baseline, highlighting the need for ongoing followup after initiation of anti-TNF therapy.35 In summary, screening and treatment of LTBI is warranted for all patients receiving any TNF antagonist, irrespective of the molecular structure,24 because the potential to develop active TB during anti-TNF therapy seems to be a class effect.3
Background TB risk The lack of international guidelines on screening for TB infection and heterogeneity in the recommendations
722
between countries10–21 need to be taken into account when interpreting and comparing data from different registries. The association of TB with anti-TNF therapy largely represents reactivation of latent infection, meaning that the burden of TB attributable to TNF antagonists may be a function of past prevalence of natural infection with TB. In a retrospective study from the US, the increased rates of TB among foreign-born individuals reflected the greater prevalence of M. tuberculosis infection outside the US.29 In the British Society for Rheumatology Biologics Registry data, no stratified analysis of the presence of latent TB in infliximab-treated patients was carried out.23,28 As a result, the lack of geographic or ethnicity data does not allow an accurate assessment of the true risk of TB associated with the use of different anti-TNF drugs.
CONCLUDING REMARKS The risk of reactivation of LTBI is increased in patients with RA and treatment with TNF antagonists further increases this risk. Screening and correct prophylaxis are the most effective ways to prevent reactivation of LTBI during anti-TNF treatment. Indeed, TB screening prior to therapy is strongly recommended for all patients, irrespective of the anti-TNF agent used. The introduction of special monitoring systems, such as prospective multinational registries, to strengthen surveillance and better quantify the extent of under-reporting of data is required, especially in countries where the TB risk is high. However, care needs to be taken when interpreting and comparing registry data. Factors to consider include TB definition and diagnosis, source(s) of data, units of measurement, timing of anti-TNF agent commercialization and guideline implementation, approved indications and background TB risk. Acknowledgement of these factors may facilitate better comparison of the real risk of TB between different anti-TNF agents, and also provide a platform from which to design better registries.
ACKNOWLEDGEMENTS Native English editing services were provided by Nicola Ryan, independent medical writer.
FUNDING SOURCES There were no funding sources for this work.
International Journal of Rheumatic Diseases 2014; 17: 716–724
Tuberculosis risk with anti-TNF agents
CONFLICT OF INTEREST The authors have no conflict of interest to disclose.
AUTHORS CONTRIBUTIONS RS conceived the idea for the paper, did the background research, drafted and critically reviewed both data and manuscript drafts. OA defined the research theme and reviewed all data and drafts.
REFERENCES 1 World Health Organization (2013) WHO Report 2013: Global Tuberculosis Report 2013. Available from URL: www.who.int/tb/publications/global_report/en. 2 Mack U, Migliori GB, Sester M et al. (2009) LTBI: latent tuberculosis infection or lasting immune responses to M. tuberculosis? A TBNET consensus statement. Eur Respir J 33, 956–73. 3 Gardam MA, Keystone EC, Menzies R et al. (2003) Antitumour necrosis factor agents and tuberculosis risk: mechanisms of action and clinical management. Lancet Infect Dis 3, 148–55. 4 Solovic I, Sester M, Gomez-Reino JJ et al. (2010) The risk of TB related to TNF antagonist therapies: a TBNET consensus statement. Eur Respir J 36, 1185–206. 5 Baronnet L, Barnetche T, Kahn V, Lacoin C, Richez C, Schaeverbeke T (2011) Incidence of tuberculosis in patients with rheumatoid arthritis. A systematic literature review. Joint Bone Spine 78, 279–84. 6 Carmona L, Hernandez-Garcia C, Vadillo C et al. (2003) Increased risk of tuberculosis in patients with rheumatoid arthritis. J Rheumatol 30, 1436–9. 7 Askling J, Fored CM, Brandt L et al. (2005) Risk and case characteristics of tuberculosis in rheumatoid arthritis associated with tumor necrosis factor antagonists in Sweden. Arthritis Rheum 52, 1986–92. 8 Seong SS, Choi CB, Woo JH et al. (2007) Incidence of tuberculosis in Korean patients with rheumatoid arthritis (RA): effects of RA itself and of tumor necrosis factor blockers. J Rheumatol 34, 706–11. 9 Wallis RS, Broder MS, Wong JY, Hanson ME, Beenhouwer DO (2004) Granulomatous infectious diseases associated with tumor necrosis factor antagonists. Clin Infect Dis 38, 1261–5. 10 Mariette X, Salmon D (2003) French guidelines for diagnosis and treating latent and active tuberculosis in patients with RA treated with TNF blockers. Ann Rheum Dis 62, 791. 11 Diel R, Hauer B, Loddenkemper R, Manger B, Kr€ uger K (2009) Recommendations for tuberculosis screening before initiation of TNF-alpha-inhibitor treatment in rheumatic diseases. Pneumologie 63, 329–34.
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12 Kavanagh PM, Gilmartin JJ, O’Donnell J, O’Flanagan D (2008) Tumour necrosis factor-alpha and tuberculosis: guidance from the National TB Advisory Committee. Ir Med J 101, 6–7. 13 Fonseca JE, Lucas H, Canh~ao H et al. (2008) Recommendations for the diagnosis and treatment of latent and active tuberculosis in inflammatory joint diseases candidates for therapy with tumor necrosis factor alpha inhibitors: March 2008 update. Acta Reumatol Port 33, 77–85. 14 Carmona L, Gomez-Reino JJ, Rodriguez-Valverde V et al. (2005) Effectiveness of recommendations to prevent reactivation of latent tuberculosis infection in patients treated with tumor necrosis factor antagonists. Arthritis Rheum 52, 1766–72. 15 Gomez-Reino JJ, Carmona L, Angel DM (2007) Risk of tuberculosis in patients treated with tumor necrosis factor antagonists due to incomplete prevention of reactivation of latent infection. Arthritis Rheum 57, 756–61. 16 Beglinger C, Dudler J, Mottet C et al. (2007) Screening for tuberculosis infection before the initiation of an anti-TNFalpha therapy. Swiss Med Wkly 137, 620–2. 17 British Thoracic Society Standards of Care Committee (2005) BTS recommendations for assessing risk and for managing Mycobacterium tuberculosis infection and disease in patients due to start anti-TNF-alpha treatment. Thorax 60, 800–5. 18 National Institute for Health and Clinical Excellence (2011) Tuberculosis: Clinical Diagnosis and Management of Tuberculosis, and Measures for its Prevention and Control. Available from URL: http://www.nice.org.uk/ nicemedia/live/13422/53642/53642.pdf 19 Mazurek GH, Jereb J, Vernon A, Lobue P, Goldberg S, Castro K; IGRA Expert Committee; Centers for Disease Control and Prevention (CDC) (2010) Updated guidelines for using Interferon Gamma Release Assays to detect Mycobacterium tuberculosis infection – United States, 2010. MMWR Recomm Rep 59, 1–25. 20 Favalli EG, Caporali R, Sinigaglia L et al. (2011) Recommendations for the use of biologic therapy in rheumatoid arthritis: update from the Italian Society for Rheumatology II. Safety. Clin Exp Rheumatol 29, S15–27. 21 Bombardier C, Hazlewood GS, Akhavan P et al. (2012) Canadian Rheumatology Association recommendations for the pharmacological management of rheumatoid arthritis with traditional and biologic disease-modifying antirheumatic drugs: part II safety. J Rheumatol 39, 1583–602. 22 Tubach F, Salmon D, Ravaud P et al. (2009) Risk of tuberculosis is higher with anti-tumor necrosis factor monoclonal antibody therapy than with soluble tumor necrosis factor receptor therapy: the three-year prospective French research axed on tolerance of biotherapies registry. Arthritis Rheum 60, 1884–94.
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23 Dixon WG, Hyrich K, Watson KD, Manger B, Kr€ uger K (2010) Drug-specific risk of tuberculosis in patients with rheumatoid arthritis treated with anti-TNF therapy: results from the British Society for Rheumatology Biologics Register (BSRBR). Ann Rheum Dis 69, 522–8. 24 Salgado E, G omez-Reino JJ (2011) The risk of tuberculosis in patients treated with TNF antagonists. Expert Rev Clin Immunol 7, 329–40. 25 Wallis RS (2008) Tumour necrosis factor antagonists: structure, function, and tuberculosis risks. Lancet Infect Dis 8, 601–11. 26 Scrivo R, Sauzullo I, Mengoni F et al. (2013) Mycobacterial interferon-c release variations during longterm treatment with tumor necrosis factor blockers: lack of correlation with clinical outcome. J Rheumatol 40, 157–65. 27 Scrivo R, Sauzullo I, Mengoni F et al. (2012) Serial interferon-c release assays for screening and monitoring of tuberculosis infection during treatment with biologic agents. Clin Rheumatol 31, 1567–75. 28 Dixon WG, Symmons DP, Lunt M et al. (2007) Serious infection following anti-tumor necrosis factor alpha therapy in patients with rheumatoid arthritis: lessons from interpreting data from observational studies. Arthritis Rheum 56, 2896–904. 29 Wolfe F, Michaud K, Anderson J, Urbansky K (2004) Tuberculosis infection in patients with rheumatoid arthritis and the effect of infliximab therapy. Arthritis Rheum 50, 372–9.
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30 Brassard P, Kezouh A, Suissa S (2006) Antirheumatic drugs and the risk of tuberculosis. Clin Infect Dis 43, 717–22. 31 Evans SJ (2000) Pharmacovigilance: a science or fielding emergencies? Stat Med 19, 3199–209. 32 Theis VS, Rhodes JM (2008) Review article: minimizing tuberculosis during anti-tumour necrosis factor-alpha treatment of inflammatory bowel disease. Aliment Pharmacol Ther 27, 19–30. 33 Burmester GR, Mariette X, Montecucco C et al. (2007) Adalimumab alone and in combination with diseasemodifying antirheumatic drugs for the treatment of rheumatoid arthritis in clinical practice: the Research in Active Rheumatoid Arthritis (ReAct) trial. Ann Rheum Dis 66, 732–9. 34 Burmester GR, Panaccione R, Gordon KB, Mcilraith MJ, Lacerda AP (2013) Adalimumab: long-term safety in 23 458 patients from global clinical trials in rheumatoid arthritis, juvenile idiopathic arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis and Crohn’s disease. Ann Rheum Dis 72, 517–24. 35 Hsia EC, Cush JJ, Matteson EL et al. (2013) Comprehensive tuberculosis screening program in patients with inflammatory arthritides treated with golimumab, a human anti-tumor necrosis factor antibody, in Phase III clinical trials. Arthritis Care Res 65, 309–13. 36 Eccles M, Mason J (2001) How to develop cost-conscious guidelines. Health Technol Assess 5, 1–69.
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REVIEW ARTICLE
Tuberculosis risk and anti-tumour necrosis factor agents in rheumatoid arthritis: a critical appraisal of national registry data Rossana SCRIVO1 and Orlando ARMIGNACCO2 1
Department of Internal Medicine and Medical Specialties, Rheumatology, Sapienza University of Rome, Rome, and 2Infectious Diseases Unit, Belcolle Hospital, Viterbo, Italy
Abstract Tuberculosis (TB) remains a major global health problem. In patients with rheumatoid arthritis (RA), the risk of reactivation of latent TB infection (LTBI) is increased and treatment with tumour necrosis factor (TNF) antagonists further increases this risk. However, interpretation of results describing comparative TB risk during therapy with different TNF antagonists is difficult. This is not only a result of different patient ethnic groups and background TB rates, but also because of differing methods of data acquisition. This paper offers a critical appraisal of registry data pertaining to RA patients treated with different anti-TNF agents, focusing on methodological approaches that may limit the generalizability of findings or invalidate the direct comparison of TB risk between different national registries. Underlying factors that can make data interpretation challenging are discussed, including differences in methods for TB diagnosis or data collection and reporting, as well as background TB risk. The introduction of special monitoring systems, such as prospective multinational registries, to strengthen surveillance and better quantify the extent of under-reporting is required, especially in countries where the background TB risk is high. Key words: anti-TNF agents, latent tuberculosis infection, registry data, rheumatoid arthritis, tuberculosis, tuberculosis prevention.
INTRODUCTION Tuberculosis (TB) is an infectious disease caused by the bacillus Mycobacterium tuberculosis, which typically manifests in the lungs (pulmonary TB) but can also affect other sites (extrapulmonary TB).1 In 2012, there were an estimated 8.6 million incident cases of TB globally (13% co-infected with HIV), which is equivalent to 122 cases per 100 000 population (Fig. 1).1 The majority of people infected with M. tuberculosis are asymptomatic, a condition known as latent TB infection (LTBI).2 It is well established that only a Correspondence: Dr Rossana Scrivo, Dipartimento di Medicina Interna e Specialita Mediche, Reumatologia – Sapienza Universita di Roma – viale del Policlinico 155, 00161 Rome, Italy. Email: [email protected]
minority of individuals infected with M. tuberculosis will go on to ever develop TB.2 In the general population, the lifetime risk of progression from LTBI to active, symptomatic disease is around 5–10%. Nevertheless, LTBI makes a significant contribution to the future pool of active TB cases, making diagnosis and treatment crucial, especially in high-risk groups.2 The introduction of biological agents, especially the blockers of tumor necrosis factor (TNF), for the treatment of immune-mediated diseases has renewed interest in the risk of developing TB in the setting of immunosuppression. Indeed, the use of anti-TNF agents may favor reactivation of TB infection via neutralization of TNF, which protects the host against M. tuberculosis.3 This analysis provides a critical appraisal of registry data pertaining to patients treated with anti-TNF agents,
© 2014 Asia Pacific League of Associations for Rheumatology and Wiley Publishing Asia Pty Ltd
Tuberculosis risk with anti-TNF agents
Figure 1 Estimated tuberculosis incidence rates, 2012. Reproduced, with the permission of the publisher, from the Global Tuberculosis Report 2013. Geneva, World Health Organization, 2013: (Fig. 2.5, Page 14 http://apps.who.int/iris/bitstream/10665/ 91355/1/9789241564656_eng.pdf, accessed 04 April 2014).1
focusing on differences that may limit the generalizability of findings or invalidate direct comparison of TB risk between different national registries. In addition to methodological bias, other underlying factors that can result in inappropriate data interpretation are examined.
RISK OF TB IN RHEUMATOID ARTHRITIS (RA) PATIENTS TREATED WITH TNF ANTAGONISTS The risk of active TB is increased in RA patients, mainly as a consequence of reactivation of LTBI.4 Several studies have reported a two- to 10-fold increase in the risk of TB in TNF antagonist-na€ıve RA patients and a two- to 30-fold risk increase in those exposed to TNF antagonists, both compared with the general population.5–8 These data include the results of a Swedish study showing that the risk of TB was increased by four-fold in RA patients treated with anti-TNF agents compared with those who were not.7 With TB occurring during anti-TNF therapy, the majority of cases emerged soon after initiation of treatment, suggesting reactivation of LTBI, although delayed-onset cases consistent with new
International Journal of Rheumatic Diseases 2014; 17: 716–724
infection have occasionally been reported.9 Such data prompted the rapid development of recommendations on screening and treatment of LTBI in patients considered for treatment with anti-TNF therapy. However, there is still a lack of consensus between countries as to how best to screen for and treat LTBI,10–21 partially due to different TB backgrounds (Table 1). Since 2000, active surveillance registries have been established in several countries, providing additional information on the TB risk associated with TNF antagonist therapy in a real-world practice setting7,9,15,22,23 (Table 2). Despite surveillance efforts, interpretation of data on the risk of developing TB after initiation of TNF antagonists is challenging. This is due to a number of factors, including the influence of patient ethnic groups and background TB rates, along with methodological differences in the way data were collected. In general, the risk of TB reactivation appears lower with the soluble TNF receptor etanercept compared with anti-TNF monoclonal antibodies, suggesting that membrane-bound and soluble TNF could play different roles in the control of TB infection.24 Without taking this into account, a Spanish study estimated TB rates only after the establishment of recommendations to prevent reactivation
717
718
Spain
Switzerland
Carmona et al. (2005)14 and Gomez-Reino et al. (2007)15
Beglinger et al. (2007)16
Ireland
Kavanagh et al. (2008)12
Portugal
Germany
Diel et al. (2009)11
Fonseca et al. (2008)13
France
Country
Mariette et al. (2003)10
Study
All patients
All patients
All patients
All patients
All patients
All patients
Risk assessment examination CXR
Not recommended
All patients
All patients
Only if discrepancy between strong epidemiological evidence of prior TB exposure and negative IGRA All patients
All patients
TST
5 mm
> 5 mm
Yes
No
No
If available
Yes
> 5 mm
10, 5 mm for IS, no change for BCG vaccinated
No
IGRA testing
10 mm
Positive TST
IGRA+, abnormal CXR suggestive of past TB inadequately treated; history of exposure
TST+, consider prophylactic treatment in TST-negative patients TST+, abnormal CXR suggestive of past TB inadequately treated; history of exposure
TST+
TST+, history of TB treated before 1970 or not treated for at least 6 months; CXR lesions larger than 1 cm3 with no certainty of treatment IGRA+, abnormal CXR suggestive of past TB inadequately treated; history of exposure
Who should receive prophylaxis?
9H or 4R
9H
9H
9H 4R 4RH
9H or 4R
2RZ 3RH 9H
LTBI Treatment†
Table 1 Summary of national guidelines for tuberculosis (TB) screening and prophylaxis in patients scheduled for biologic treatment
1 month, but consider days after or at same time as starting prophylaxis 1 month after completion of prophylaxis
As long as possible after starting prophylaxis 1 month on prophylaxis
On completion of TB treatment > 2 months on TB treatment
> 2 months after completion of TB treatment
> 3 weeks after starting prophylaxis
1–2 months after starting prophylaxis
Active TB
LTBI
Time delay before anti-TNF therapy
R. Scrivo and O. Armignacco
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International Journal of Rheumatic Diseases 2014; 17: 716–724
USA
Italy
Canada
Mazurek et al. (2010)19
Favalli et al. (2011)20
Bombardier et al. (2012)21 High-risk groups
All patients
All patients
All patients
Risk assessment examination CXR
All patients
All patients
All patients
Not for patients on IS as unreliable
TST
≥ 5 mm
5 mm if IS, 10 mm if risks, e.g. new immigrant, drug users 15 mm if low risk > 5 mm
5 mm in unvaccinated, 15 mm in vaccinated
Positive TST
In selected patients (low-grade TST positivity and previous BCG vaccination) IGRAs may be useful to complete the screening program In selected patients (to identify falsepositive TST in patients who have received BCG and have no epidemiologic r isk factors)
Yes
No (update due 2010 by NICE)
IGRA testing
Any RA patient with LTBI
TST+, abnormal CXR suggestive of past TB; history of exposure or prior partially treated
TST+ in presence of clinical suspicion, TST if clinical or epidemiological risks
TST+ stratified for risk; previous TB inadequately treated or abnormal CXR; IS patients stratified for risk
Who should receive prophylaxis?
9H
9H
9H
6H or 3HR
LTBI Treatment†
1–2 months after the initiation of prophylaxis
1 month on prophylaxis
If abnormal CXR or history of TB, complete prophylaxis. If normal CXR or IS can start concurrently Preferably complete prophylaxis
LTBI
Preferably complete TB t reatment
> 2 months on TB treatment
Active TB
Time delay before anti-TNF therapy
CXR, chest radiograph; TST, tuberculin skin test; IGRA, interferon-c release assay; LTBI, latent tuberculosis infection; TNF, tumour necrosis factor; TB, tuberculosis; IS, immunosuppressed/ immunosuppression; BCG, bacille Calmette-Guerin; NICE, National Institute for Clinical Excellence; min, minimum; RZ, rifampicin plus pyrazinamide; RH, rifampicin plus isoniazid; H, isoniazid: R, rifampicin. †The number denotes the number of months of LTBI treatment.
UK
Country
BTS (2005)17 and NICE (2011)18
Study
Table 1 (continued)
Tuberculosis risk with anti-TNF agents
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Table 2 Risk of active tuberculosis in patients treated with anti-tumour necrosis factor therapy: synopsis of registry data Study
Country
Reference period
ADA
ETN
INF
Notes
IR 80† (16–232)
IR 145† (3.3–719)
The difference observed between etanercept and infliximab was based only on a few cases. Infliximab (highest incidence rate) vs. etanercept (lowest incidence rate) P = 0.2264 69 TB cases developed in RA patients who did not receive correct prophylaxis as recommended Includes patients prior to TB screening and those following screening recommendations
Askling et al. (2005)7
Sweden (ARTIS)
1999–2004
Gomez-Reino et al. (2007)15
Spain (BIOBADASER)
From September 2003 (after TB recommendations dissemination)
IR 176‡ (24–1254)
IR 114‡ (28–459)
IR 383‡ (159–921)
Tubach et al. (2009)22
France (RATIO)
2004–2007
IR 215.0‡ (0.0–521.7)
IR 9.3‡ (0.0–9.4)
IR 187.5‡ (0.1–374.8
Dixon et al. (2010)30
UK (BSRBR)
2001–2008
IR 144‡ (72–258)
IR 39‡ (13–92)
IR 136‡ (68–244)
Because the table only includes national registries, the category of evidence and strength of the recommendation according to the category of evidence are IIIC for all of them.36 ADA, adalimumab; ETN, etanercept; INF, infliximab; IR, incidence rate; TB, tuberculosis. †IR values are given 100 000 person-years. ‡IR values are given per 100 000 people.
of LTBI, predictably resulting in no significant difference in risk between the three TNF antagonists examined (the monoclonal antibodies adalimumab and infliximab, and etanercept); the study was also underpowered to detect significant differences between agents in TB rates15 Swedish registry data also suggest that the difference in risk between infliximab and etanercept may not be as marked as previously thought.7 This should be taken into consideration when analyzing TB risk data from RA patients not receiving appropriate TB prophylaxis, as was the case in the 69 TB cases included in the French RATIO registry.22 Apart from the risk of LTBI reactivation associated with biological agents, another matter of concern pertains to newly acquired TB infection and development of active disease during the course of treatment with biological agents. Interestingly, using a Markov model to describe transitions between clinical states (Fig. 2),
720
both the monoclonal antibodies and the soluble receptor were equivalent with respect to the rate of newly acquired TB infection.25 Therefore, regardless of the anti-TNF agent used, patients receiving this therapy should be closely monitored throughout the treatment period. It has been suggested that clinical information should guide appropriate management and treatment decisions in patients with anti-TNF-related TB infection, because diagnostic tests are not always reliable.26,27
COMPARABILITY BETWEEN REGISTRIES: KEY POINTS By definition, registry data are heterogeneous and are subject to several selection and methodological biases, even in large, comprehensive data sets.28 Nevertheless, comparability between different registry populations is actually quite important, particularly when trying to
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Tuberculosis risk with anti-TNF agents
number of doses of each anti-TNF agent sold in France during the 3-year study, both according to the national French agency of drugs and the three pharmaceutical company estimations. Therefore, the denominator of the incidence rate is based only on estimates.22 Low numbers of events in observational studies should also be considered as a limiting factor of such analyses.7,15 Nationwide registries that have the ability to assess comorbidity rates via linking to other registries, such as those in Sweden, increase the likelihood of obtaining high-quality data. Figure 2 Progression of Mycobacterium tuberculosis infection. Modified from Wallis (2008).25
Units of measure for TB rate
identify rare events that do not have standardized recording protocols. As a result, it would be helpful to analyze local administrative databases in order to get high-quality data from populations that effectively represent the setting and events under investigation. Issues related to the methods of data analysis are often ignored, but these must be taken into consideration when interpreting data, even from each individual registry.28 When considering TB risk associated with the use of biological agents, we believe that all sources of variability should be carefully considered. In the subsequent sections we discuss methodological bias and other underlying factors that may result in inappropriate data interpretation when comparing registry information.
Methods commonly used to report data (i.e. events per patient-years, 1000 or 100 000 patient-years, standardized incidence ratio, relative risk) differ from study to study. Furthermore, the risk of TB with anti-TNF therapy may vary over time. Different average follow-up durations for treatment with different drugs may influence estimates if the risk of TB is non-linear. In a US registry report, excess TB cases were observed during the first 3 months of infliximab treatment compared with etanercept treatment, in both time-to-onset and timeto-report-date analyses.25 However, the median time to onset of TB was 6 weeks for infliximab, 3–8 months for adalimumab and 11.2 months for etanercept.32 Adverse events occurring soon after initiating a new therapy may be more likely to be reported, introducing a potential source of bias.
METHODOLOGICAL BIAS
UNDERLYING FACTORS
Criteria used to define and determine TB cases
Timing of anti-TNF agent commercialization
Final analyses should take into account whether TB was identified on the basis of indicative radiological findings, clinical suspicion or patient self-report,29 positive bacterial culture,14 isoniazid prescription30 or discharge diagnosis.7 Each method has its limitations.
Disparity in anti-TNF agent usage prior to the institution of TB screening and prophylaxis guidelines could explain the differences in TB rates among patients taking different TNF antagonists. Etanercept was not commercially available before screening tests were recommended, resulting in fewer patients taking etanercept compared with infliximab under these conditions. Furthermore, adalimumab was licensed later than both infliximab and etanercept, meaning that patients receiving adalimumab may have been more likely to have received previous therapy with other anti-TNF drugs. This is important because sequential drug use may multiply the risk of developing TB.23
TB data source Estimates of rates in spontaneous pharmacovigilance studies can be imprecise as a result of under-reporting of cases, because physician reporting is voluntary or the denominator may be unknown. ‘Unknown denominator’ refers to uncertainties with respect to the sample size corresponding to the reported number of adverse events.31 The French RATIO study, for example, attempted to examine drug-specific risk by calculating event rates from spontaneously reported cases of TB and estimates of national anti-TNF drug use across all indications. Such estimates were based on the total
International Journal of Rheumatic Diseases 2014; 17: 716–724
Single versus multiple indications When interpreting TB risk data in the setting of antiTNF therapy, it is important to note whether rates are for patients with one disease or whether data are pooled
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across a number of different indications. This can influence TB risk comparison between agents as a result of different approved indications (e.g. etanercept is not indicated in Crohn’s disease), different timing of drug development (infliximab was the first biologic drug to be approved in Crohn’s disease and then in RA, when no TB prophylaxis guidelines existed) and the potential differences between underlying disease states with respect to TB risk. For example, looking at adalimumab, TB risk decreases from 0.5/100 patient-years in patients with RA33 to 0.2/100 patient-years when patients with psoriasis or Crohn’s disease are added.34
Pooling data for different drugs Pooling TB risk data across different anti-TNF drugs is not accurate and does not provide information that can be readily translated into clinical practice. Available data suggest that, although TB rates appear higher in patients receiving anti-TNF monoclonal antibodies, treatment with the soluble TNF receptor etanercept is not completely without risk. Therefore, clinicians should always be alert for the possibility of TB reactivation or development during etanercept therapy.23 Compliance with preventive isoniazid appears to dramatically reduce TB reactivation risk in patients treated with any anti-TNF agent. The Spanish registry is the only prospective cohort study to date. It showed an 80% reduction in TB rate after implementation of the official recommendations for 9 months of isoniazid therapy in patients with either positive tuberculin skin test (TST) (or two-step TST), past history of untreated TB or chest radiograph suggestive of past TB.15 The benefit was only apparent in patients who were compliant with prophylactic therapy. Conversely, TB rates in the French RATIO registry pertain to patients who had never received correct chemoprophylaxis according to the French recommendations.22 LTBI reactivation and new TB cases have been detected during treatment with golimumab, a newer anti-TNF agent, in patients with negative screening tests at baseline, highlighting the need for ongoing followup after initiation of anti-TNF therapy.35 In summary, screening and treatment of LTBI is warranted for all patients receiving any TNF antagonist, irrespective of the molecular structure,24 because the potential to develop active TB during anti-TNF therapy seems to be a class effect.3
Background TB risk The lack of international guidelines on screening for TB infection and heterogeneity in the recommendations
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between countries10–21 need to be taken into account when interpreting and comparing data from different registries. The association of TB with anti-TNF therapy largely represents reactivation of latent infection, meaning that the burden of TB attributable to TNF antagonists may be a function of past prevalence of natural infection with TB. In a retrospective study from the US, the increased rates of TB among foreign-born individuals reflected the greater prevalence of M. tuberculosis infection outside the US.29 In the British Society for Rheumatology Biologics Registry data, no stratified analysis of the presence of latent TB in infliximab-treated patients was carried out.23,28 As a result, the lack of geographic or ethnicity data does not allow an accurate assessment of the true risk of TB associated with the use of different anti-TNF drugs.
CONCLUDING REMARKS The risk of reactivation of LTBI is increased in patients with RA and treatment with TNF antagonists further increases this risk. Screening and correct prophylaxis are the most effective ways to prevent reactivation of LTBI during anti-TNF treatment. Indeed, TB screening prior to therapy is strongly recommended for all patients, irrespective of the anti-TNF agent used. The introduction of special monitoring systems, such as prospective multinational registries, to strengthen surveillance and better quantify the extent of under-reporting of data is required, especially in countries where the TB risk is high. However, care needs to be taken when interpreting and comparing registry data. Factors to consider include TB definition and diagnosis, source(s) of data, units of measurement, timing of anti-TNF agent commercialization and guideline implementation, approved indications and background TB risk. Acknowledgement of these factors may facilitate better comparison of the real risk of TB between different anti-TNF agents, and also provide a platform from which to design better registries.
ACKNOWLEDGEMENTS Native English editing services were provided by Nicola Ryan, independent medical writer.
FUNDING SOURCES There were no funding sources for this work.
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Tuberculosis risk with anti-TNF agents
CONFLICT OF INTEREST The authors have no conflict of interest to disclose.
AUTHORS CONTRIBUTIONS RS conceived the idea for the paper, did the background research, drafted and critically reviewed both data and manuscript drafts. OA defined the research theme and reviewed all data and drafts.
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