Incidence of severe infection in patients with rheumatoid arthritis taking biologic agents: a systematic review : JBI Evidence Synthesis

Secondary Logo

Journal Logo

Advanced Search
Evidence Syntheses

Incidence of severe infection in patients with rheumatoid arthritis taking biologic agents: a systematic review

Makimoto, Kiyoko1,2; Konno, Rie3; Kinoshita, Atsushi3; Kanzaki, Hatsumi3; Suto, Shunji4

Author Information

1Osaka University, Suita, Osaka, Japan

2The Japan Centre for Evidence Based Practice: A JBI Centre of Excellence, Osaka University, Suita, Osaka, Japan

3Hyogo Medical University, Hypgo, Kobe, Japan

4Nara Medical University, Kashihara, Nara, Japan

The authors declare no conflict of interest.

A Japanese-language version of the abstract of this review is available as Supplemental Digital Content [https://links.lww.com/SRX/A8].

Correspondence: Kiyoko Makimoto, [email protected]

JBI Evidence Synthesis 21(5):p 835-885, May 2023. | DOI: 10.11124/JBIES-22-00048

Abstract

Introduction

Rheumatoid arthritis (RA) is a chronic inflammatory disease, with a prevalence ranging from 0.5% to 1%.1 The effects of RA lead to a high incidence of inability to work, and it is estimated that 35% of patients with RA for 10 years ceased work prematurely.2

In the 1980s, disease-modifying antirheumatic drugs (DMARDs), such as methotrexate, a cytotoxic agent, became a significant treatment option for RA; these drugs are still being used as first-line treatment. In the late 1990s, a substantial change in the treatment of RA occurred with the introduction of biologic agents that served as targeted therapies for refractory RA. Biologic agents are antibody-based and highly effective in achieving and maintaining remission. Since their introduction, various biologic and synthetic drugs have been developed.1 There are 5 types of biologics: i) tumor necrosis factor-alpha (TNF-α) targeted therapy, ii) B-cell targeted therapy, iii) T-cell targeted therapy, and iv) interleukin (IL) targeted therapy, and v) other. These agents provide an antigen-binding site that directly targets a substrate thought to be involved in the pathogenesis of the disease, enabling immune-mediated depletion of the targeted substrate. They inactivate immune cells, such as macrophages, neutrophils, B-cells, and T-cells, and the resulting disturbance in the immune system may increase the risk of infection.

The first systematic review of the adverse outcomes of biologic agents, which was based on data from 9 clinical trials, reported an increased risk of severe infection and malignancy.3 With the global marketing of biologic agents for the treatment of RA, there is an increasing number of clinical trials and observational studies. A systematic review of observational studies on the risk of developing severe infection among patients receiving biologics identified an increased risk of infection requiring hospitalization in some studies.4 Another systematic review of randomized clinical trials showed that both standard-dose and high-dose biologics are associated with an increased risk of severe infection, compared with traditional DMARDs.5

These systematic reviews on adverse outcomes in severe infections examined relative risk and did not focus on the incidence of severe infections. Patients and clinicians should be made aware of the incidence and types of severe infection that may arise when using biologics, to facilitate early detection of such infections.

Major risk factors for severe infection associated with biologic treatment are age; comorbidities, such as chronic obstructive airways disease; and a history of glucocorticoid use.6 Types of severe infections associated with biologic treatment include respiratory, skin and soft tissue, genitourinary, and bone/joint infections. Further, the type of severe infections may be associated with the type of biologic agent.

In recent years, the incidence of severe infection has been reported via a registry of patients with RA or data from medical claims databases in the USA and Western Europe.4 These data sources, often captured using observational studies, reflect real-world cases. A review on the incidence of severe infection related to biologic treatment demonstrated that the incidence varied from 3.2 to 7.0 per 100 person-years in 4 small-scale studies.7 Since this publication was released, more than 40 observational studies on biologics reporting severe infection have been published.4

A preliminary search of PROSPERO, PubMed, the Cochrane Database of Systematic Reviews, and the JBI Evidence-based Practice Database failed to identify any current or in-progress systematic reviews on the topic.

Review questions

  • What is the incidence of severe infection in patients with RA who use biologic agents?
  • What is the distribution of severe infection by the site of infection and by type of biologic?
  • What is the mortality rate associated with severe infection?

Inclusion criteria

Participants

This review included studies of adult patients (≥18 years of age) with RA, residing in any country, and treated with biological DMARDs. The diagnosis of RA followed international standards, such as the 1987 American College of Rheumatology criteria or the 2010 American College of Rheumatology/European League Against Rheumatism Classification Criteria for RA.8 The change in diagnostic criteria should not affect the incidence of severe infection in patients receiving biologics because the 1987 diagnostic criteria did not include early-onset RA,9 which is not a target for biologic treatment. This change would also not affect the incidence of severe infection because administering biologic agents is not the first-line treatment for RA.

Condition

This review included studies reporting severe infections in patients with RA who received biologic agents, with severe infection being defined as an infection that requires hospitalization for treatment or outpatient intravenous treatment. Eligible biologic agents included anti-CD20 monoclonal antibodies (B-cell-targeted therapy), a TNF-α inhibitors, IL-1 receptor antagonists, IL-6 receptor antagonists, selective T-cell co-stimulation modulators, and Janus kinase (JAK) inhibitors.10 JAK is generally classified as a synthetic biologic DMARD, and JAK inhibitors were treated as biologic agents in this review.

Context

This review included studies of patients in inpatient and outpatient settings who were listed in an RA registry. Additionally, the studies were required to have included information on whether hospitals or regions conducted surveillance on the use of biologic DMARDs and adverse outcomes.

Types of studies

This review considered analytical observational studies, including longitudinal or cohort studies, reporting person-years of observation. Additionally, registry data on patients with RA and insurance claims data were included. Cross-sectional and case-control studies were excluded because they cannot accurately estimate infection incidence. Experimental study designs were excluded because the purpose of the study was to review patients’ real-world experiences.

Only studies published after 1999 were included, since the first biologic drug, infliximab (anti-TNF), was approved by the U.S. Food and Drug Administration in 1999.1

Methods

The systematic review was conducted following the JBI methodology for systematic reviews of incidence and prevalence.11

Search strategy

The search strategy aimed to locate published and unpublished studies. An initial limited search of PubMed and CINAHL was undertaken to identify articles on the topic of interest. The text words contained in the titles and abstracts of relevant articles and the index terms used to describe the articles were then used to develop a full search strategy in PubMed, CINAHL, Embase, and Web of Science databases (see Appendix I). The search strategy, including all identified keywords and index terms, was adapted for each included information source. The abstracts from the American College of Rheumatology and European League Against Rheumatic Diseases meetings were indexed in Embase. Embase search results included the abstracts from these conferences; therefore, the conference abstract database was not searched. The reference lists of all the selected studies for critical appraisal were screened for additional studies. MedNar and OpenGrey were searched for gray literature. The language was limited to English due to the resource limitations of the author team. The potential bias of the language limitation was considered low after a MEDLINE search for non-English publications using the same specifications. All the database searches were updated on December 6, 2021.

Study selection

Following the search, all identified citations were collated and uploaded into EndNote v.X9 (Clarivate Analytics, PA, USA). After removing duplicates, titles and abstracts were screened by 2 independent reviewers (KM and RK) to remove ineligible studies whose contents were not related to RA (such as those focused on juvenile RA, diseases other than RA, and randomized controlled trials). Thereafter, potentially relevant studies were retrieved in full (n = 111), and their citation details were managed using EndNote. Two independent reviewers (KM and RK) assessed the full texts of selected citations against the inclusion criteria in detail. Reasons for excluding full-text studies that did not meet the inclusion criteria are presented in Appendix II. Any disagreements between the reviewers at any stage of the study selection process were resolved through discussion or with a third reviewer. A total of 52 studies were included in the final review. The search results are presented in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram (Figure 1).12

F1
Figure 1:
Search results and study selection and inclusion process12

Assessment of methodological quality

Fifty-two eligible studies were critically appraised by 2 independent reviewers (KM and RK). All the included studies used a cohort study design; therefore, the reviewers used the standardized critical appraisal instrument from JBI for cohort studies.13

Any disagreements were resolved through discussion or with a third reviewer (AK). All the studies appraised were included in the review. Sensitivity analyses were conducted based on the quality of the study to examine the effect of that quality.

Data extraction

Two independent reviewers (KM and RK) used Microsoft Excel (Redmond, Washington, USA) to extract data from papers included in the review (Appendix III). The extracted data included specific details about the populations, study methods, definition of infection, number of infections, person-years of observation, and the other relevant variables to the review questions and specific objectives. AK and HK extracted the site of infection and infection-related mortality data (eg, the number of deaths among patients with severe infections [numerator], the number of patients with severe infection [denominator]). Any disagreements between the reviewers were resolved through discussion or with a third reviewer (KA). Where required, authors of papers were contacted to request missing or additional data. The number of infections and person-years for each study cohort by drug type were entered into an Excel spreadsheet for preliminary analysis.

Data synthesis

The incidence rate was calculated by the number of infections divided by person-years and was expressed per 100 person-years. If only the incidence and number of infections were given, the person-years were calculated by dividing the number of infections by the incidence rate.

Where possible, the studies were pooled using a statistical meta-analysis system, MetaXL v.5.3 (EpiGear International Pty Ltd) IVhet Inverse variance heterogeneity model.14 The IVhet model uses an estimator under the fixed effects model assumption with a quasi-likelihood-based variance structure. It is suited for meta-analysis of studies displaying high heterogeneity, as the random effects model underestimates standard errors and provides a larger mean square error than the fixed effects model. The incidence was expressed as the number of severe/hospitalized infections per 100 person-years with 95% CI around the summary estimate. Heterogeneity was assessed statistically using the standard I2 tests. MetaXL does not provide a statistical test for differences in the incidence among subgroups, and no overlapping of 95% CI between the subgroups is considered statistically significant at P< 0.05.

For secondary outcomes (site of infection and infection-associated mortality), the distribution of infections by organ class (site of infection) was organized by major organ systems, such as the respiratory system. The variation in the proportion of infection in each organ system was examined. The mortality rate was organized by the year of publication, and country and mean age were extracted as a reference.

For the sensitivity analysis, 2 types of heterogeneity need to be considered. One is clinical heterogeneity, which refers to the differences in the distribution of the demographic and clinical risk factors among studies. These risk factors include older age, corticosteroid exposure, previous biologic treatment, comorbidities, and the severity of RA.6,7 Age is one of the typical risk factors for infections, and the subgroup analysis was conducted by grouping cohorts with a mean age ≥65 years as the older age group.

The second type of heterogeneity, methodological heterogeneity, stems from the difference in the study design, data sources, definition of infection, and duration of follow-up among studies. Subgroup analysis by the definition of infection and the type of data source was conducted as an initial step for sensitivity analyses to identify the studies or group of studies that needed to be excluded. It limited TNF-α inhibitors to one group, because the number of studies for each TNF-α inhibitor subtype was too small to examine the multiple risk factors.

Results

Study inclusion

A total of 2631 records were identified during database searches, after which 771 duplicate records were removed. The reviewers then screened 1860 titles and abstracts against the inclusion criteria, which led to the exclusion of 1747 records. Two full-text reports could not be retrieved, but the remaining 111 full-text reports were assessed for eligibility, following which another 59 were excluded (see Appendix II). Twenty-five reports identified via hand-searching of reference lists were assessed for eligibility, but none were included. Thus, in total, 52 studies were included in the systematic review.

Methodological quality

Most of the studies were based on RA registries in Western Europe and the USA and incorporated a historical cohort design. Registry-based studies uniformly applied the American College of Rheumatology criteria for the diagnosis of RA,11 and major rheumatology centers in each country were involved in the diagnosis and clinical data collection. Although the extent of quality control seemingly differed among these studies,15 the diagnosis of RA, infection, and exposure to biologic drugs were considered valid and reliable. Table 1 presents the full results of the methodological quality assessments undertaken in this review.16–67

Table 1 - Critical appraisal of included cohort studies
Citation Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11
Aaltonen, 201516 Y Y Y Y Y Y Y Y Y Y Y
Askling, 200717 Y Y Y Y Y Y Y Y Y Y Y
Carmona, 200718 Y Y Y Y Y Y Y Y Y Y Y
Carrara, 201919 Y Y Y Y Y Y Y Y Y Y Y
Cecconi, 202020 Y Y Y Y Y Y Y Y Y Y Y
Chen, 202021 Y Y Y Y Y Y U Y U Y Y
Chiu, 201422 Y Y Y Y Y Y U Y U Y Y
Curtis, 200723 Y Y Y Y Y Y U Y U Y Y
Curtis, 201324 Y Y Y Y Y Y U Y U Y Y
Dixon, 200625 Y Y Y Y Y Y Y Y Y Y Y
Dominique, 202026 Y Y Y N N Y Y Y Y Y N
Emery, 201427 Y Y Y Y Y Y Y Y Y Y Y
Favalli, 200928 Y Y Y Y Y Y Y Y Y Y Y
Gottenberg, 201029 Y Y Y Y Y Y Y Y Y Y Y
Gron, 201930 Y Y Y Y Y Y Y Y Y Y Y
Hansen, 200431 Y Y Y N N Y Y Y Y Y N
Harrold, 201832 Y Y Y Y Y Y Y Y Y Y Y
Harrold, 202033 Y Y Y Y Y Y Y Y Y Y Y
Henry, 201834 Y Y Y Y Y Y Y Y Y Y Y
Inanc, 200635 Y Y Y N N Y Y Y Y Y N
Isvy, 201236 Y Y Y N N Y Y Y Y Y N
Jani, 201837 Y Y Y Y Y Y Y Y Y Y Y
Jeon, 202138 Y Y Y Y Y Y U Y U Y Y
Jonston, 201339 Y Y Y Y Y Y U Y U Y Y
Komano, 201140 Y Y Y Y Y Y Y Y Y Y Y
Kroesen, 200341 Y Y Y N N Y Y Y Y Y N
Lahaye, 201642 Y Y Y Y Y Y Y Y Y Y Y
Lampropoulos, 201543 Y Y Y Y Y Y Y Y Y Y Y
Lane, 201144 Y Y Y Y Y Y U Y U Y Y
Lang, 201245 Y Y Y Y Y Y Y Y Y Y Y
Listing, 200546 Y Y Y Y Y Y Y Y Y Y Y
Machado, 201847 Y Y Y Y Y Y U Y U Y Y
Montastruc, 201948 Y Y Y Y Y Y U Y U Y Y
Morel, 201749 Y Y Y Y Y Y Y Y Y Y Y
Mori, 201750 Y Y Y Y Y Y Y Y Y Y Y
Neven, 200551 Y Y Y N N Y Y Y Y Y N
Nguyen-Khoa, 201252 Y Y Y Y Y Y U Y U Y Y
Ozen, 201953 Y Y Y Y Y Y Y Y Y Y Y
Pawar, 201854 Y Y Y Y Y Y U Y U Y Y
Ranza, 201955 Y Y Y Y Y Y Y Y Y Y Y
Rutherford, 201856 Y Y Y Y Y Y Y Y Y Y Y
Sakai, 201257 Y Y Y Y Y Y Y Y Y Y Y
Sakai, 201458 Y Y Y Y Y Y Y Y Y Y Y
Salmon, 201659 Y Y Y Y Y Y Y Y Y Y Y
Schenfeld, 201760 Y Y Y Y Y Y U Y U Y Y
Schneeweiss, 200761 Y Y Y Y Y Y U Y U Y Y
Sharma, 201962 Y Y Y Y Y Y Y Y Y Y Y
Silva-Fernandez, 201863 Y Y Y Y Y Y Y Y Y Y Y
Simon, 201964 Y Y Y Y Y Y U Y U Y Y
Tokunaga, 202165 Y Y Y Y Y Y Y Y Y Y Y
van Dartel, 201366 Y Y Y Y Y Y Y Y Y Y Y
Yun, 201667 Y Y Y Y Y Y U Y U Y Y
% 100 100 100 88 88 100 71 100 71 100 88
Y, yes; N, no; U, unclear.
JBI critical appraisal checklist for cohort studies.
Q1. Were the two groups similar and recruited from the same population?
Q2. Were the exposures measured similarly to assign people to both exposed and unexposed groups?
Q3. Was the exposure measured in a valid and reliable way?
Q4. Were confounding factors identified?
Q5. Were strategies to deal with confounding factors stated?
Q6. Were the groups/participants free of the outcome at the start of the study (or at the moment of exposure)?
Q7. Were the outcomes measured in a valid and reliable way?
Q8. Was the follow-up time reported and sufficient to be long enough for outcomes to occur?
Q9. Was follow-up complete, and if not, were the reasons to loss to follow-up described and explored?
Q10. Were strategies to address incomplete follow-up utilized?
Q11. Was appropriate statistical analysis used?

Studies that were based on insurance claims databases pose several problems. First, the diagnosis of RA ascertained from the claims-based diagnosis may not be as accurate as the diagnosis reported in RA registry-based studies. The only exception was in one Italian study, which used multiple linked databases, including the copayment coding information of RA certified by the rheumatologist.19 The critical appraisal tool did not include a query regarding the accuracy of the RA diagnosis, that is, eligibility for inclusion in the study. Second, regarding the diagnosis of infection in the claims database, if the infection is identified from a primary diagnosis, the number of infections may be underestimated (if a patient with an infection is admitted to treat comorbidities, the infection may not be the primary diagnosis). In contrast, if the diagnosis of the infection is identified from any position in the list of “discharge diagnoses, the number of infections may be overestimated if the infection could be treated in an outpatient setting. Thus, Q7, which queried the reliability and validity of outcome measurement” was judged as unclear. Third, follow-up may not have been completed because of changes in employment status or insurance; thus, the follow-up status was assessed as unclear (Q9). However, exposure to biologics is considered accurate in insurance claims–based studies.

Six RA registry-based studies presented their findings as brief case reports of severe infections, with a short description of the cohort,26,31,35,36,41,51 and no adjustment for confounders was described. As the studies were based on a cohort design developed to follow patients diagnosed with RA with periodic censoring, the cohort study format was used for methodological quality assessment. Items related to confounders (Q4 and Q5) were considered as “no.”

Three studies employed a prospective cohort study design and were grouped into either medical record group or registry based on the representative of the patients at the regional or national level. One is a single-center Turkish study,35 and the other is a 3-center study.51 The study by Mori et al. was a large-scale multicenter cohort in the northern Kyushu region in Japan and was grouped into a registry-based study.50

Characteristics of included studies

Information pertaining to the 53 study cohorts that were included in the review are summarized in Table 2 and originated from 21 countries. Data sources included the RA registry, insurance claims, and rheumatology centers, and comprised data for 8 biologic drugs plus DMARDs (a group of nonbiologic drugs for RA). The type of infection was either hospitalized or severe. Hospitalized infection referred to the inpatient treatment of infection (n=18), whereas severe infection was defined as i) hospitalization with infection, ii) administration of outpatient intravenous antibiotic therapy, or iii) death as the clinical outcome (n=34). Earlier studies, mainly European studies, followed the definition of severe infection, whereas US studies mainly employed the definition of hospitalized infection. Fifteen studies reported the number of the first episode of infection. The remaining studies did not specify either the first or all of the episodes of infection in each patient.

Table 2 - Summary of included studies
Item Content
Country USA (n=17), France (n=6), Japan (n=5), UK (n = 5), Brazil (n=2), Denmark (n = 2), Italy (n=2), Argentina, Australia, Bavaria, Finland, Germany, Greece, Netherlands, South America, South Korea, Spain, Sweden, Switzerland, Taiwan, Turkey (1 each)
Data source Registry (n=28), insurance claims database (n=16), medical/rheumatology center (n=8)
Drugs i) TNF-α inhibitors (n=46): adalimumab (n=12), certolizumab (n=4), etanercept (n=14), infliximab (n=13), golimumab (n=3)
ii) abatacept (n=18)
iii) tocilizumab (n=13)
iv) rituximab (n=12)
v) tofacitinib (n=1)
vi) DMARD (n=20)
Definition of infection Hospitalized infection (n=18), severe infection (n=34)
First infections or all infections First infection reported (n=15)
Pathogen Bacterial infection only (n=9), opportunistic infections (n=13)
Infection site Infection site reported (n=34)
DMARD, disease modifying anti-rheumatic drug; TNF-α, tumor necrosis factor-alpha.

Appendix IV outlines the study characteristics. Most of the studies used a historical cohort design and obtained data from the RA registry or insurance claims data. Among the 52 studies, 5 reported 2 cohorts18,24,30,47,55 and 2 studies reported 3 cohorts of RA patients.39,54 Three reports described international collaborative studies that involved multiple countries.26,30,55

Review findings

The sample size ranged from 60 to 141,869. In total, 18,428 infections with 395,065 person-years of biologic drug exposure were included in the analysis. For DMARDs, the number of infections was 5146 with 139,213 persons-years of observation. The mean age of the patients in the included studies was mostly in their mid-50s, and 3 studies included older patients with a mean age of ≥65 years. Some studies reported treatment episodes39,67 because the patient tended to receive a series of biologic drugs when the treatment was ineffective or if they experienced severe adverse reactions. Thus, 1 patient could have multiple episodes within a study. More than two-thirds of the participants in the included studies were female, and the proportion of women ranged from 69% to 93%.

TNF-α inhibitors

The meta-analysis of the incidence of infection was stratified by the type of biologic agent. The first biologic drug class licensed for the treatment of RA was the TNF-α inhibitor, which includes adalimumab, certolizumab, etanercept, infliximab, and golimumab.

The infection rate was available for patients in 39 studies with 48 cohorts who were administered TNF-α inhibitors; however, only 2 studies reported the infection rate for all 5 types of TNF-α inhibitor drugs,17,65 and most of the studies reported information for a couple of TNF-α inhibitor drugs.

Figure 2 is a forest plot of TNF-α inhibitors by age group. The meta-analysis revealed an infection incidence rate of 5.0/100 person-years (95% CI 3.8–6.7) among patients receiving TNF-α inhibitors. The overall incidence rate for younger cohorts (mean age <65 years) was less than half that of older cohorts (4.6/100 person-years [95% CI: 3.5–6.1] and 11.3/100 person-years [95% CI: 5.2–24.7], respectively) with overlapping 95% CI. The heterogeneity for the subgroups and overall meta-analysis was high (≥98%).

F2
Figure 2:
Forest plot of the incidence of severe infections per year among patients with rheumatoid arthritis receiving tumor necrosis factor-α inhibitors, by age group

To display the incomparability of data related to clinical heterogeneity, 4 studies28,33,50,67 were selected based on the incidence rates and data source (Appendix V). Patients in the 2 studies with lower incidence rates19,33 were younger than those in the studies with higher incidence rates.50,67 Comorbidity is difficult to compare owing to the discrepancy in grouping diagnosis and lack of information on the severity of the disease. The study by Mori et al.50 was the only study that provided diagnostic criteria based on laboratory and radiographic test results. The study with the highest incidence showed a high prevalence of risk factors, such as severe comorbidity (eg, chronic obstructive pulmonary disease, heart failure), prior biologic treatment, corticosteroid use, and hospitalized infection.67 In contrast, patients in the study with the lowest incidence19 had a shorter duration of RA and lower prevalence of previous severe infection than the other studies; the study reported bacterial infections only. The other study with low incidence reported a high prevalence of cardiovascular diseases, including a wide array of diagnoses, including hypertension.33 There were no cases of diabetes mellitus, although nearly one-fourth of the patients were morbidly obese (body mass index>35kg/m2).

Subgroup analysis (Figure 3) was conducted for the following 5 TNF-α inhibitors: adalimumab, certolizumab, etanercept, golimumab, and infliximab. The incidence rate ranged from 5.0/100 person-years (95% CI 2.7–9.2) for etanercept to 12.5/100 person-years (95% CI 0.7–215.7) for golimumab. There was a greater than 10-fold difference in the incidence rates within the TNF-α inhibitors. The 95% CI for the 5 subgroups overlapped, and statistical heterogeneity was 98% for the subgroup analysis.

F3
Figure 3:
Subgroup analysis of the incidence of severe infections per year among patients with rheumatoid arthritis according to the type of tumor necrosis factor-α inhibitors

Selective T-cell co-stimulation modulators: abatacept

Thirteen studies with 18 cohorts reported the infection rate for patients with RA receiving abatacept (Figure 4). The overall infection rate for the 18 cohorts was 5.5/100 person-years (95% CI 3.3–9.0), and the rate for younger cohorts was significantly lower than that of older cohorts (4.0/100 person-years and 12.4/100 person-years, respectively). However, the incidence rate for 4 younger cohorts was comparable to or higher than that of the older cohort in Lahaye et al.42 The heterogeneity for the subgroups and all the studies was high (≥96%).

F4
Figure 4:
Forest plot of the incidence of severe infections per year among patients with rheumatoid arthritis receiving abatacept

B-cell-depleting agent: rituximab

Twelve cohorts were identified as using rituximab (Figure 5). The overall infection rate was 8.7/100 PY (95% CI 4.9–15.4), and the heterogeneity was high (≥89%).

F5
Figure 5:
Forest plot of the incidence of severe infections per year among patients with rheumatoid arthritis receiving rituximab

IL-6 receptor antagonists: tocilizumab

Eleven studies reported the infection rate with tocilizumab use in 13 cohorts (Figure 6). The overall incidence rate was 6.5/100 person-years (95% CI 4.7–9.1; range 1.1–17.9/100 person-years). The heterogeneity was high (≥90%).

F6
Figure 6:
Forest plot of the incidence of severe infections per year among patients with rheumatoid arthritis receiving tocilizumab

Janus kinase inhibitor: tofacitinib

As only 1 study45 reported the infection rate for patients with RA treated with tofacitinib, meta-analysis was not conducted. The infection rate was 3.7/100 person-years (95% CI 2.2–5.8).

Conventional DMARDs

The forest plot for DMARDs is shown in Figure 7. If the study reported an infection incidence rate by the specific drug, the drug-specific incidence rate was presented. Methotrexate is the first-line DMARD, and 2 studies19,29 reported the incidence rate for methotrexate. The overall incidence rate for DMARDs was 4.7/100 person-years (95% CI 2.2–9.8; range 0.3–13.3/100 person-years), and the heterogeneity was high (≥94%). The older subgroup had an incidence rate more than 4 times higher than that of the younger subgroup (13.3% and 3.0%, respectively), without overlapping 95% CIs.

F7
Figure 7:
Forest plot of the incidence of severe infections per year among patients with rheumatoid arthritis receiving non-biologic disease-modifying antirheumatic drugs

Summary of infection incidence rates with use of identified agents

In summary, younger cohorts tended to have lower incidence rates than the older cohorts within each drug category. The severe infection rate ranged from 4.0/100 person-years (95% CI 2.8–5.6) for abatacept in younger cohorts to 18.7/100 person-years (95% CI 17.2–20.3) for rituximab in older cohorts among the 8 types of biologic agents. The lowest infection rate was 0.9/100 person-years (95% CI 0.8–1.0) among the low-risk cohort, whereas the highest infection rate was 18.1/100 person-years (95% CI 10.0–32.7) among the cohort with multiple risk factors.

Subgroup analysis of infection incidence based on the definition of infection

Infection rates according to the definition of infection were examined in cohorts treated with a TNF-α inhibitor, as this number of cohorts was the largest. Older cohorts were excluded to reduce clinical heterogeneity. A subgroup analysis was subsequently conducted to examine the differences in the infection incidence based on the definition of infection. The categories were as follows: first hospitalized infection, hospitalized infection, first severe infection, and severe infection. The 95% CI of the 4 infection subgroups overlapped (Figure 8), and the heterogeneity was high. I2 exceeded 90% for the 4 subgroups. In the severe infection subgroup, 2 studies with person-years less than 100 had high incidence rates.31,41 The total person-years was 150,687, and eliminating these small-scale studies had little impact on the overall incidence rate.

F8
Figure 8:
Subgroup analysis of severe infection incidence in patients with rheumatoid arthritis taking biologic agents, by the definition of infection (first hospitalized infection, hospitalized infection, first severe infection, and severe infection)

Subgroup analysis of infection incidence by data source

Infection rates were aggregated into 3 types of databases: insurance claims, RA registries, and medical records (Figure 9). At the subgroup level, 95% CIs of the 3 subgroups mostly overlapped; however, the infection rates for the insurance claims–based and medical records–based studies contained outliers. One of the studies with an outlier predominantly described a case of severe infections at a university hospital in Switzerland,41 and only the mean age and person-years of exposure during the study period were reported. The other study by Yun et al.67 comprised patients with multiple clinical and demographic risk factors of infection as described in TNF-α inhibitors.

F9
Figure 9:
Subgroup analysis of severe infection incidence in patients with rheumatoid arthritis taking biologic agents, by data source (insurance-claims, rheumatoid arthritis registries, and medical records)

Secondary outcomes

Site of infection. Fifteen studies reported the site of infection (Table 3), most of which included respiratory, skin/soft tissue, urinary tract, and sepsis/bacteremia. Several studies included postoperative infection,21,30,40,42 device-associated infection,44 dental,63 and endocarditis/pericarditis.17 Opportunistic infections accounted for 20% to 30% of all severe infections,28,52,57 and comprised Pneumocystis jirovecii-induced pneumonia, systemic fungal infections, cytomegalovirus, pneumocystosis, candidiasis, viral hepatitis, and herpes zoster. Two studies showed that viruses accounted for 20% of the pathogens that were identified.28,49 The site of infection based on the type of biologic agent was not examined owing to the limited number of studies reporting the site of infection and the variability in the site of infections reported.

Table 3 - Number of studies reporting the site of infection and range of the proportion of infections in patients with rheumatoid arthritis taking biologic agents
Site of infection No. of studies* Range
Respiratory 15 30%-50%
Skin/soft tissue 15 10%-30%
Urinary tract 14 5%-20%
Sepsis/bacteremia 12 2%-25%
Bone/joint 12 1%-18%
Gastrointestinal 8 1%-10%
Eye/ear/nose 4 1%-6%
Implant/surgical site 3 6%-10%
Central nervous system 2 1%-5%
* A total of 15 studies reported the site of infection. This is the number of studies (out of 15 studies) that reported the proportion of the infection due to the specific site.

Infection-associated mortality. Ten studies reported infection-related mortality rates (Table 4). Two studies specified deaths that occurred less than 30 days after hospitalization or following infection,56,67 and the remaining 8 did not mention the time frame for the deaths. The denominators were either the number of patients with severe infections or the number of severe infections. Both denominators are acceptable because patients may have more than 1 infection during the follow-up period.

Table 4 - Mortality rates among patients with rheumatoid arthritis taking biologic agents who had severe infections
Author Country Mortality rate Mean age (years)
Carmona,18 2007a Spain EMECAR 22.2% (14 deaths/63 infections)
BIOBADASER 6.1% (7 deaths/114 infections)		 61±13
Favalli,28 2009 Italy 5.5% (4 deaths/73 patients) 55.8±13.0
Gottenberg,29 2010 France 5.1% (4 deaths/78 patients) 57.7±12.7
Listing,46 2005 Germany 6.1% (4 deaths/66 infections) Etanercept, 53.7±12.6
Morel,49 2017 France 2.5% (3 deaths/122 patients) 56.6±13.6
Mori,50 2017 Japan 8.1% (7 deaths/86 infections) 60.9±14.2
Ranza,55 2019 Argentina, Brazil 7.9% (15 deaths/191 patients) 52.4±13.1
Rutherford,56 2018b UK All infections 10.4% (95% CI 9.2-11.6); sepsis/bacteremia 45% (95% CI 33-61); skin 2% (95% CI 1-3) 56±12
Sakai,57 2012 Japan 3.7% (3 deaths/82 infections) TNF-α inhibitors All TNF-α inhibitors, 56.3±13.4
Yun,67 2016c USA Range: golimumab 4.0% to certolizumab 7.8%
No significant differences in mortality among biologic agents.		 Range: golimumab, 60.4 (13.5) to abatacept, 66.8 (12.1)
BIOBADASER, registry for active long-term follow-up of the safety of biologic agents in patients with rheumatoid arthritis; EMECAR, external cohort of patients with rheumatoid arthritis; TNF-α, tumor necrosis factor-alpha.
aThe number of severe infections (denominators) in Table 2 of Carmona et al.18, and the number of deaths on p. 882.
b<30 day mortality following infection.
c<30 days after hospitalization.

Most of the studies had mortality rates less than 10%, and in two cohorts, mortality rates exceeded 10%. Furthermore, the mortality rates substantially differed by organ site. Sepsis/bacteremia was associated with the highest mortality rate of 45%, whereas skin infections were associated with the lowest mortality rate of 2%.56

Discussion

A meta-analysis was conducted to estimate the infection rate in observational studies by the type of biologic drug used to treat RA. Among the TNF-α inhibitor agents, the infection rate was 5.0/100 person-years (95% CI 3.9–6.7), and there was an 18-fold difference between the lowest and highest infection rates. The wide variation in infection rates in observational studies is mainly due to clinical and methodological heterogeneity. Thus, the results should be interpreted as the range of infection rates in the real-world setting rather than as a summary estimate of the infection rate.

Annual updates from systematic reviews on the safety of biologic agents in observational studies have reported relative risks of severe infection when using DMARDs or one of the biologic DMARDs as a reference.1 The relative risks of most infections were not significantly high. Nonetheless, the probability of severe infection differs substantially among the included studies. The differences in the definition of the infection and the distribution of risk factors of serious infections among the included studies are likely to account for the variability in infection rates.

With regards to methodological heterogeneity, the differences in the type of pathogens and site of infection among the included studies have undoubtedly contributed to the variation in the infection rates. The exclusion of viral infection may have resulted in a lower infection rate of 20% because viral infections accounted for approximately 20% of the severe infections in the included studies.28,52,57 The differences in the reported site of infection among studies partially explained the discrepancy in the infection rates. For example, 8 of the 15 studies reported gastrointestinal infections, which is relatively common. The absence of these severe infections in the study indicates that these infections did not occur during the study period, or it was not a part of the site of infection that was to be monitored.

Among the studies that evaluated opportunistic infections, the type of pathogen and site of infection that were related to opportunistic infections varied. The US insurance claims–based study reported that opportunistic infections accounted for 24.8% of serious infections.52 The RA registry–based study in Italy found that viruses, mycobacteria, and fungi accounted for 30% of the infections with the identified pathogens.28 Thus, excluding opportunistic infections may lead to an underestimation of severe infections by as much as 30%.

The definition of the infection is the other potential source of methodological heterogeneity, and the severe infection subgroup had a higher incidence than the hospital infection subgroup. Severe infections included outpatient intravenous treatment, in addition to hospitalized infections. An overall trend for shortening length of stay is observed, and the need for outpatient intravenous treatment for severe infection may increase and needs to be monitored.

The major risk factors of severe infections are higher age, presence and number of comorbidities,53 severity of RA,27 increase in glucocorticoid dose,28,60 history of biologic DMARD treatment,50,53 and history of severe infection.19,21 These risk factors could not be used for further analysis because the number of studies reporting the prevalence of risk factors was limited. The formats used to present data differed, such as the mean age versus age category. With regards to comorbidity diagnosis, the discrepancy in diagnostic grouping poses a problem when comparing risk factors. Chronic obstructive pulmonary disease was most frequently diagnosed in our included studies; however, some studies used a broader diagnostic category, such as pulmonary disease.21,32 In some studies, the Charlson Comorbidity Index, a summary measure of comorbidity, was used as opposed to a specific diagnosis of comorbidity.68 The Charlson Comorbidity Index calculates the number of selected comorbidities and the severity of the comorbidity, and is mainly used to predict patient mortality. However, the validity of the Charlson Comorbidity Index has not been thoroughly tested to predict severe infection in patients treated with biologic agents. Standardizing reporting risk factors is essential for further research for the individualized prediction of the risk of infection in patients with RA.

The incidence of severe infections was higher in the first year of therapy than in the subsequent years.33,52,57,66 This phenomenon indicates that patients who receive biologic treatment and experience improved RA-related outcomes, and remain free of adverse outcomes in the first year, are at low risk of developing severe infections in the subsequent year. A longer follow-up cohort without switching the biologic agent is likely to result in lower infection rates than their shorter follow-up counterparts. Nevertheless, the 10-year follow-up study in Australia reported that 1 patient experienced 8 episodes of severe infection.62 The distribution of the number of infections in more extended follow-up studies is essential for understanding the spectrum of adverse outcomes.

Very low infection rates19 or very high infection rates31,62,67 have been observed in studies that are based on insurance claims data or medical records.62 It is possible that infections are undercounted or overcounted because of the method used to identify the infection. The lowest infection rate among the 8 drugs reported by Carrara et al.19 was based on the first bacterial infection in the patient population with a lower risk profile. Insurance claims–based studies are more likely to overcount the number of severe infections because inpatients treated for infection may have been admitted for reasons other than infection. Medical records–based studies tend to have a smaller sample, and high infection rates could be a chance factor. Conversely, compared with registry-based studies, medical records–based studies may have a higher sensitivity for detecting infections. The quality of the RA registry–based studies is considered to be high in terms of diagnosis of RA and standardized data collection within the registry, although a survey of the RA registries in 16 European countries revealed differences in the content of data, frequency of data collection, and quality assurance.15

Estimating the mortality rate among patients with severe infection is difficult, as the rate depends on the site of infection. Only 1 study reported the site-specific mortality rate.56 Further large-scale studies are needed to report the infection site–specific mortality rates for comparative analysis.

Study limitations

The clinical, methodological, and statistical heterogeneities were high. Considering the methodological heterogeneity, the type of pathogen and infection site varied among the included studies, and the impact on the inclusion or exclusion of specific pathogens or infection sites could not be examined. Considering the data sources, the Medicare (US government insurance for older people) study by Yun et al.67 had an incidence rate twice as high as that in any other study. Medicare coverage differs among states and changes over time.69 During their study period, one of the Medicare plans had limited outpatient service and medication coverage, and 40% to 60% among 5 types of TNF-α inhibitor cohorts were Medicaid eligible (US government insurance for low-income persons).69 The high prevalence of multiple risk factors and low insurance coverage may have contributed to the high incidence rate. Nevertheless, examining the effect of insurance coverage on the infection rates using the insurance claims data is challenging.

Owing to the high statistical heterogeneity for the total and subgroups, the candidates for exclusion for further sensitivity analysis were not found. We could not perform network meta-analysis to control the clinical and methodological heterogeneity owing to the inconsistency in the definition of the clinical variables and reporting format.

Considering the exclusion of non-English literature on MEDLINE, there were 52 records for non-English publications. Of the 52 records, 21 were reviews, 15 focused on autoimmune diseases other than RA or juvenile RA, 6 involved diagnosis of infection and screening for tuberculosis in patients with RA receiving biologics, and 4 were ineligible study designs. Two papers (in Japanese and German) were potentially eligible; both of them belonged to the RA registry in their respective country and reported an infection incidence at their center. The number of studies in non-English literature by countries were 9 for China, 3 for Russia, and 2 for Czech Republic. In China, the RA registry was established in November 2016.70 A PubMed search on this registry did not find any publications on our review topic. Access to biologic treatment for RA is limited in central and eastern European countries, which excluded the Czech Republic.71 A Google Scholar search failed to identify any literature on the use of biologic agents in Russia; thus, the English-language limitation in our review is unlikely to result in a bias in our study findings.

Conclusions

Fifty-two studies were examined to determine the incidence of severe infection rates among the 8 biologic agents. The minimal and maximal infection rates differed by approximately 10-fold for most biologic agents, and statistical heterogeneity was very high due to both clinical and methodological heterogeneity. The primary factor associated with clinical heterogeneity was the difference among the studies with regards to the distribution of the risk factors for severe infection. However, for the methodological heterogeneity, the differences in the data source and the definition of severe infection seemed to have contributed to the disparity in infection rates.

Implications for research and practice

The treat-to-target approach is globally regarded as a guiding principle in the treatment of RA. It requires a shared decision goal for treatment, periodic progress assessment, and assessment of the need for change in treatment.72 Patients need to understand the benefit of the treatment, risk of adverse outcomes, and treatment cost for the shared decision goal. Currently, the risk of adverse outcomes is mostly expressed as relative risks.4 Owing to the variability in the incidence of severe infections among studies, relative risks may not be relevant to patients and clinicians. Reporting the incidence rate in epidemiologic studies and meta-analyses is preferred for communicating the risk of adverse outcomes between clinicians and patients.

Pooling individual patient data on severe infections is desirable to control extraneous variables. Pooling of data was performed in a study on biologic drug (abatacept) retention, and the drug retention rates by controlling for patient demographics and gross domestic product in European RA registries were reported.73 For benchmarking infection rates, defining the severe infection is essential. The Delphi method can be used to reach a consensus on the type of pathogen, site of infection, and classification of comorbidities to be included. Each RA registry can add additional information based on its regional priority list. We excluded 34 studies on opportunistic infections in patients treated with biologic agents, and of these, 19 studies did not report the severity of infection, as shown in Appendix II. Reported bacterial infections in patients receiving biologic drugs were exclusively severe infections, while studies focusing on opportunistic infections rarely reported the severity of infection. The severity of infection indicates the burden of disease, including medical care costs, and needs to be documented.

The proportion of patients who continued biologic treatment differed depending on the type of biologic drugs.74,75 For etanercept, it was approximately 80% at 1 year, down to 50% at 5 years.74 Studies on the discontinuation of biologic drugs reported that inadequate response or loss of efficacy was the major reason76,77; adverse outcomes, patient preference, or physicians’ preference are also commonly reported. Cross-study comparison of the reason for discontinuation is not possible due to differences in wording and allowing for multiple responses, in addition to the reason being unknown. Future studies should describe a biologic treatment trajectory for patients with RA to facilitate shared decision-making.

Biosimilars, alternatives to biologics, are similar to the referenced biologic product in terms of quality, safety, and efficacy, and are expected to increase patient access to biologic agents.78 Pathogens associated with opportunistic infections in patients treated with specific biologic agents have been documented,79–81 such as vaccination against influenza virus, pneumococcus, and herpes zoster, which are considered for high-risk patients.79,81 Educating patients is essential for close monitoring of early signs of infection and seeking prompt medical treatment, as the clinical manifestation of symptoms can be an early sign of sepsis.41,79

Funding

This review was funded by the Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research no. 17K12440. The funding agency played no role in the conduct of this review.

Author contributions

All the authors contributed to the conceptual framework of this review. RK conducted the literature search. RK and KM conducted the quality appraisal of the selected studies, and KH and AK consulted regarding the content of the data and worked with the manuscript. SS conducted meta-analysis. All the authors contributed to the draft this review and approved the final version of this review.

Appendix I: Search strategy

PubMed

Date searched: December 6, 2021

-
Search Query Records retrieved
#1 “arthritis”[MeSH Terms] OR “arthritis”[All Fields] OR “arthritides”[All Fields] OR “polyarthritides”[All Fields] 337,133
#2 “adjuvant arthritis”[All Fields] 1995
#3 “rheumatoid nodule”[All Fields] 1052
#4 or/1-3 337,175
#5 (((“antibodies, monoclonal”[MeSH Terms] OR (“antibodies”[All Fields] AND “monoclonal”[All Fields])) OR “monoclonal antibodies”[All Fields]) OR (“antibodies”[All Fields] AND “monoclonal”[All Fields])) OR “antibodies monoclonal”[All Fields] 290,348
#6 “monokines”[MeSH Terms] OR “monokines”[All Fields] OR “monokine”[All Fields] 163,134
#7 ((“receptors, interleukin-1”[MeSH Terms] OR (“receptors”[All Fields] AND “interleukin 1”[All Fields])) OR “interleukin-1 receptors”[All Fields]) OR “receptors interleukin 1”[All Fields] 13,769
#8 ((“receptors, interleukin-6”[MeSH Terms] OR (“receptors”[All Fields] AND “interleukin 6”[All Fields])) OR “interleukin-6 receptors”[All Fields]) OR “receptors interleukin 6”[All Fields] 15,174
#9 “immunoglobulin g”[MeSH Terms] OR “immunoglobulin g”[All Fields] 154,470
#10 “immunoconjugated”[All Fields] OR “immunoconjugates”[MeSH Terms] OR “immunoconjugates”[All Fields] OR “immunoconjugate”[All Fields] 12,032
#11 (“polyethylene glycols”[MeSH Terms] OR (“polyethylene”[All Fields] AND “glycols”[All Fields])) OR “polyethylene glycols”[All Fields] 71,341
#12 (“immunoglobulin fab fragments”[MeSH Terms] OR ((“immunoglobulin”[All Fields] AND “fab”[All Fields]) AND “fragments”[All Fields])) OR “immunoglobulin fab fragments”[All Fields] 28,847
#13 “t-lymphocytes”[MeSH Terms] OR “t lymphocytes”[All Fields] OR “t lymphocytes”[All Fields] 349,351
#14 “infliximab”[MeSH Terms] OR “infliximab”[All Fields] OR “infliximab s”[All Fields] 14,944
#15 “remicade”[All Fields] 387
#16 “adalimumab”[MeSH Terms] OR “adalimumab”[All Fields] 8501
#17 “humira”[All Fields] 271
#18 “trudexa”[All Fields] 1
#19 “abatacept”[MeSH Terms] OR “abatacept”[All Fields] 3733
#20 “orencia”[All Fields] 63
#21 “anakinra”[All Fields] 1789
#22 “kineret”[All Fields] 90
#23 ((“certolizumab pegol”[MeSH Terms] OR (“certolizumab”[All Fields] AND “pegol”[All Fields])) OR “certolizumab pegol”[All Fields]) OR “certolizumab”[All Fields] 1293
#24 “cimzia”[All Fields] 49
#25 “etanercept”[MeSH Terms] OR “etanercept”[All Fields] 8597
#26 “enbrel”[All Fields] 328
#27 “golimumab”[Supplementary Concept] OR “golimumab”[All Fields] OR “golimumab s”[All Fields] 1251
#28 “simponi”[All Fields] 1253
#29 “rituximab”[MeSH Terms] OR “rituximab”[All Fields] OR “rituximab s”[All Fields] 23,846
#30 “rituxan”[All Fields] 306
#31 “mabthera”[All Fields] 187
#32 “tocilizumab”[Supplementary Concept] OR “tocilizumab”[All Fields] 3442
#33 “actemra”[All Fields] 50
#34 “roactemra”[All Fields] 20
#35 or / 5-34 979,852
#36 “severe infection”[All Fields] 3942
#37 “infection”[All Fields] 1,228,576
#38 “tuberculosi”[All Fields] OR “tuberculosis”[MeSH Terms] OR “tuberculosis”[All Fields] OR “tuberculoses”[All Fields] OR “tuberculosis s”[All Fields] 261,054
#39 (((((“influenza s”[All Fields] OR “influenza, human”[MeSH Terms]) OR (“influenza”[All Fields] AND “human”[All Fields])) OR “human influenza”[All Fields]) OR “influenza”[All Fields]) OR “influenzae”[All Fields]) OR “influenzas”[All Fields] 134,555
#40 “respiratory tract infection”[All Fields] 9625
#41 “pneumonia”[MeSH Terms] OR “pneumonia”[All Fields] OR “pneumoniae”[All Fields] OR “pneumonias”[All Fields] OR “pneumoniae s”[All Fields] 228,580
#42 ((“mycobacterium infections”[MeSH Terms] OR (“mycobacterium”[All Fields] AND “infections”[All Fields])) OR “mycobacterium infections”[All Fields]) OR “mycobacteriosis”[All Fields] 233,711
#43 “Pneumocystis jiroveci pneumonia”[All Fields] 441
#44 “mycobacterium avium complex”[All Fields] 4270
#45 “atypical pneumonia”[All Fields] 1366
#46 ((((((((((“infection”) OR (“atypical pneumonia”)) OR (“atypical pneumonia”)) OR (“mycobacterium avium complex”)) OR (“Pneumocystis jiroveci pneumonia”)) OR (Mycobacteriosis)) OR (pneumonia)) OR ((“respiratory tract infection”))) OR (influenza)) OR (tuberculosis)) OR (“severe infection”) 1,717,631
#47 or/36-46 Limit 1999 to present 4100
#48 “cohort design”[All Fields] OR “cohort stud*“[All Fields] 402,175
#49 “registries”[MeSH Terms] OR “registries”[All Fields] OR “registry”[All Fields] OR “registry s”[All Fields] 183,002
#50 (((“longitudinal studies”[MeSH Terms] OR (“longitudinal”[All Fields] AND “studies”[All Fields])) OR “longitudinal studies”[All Fields]) OR “prospective”[All Fields]) OR “prospectively”[All Fields] 1,042,673
#51 “follow up”[All Fields] 1,330,455
#52 “incidence”[All Fields] OR “incidence”[MeSH Terms] OR “incidences”[All Fields] OR “incident”[All Fields] OR “incidents”[All Fields] 944,818
#53 or / 48-52 2,430,473
#54 # 47 AND #53 1084
#55 #54 Limit to English language 1032

CINAHL (EBSCO)

Date searched: December 6, 2021

-
Search number Query Records retrieved
S1 (MH “Arthritis+“) OR “arthritis” OR arthritides OR polyarthritides OR “rheumatoid nodule” 110,167
S2 (MH “Infection+“) OR ( infections OR infect OR infectability OR infectable OR infectant OR infectants OR infected OR infecteds OR infectibility OR infectible OR infecting OR infection s OR infections OR infections OR infection OR infective OR infectiveness OR infectives OR infectivities OR infects OR pathogenicity OR pathogenicity OR infectivity OR tuberculos* OR pneumoni* OR influenza OR mycobacteriosis OR mycobacterium avium complex ) 536,561
S3 (MH “Monokines+“) OR monokine* 22,514
S4 (antibodies AND monoclonal) OR ”monoclonal antibodies” OR ”antibodies monoclonal” 34,717
S5 “receptors interleukin 1” OR “interleukin-1 receptors” OR ( receptors AND “interleukin 1” ) OR ( receptors AND “interleukin 6” ) OR “receptors interleukin 6” OR “interleukin-6 receptors” 1741
S6 “immunoglobulin g” OR immunoconjugate* 2387
S7 (MH “Polyethylene Glycols”) OR ( polyethylene AND glycols ) OR “polyethylene glycols” 3108
S8 ( immunoglobulin AND fab AND fragment* ) OR “immunoglobulin fab fragment*“ 161
S9 MH “T Lymphocytes” OR “t-lymphocyte*“ 20,646
S10 roactemra OR OR tocilizumab OR mabthera OR rituxan OR rituximab OR simponi OR golimumab OR enbrel OR etanercept OR cimzia OR kineret OR anakinra OR orencia OR abatacept OR trudexa OR humira OR remicade OR infliximab OR tocilizumab OR “certolizumab AND pegol” OR “certolizumab pegol” OR certolizumab OR abatacept OR adalimumab OR actemra 15,751
S11 S3 OR S4 OR S5 OR S6 OR S7 OR S8 OR S9 OR S10 88,444
S12 “cohort design” OR “cohort stud*” OR registries OR registry 169,690
S13 S1 AND S2 AND S11 AND S12 from 1999 – 2021 / English 102

Embase

Date searched: December 6, 2021

-
Search Number Query Results retrieved
#1 arthritides OR ‘arthritis’/exp OR ‘arthritis’ OR ‘polyarthritis’/exp OR ‘polyarthritis’ OR ‘rheumatoid nodule’/exp OR ‘rheumatoid nodule’ 619,839
#2 ‘tocilizumab’/de OR ‘tocilizumab’ OR roactemra OR actemra OR mabthera OR ‘rituximab’/de OR ‘rituximab’ OR ‘golimumab’/de OR ‘golimumab’ OR simponi OR ‘etanercept’/de OR ‘etanercept’ OR enbrel OR ‘certolizumab pegol’/de OR ‘certolizumab pegol’ OR cimzia OR ‘anakinra’/de OR ‘anakinra’ OR kineret OR orencia OR trudexa OR ‘adalimumab’/de OR ‘adalimumab’ OR humira OR ‘infliximab’/de OR ‘infliximab’ OR remicade OR ‘certolizumab’/de OR ‘certolizumab’ OR ‘abatacept’/de OR ‘abatacept' 192,363
#3 antibod* OR monoclonal OR (monoclonal AND antibod*) OR monokine* OR ‘monokine’/de OR ‘monokine’ OR ‘monoclonal antibody’/de OR ‘monoclonal antibody’ OR ‘antibody’/de OR ‘antibody’ OR ‘interleukin 1 receptor type i’/de OR ‘interleukin 1 receptor type i’ OR ‘interleukin 1 receptor type ii’/de OR ‘interleukin 1 receptor type ii’ OR ‘interleukin 1 receptor*‘ OR (receptor* AND ‘interleukin 1’) OR ‘interleukin 6 receptor’/de OR ‘interleukin 6 receptor’ OR (receptor AND ‘interleukin 6’) OR ‘immunoglobulin g’/de OR ‘immunoglobulin g’ OR ‘antibody conjugate’/de OR ‘antibody conjugate’ OR immunoconjugat* OR ‘macrogol derivative’/de OR ‘macrogol derivative’ OR ((‘polyethylene’/de OR ‘polyethylene’) AND (‘glycol’/de OR ‘glycol’)) OR ‘immunoglobulin f(ab) fragment’/de OR ‘immunoglobulin f(ab) fragment’ OR (immunoglobulin AND fab AND fragments) OR ‘t lymphocyte’/de OR ‘t lymphocyte' 2,285,616
#4 #2 OR #3 2,396,048
#5 ‘infection’/de OR ‘infection’ OR infect* OR infectable OR infectant* OR infected OR infecting OR infectability OR infectible OR ‘pathogenicity’/de OR ‘pathogenicity’ OR infectiv* OR infectivity OR infective* OR ‘tuberculosis’/de OR ‘tuberculosis’ OR ‘pneumonia’/de OR ‘pneumonia’ OR ‘influenza’/de OR ‘influenza’ OR ‘mycobacteriosis’/de OR ‘mycobacteriosis’ OR ‘mycobacterium avium complex lung disease’/de OR ‘mycobacterium avium complex lung disease’ OR ‘mycobacterium avium complex’ OR ‘severe infection’ OR ‘respiratory tract infection’/de OR ‘respiratory tract infection’ 4,095,761
#6 registries OR registry OR ‘cohort analysis’/de OR ‘cohort analysis’ OR ‘cohort stud*‘ OR ‘case control design’ OR ‘case control stud*‘ 1,307,174
#7 #1 AND #4 AND #5 AND #6 2860
#8 #7 AND (1999:py OR 2000:py OR 2001:py OR 2002:py OR 2003:py OR 2004:py OR 2005:py OR 2006:py OR 2007:py OR 2008:py OR 2009:py OR 2010:py OR 2011:py OR 2012:py OR 2013:py OR 2014:py OR 2015:py OR 2016:py OR 2017:py OR 2018:py OR 2019:py OR 2020:py OR 2021:py) 2856
#9 #7 AND (1999:py OR 2000:py OR 2001:py OR 2002:py OR 2003:py OR 2004:py OR 2005:py OR 2006:py OR 2007:py OR 2008:py OR 2009:py OR 2010:py OR 2011:py OR 2012:py OR 2013:py OR 2014:py OR 2015:py OR 2016:py OR 2017:py OR 2018:py OR 2019:py OR 2020:py OR 2021:py) AND [embase]/lim NOT ([embase]/lim AND [medline]/lim) 1328

Web of Science

Date searched: December 6, 2021

-
Query Records retrieved
(TS=(arthritis OR arthritides OR polyarthritides OR “rheumatoid nodule”) AND ALL=(infections OR infect OR infectability OR infectable OR infectant OR infectants OR infected OR infecteds OR infectibility OR infectible OR infecting OR infection s OR infections OR infections OR infection OR infective OR infectiveness OR infectives OR infectivities OR infects OR pathogenicity OR pathogenicity OR infectivity OR tuberculos* OR pneumoni* OR influenza OR mycobacteriosis OR “mycobacterium avium complex”) AND ALL=(“antibodies, monoclona” OR “monoclonal antibodies” OR “antibodies monoclonal” OR monokine OR monokines OR “receptors interleukin 1” OR “interleukin-1 receptors” OR “receptors AND interleukin 1” OR “receptors interleukin 6” OR “interleukin-6 receptors” OR “receptors AND interleukin 6” OR “immunoglobulin g” OR immunoconjugate* OR “polyethylene AND glycols” OR “polyethylene glycols” OR “immunoglobulin AND fab AND fragment*” OR “immunoglobulin fab fragment*” OR “t-lymphocyte*“ OR “t lymphocytes” OR roactemra OR actemra OR tocilizumab OR mabthera OR rituxan OR rituximab OR simponi OR golimumab OR enbrel OR etanercept OR cimzia OR kineret OR anakinra OR orencia OR abatacept OR trudexa OR humira OR remicade OR infliximab OR tocilizumab OR “certolizumab pegol” OR certolizumab OR abatacept OR adalimumab)) AND ALL=(“cohort design” OR “cohort stud*” OR “registries OR registry”) Limit to English (data were available from 2002) 169

MedNar

Date searched: December 6, 2021

keywords: rheumatoid arthritis, biologic, severe infection

Results 298

OpenGrey

Date searched: December 6, 2021

keywords: rheumatoid arthritis, biologic, severe infection

Results 13

Appendix II: Studies ineligible following full-text review

  1. Accortt NA, Bonafede MM, Collier DH, Iles J, Curtis JR. Risk of subsequent infection among patients receiving tumor necrosis factor inhibitors and other disease-modifying antirheumatic drugs. Arthritis Rheumatol. 2016;68(1):67-76.
    2. Reason for exclusion: Non–rheumatoid arthritis (RA) patients were included.
  2. Aggarwal R, Manadan AM, Poliyedath A, Sequeira W, Block JA. Safety of etanercept in patients at high risk for mycobacterial tuberculosis infections. J Rheumatol. 2009;36(5):914-7.
    3. Reason for exclusion: Non-severe infections were included and were not stratefied by severity.
  3. An Y, Liu T, He D, Wu L, Li J, Liu Y, et al. The usage of biological DMARDs and clinical remission of rheumatoid arthritis in China: a real-world large scale study. Clin Rheumatol. 2017;36(1):35-43.
    4. Reason for exclusion: Infection rates were not reported.
  4. Askling J, C Fored CM, Brandt L, Baecklund E, Bertilsson L, Cöster L, et al. Risk and case characteristics of tuberculosis in rheumatoid arthritis associated with tumor necrosis factor antagonists in Sweden. Arthritis Rheum. 2005;52(7):1986-92.
    5. Reason for exclusion: Treatment for latent tuberculosis was reported.
  5. Baddley JW, Winthrop KL, Chen L, Liu L, Grijalva G, Delzell E, et al. Non-viral opportunistic infections in new users of tumor necrosis factor inhibitor therapy: results of the SAfety Assessment of Biologic ThERapy (SABER) study. Ann Rheum Dis. 2014;73(11):1942–48.
    6. Reason for exclusion: Severity of opportunistic infection was not reported by type of autoimmune diseases. The study included inflammatory bowel disease, psoriasis, psoriatic arthritis, and ankylosing spondylitis.
  6. Bauer H, Luxembourger C, Gottenberg JE, Fournier S, Abravanel F, Cantagrel A, et al. Outcome of hepatitis E virus infection in patients with inflammatory arthritides treated with immunosuppressants. Medicine (Baltimore). 2015;94(14):e675.
    7. Reason for exclusion: Non-severe infections were included and were not stratefied by severity.
  7. Bello S, Serafino L, Bonali C, Terlizzi N, Fanizza C, Lapaldula G. Incidence of influenza-like illness into a cohort of patients affected by chronic inflammatory rheumatism and treated with biological agents. Reumatismo. 2012;64 (5):299-306.
    8. Reason for exclusion: Severity of the infection was not reported.
  8. Bergstrom L, Yocum DE, Ampel NM, Villanueva I, Lisse J, Gluck O, et al. Increased risk of coccidioidomycosis in patients treated with tumor necrosis factor alpha antagonists. Arthritis Rheum. 2004;50(6):1959-66.
    9. Reason for exclusion: Non-RA patients were included.
  9. Borekci S, Atahan E, Demir Yilmaz D, Mazican N, Duman B, et al. Factors affecting the tuberculosis risk in patients receiving anti-tumor necrosis factor-α treatment. Respiration. 2015;90(3):191-8.
    10. Reason for exclusion: No infection rates by person-years; only provided person-years of observation for patients with infection.
  10. Boussaid S, Ben Aissa R, Kochbati S, Elleuch M, Abdelmoula L, Laatar A, et al. Infectious risk during biologic therapy for inflammatory rheumatic diseases: data from the Tunisian BINAR registry. Ann Rheum Dis. 2021;80(Suppl 1):856.
    11. Reason for exclusion: Infection rates were not stratified by type of autoimmune diseases.
  11. Brassard P, Kezouh A, Suissa S. Antirheumatic drugs and the risk of tuberculosis. Clin Infect Dis. 2006:43:717-22.
    12. Reason for exclusion: Severity of the infection was not reported.
  12. Brocq O, Roux CH, Albert C,Breuil V, Aknouche N, Ruitford S, et al. TNF alpha antagonist continuation rates in 442 patients with inflammatory joint disease. Joint Bone Spine. 2007;74(2):148-54.
    13. Reason for exclusion: Person-years data were not reported.
  13. Chan M-J, Wen Y-H, Huang Y-B, Chuang Y-L, Wang Y-CL, Hsy C-N. Risk of tuberculosis comparison in new users of antitumour necrosis factor-α and with existing disease-modifying antirheumatic drug therapy. J Clin Pharm Ther. 2018;43:256–64.
    14. Reason for exclusion: Patients with psoriatic arthritis were included, and patients with rheumatoid arthritis were not stratified.
  14. Chandrashekara S, Shobha V, Rao V, Desai A, Jois R, Dharmanand BG, et al. Incidence of infection other than tuberculosis in patients with autoimmune rheumatic diseases treated with bDMARDs: a real time clinical experience from India. Rheumatology Int. 2019;39:497–507.
    15. Reason for exclusion: RA-specific infection rates were not reported.
  15. Chen DY, Shen GH, Chen YM, Chen HH, Hsieh CW, Lan JL. Biphasic emergence of active tuberculosis in RA patients receiving TNF alpha inhibitors. Ann Rheum Dis. 2012;71(2):231-7.
    16. Reason for exclusion: No person years of exposure, no severity of infection.
  16. Chen Y-H, Chen W-S, Tsai W-C, Hu J-C, Chen S-C, Mardekian J, et al. Real-world use of tofacitinib compared with tumor necrosis factor inhibitors in a cohort of 211 patients with rheumatoid arthritis: data from a drug-based registry study in Taiwan. Int J Rheumatic Dis. 2020;23(SUPPL 1):347.
    17. Reason for exclusion: Infection rates were not reported.
  17. Chen Y-M, Chen H-H, Huang W-N, Chen Y-S, Hsieh T-Y, Yang S-S, et al. Reactivation of hepatitis B virus infection following rituximab treatment in HBsAg‐negative, HBcAb‐positive rheumatoid arthritis patients: a long‐term, real‐world observation. Int J Rheum Dis. 2019;00:1–7.
    18. Reason for exclusion: Severity of infections (hepatitis B virus) was not reported.
  18. Cipriani P, Berardicurti O, Masedu F, D’Onofrio F, Navarini L, Ruscitti P, et al. Biologic therapies and infections in the daily practice of three Italian rheumatologic units: a prospective, observational study, Clin Rheumatol. 2017;36:251–60.
    19. Reason for exclusion: Autoimmune diseases other than RA were included.
  19. Curtis JR, Jain A, Askling J, Bridges Jr SL, Carmona L, Dixon W, et al. A comparison of patient characteristics and outcomes in selected European and U.S. rheumatoid arthritis registries. Semin Arthritis Rheum. 2010;40(1):2–14.
    20. Reason for exclusion: RA registries in this paper published infection-rate data for TNF-alpha inhibitor elsewhere (Sweden, Swiss, Spain, UK, USA CORONA registry), although eligibility criteria may differ.
  20. Curtis JR, Xie F, Yun H, Bernatsky S, Winthrop KL. Real-world comparative risks of herpes virus infections in tofacitinib and biologic-treated rheumatoid arthritis patients. Ann Rheum Dis 2016;75(10):1843–7.
    21. Reason for exclusion: Severity of infection (herpes zoster) was not reported.
  21. Dixson WG, Hyrick KL, Watson KD, Lunt M, Galloway J, Ustianowski A, et al. 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. 2010;69:522–8.
    22. Reason for exclusion: Severity of infection (tuberculosis) was not reported.
  22. Ebina K, Hashimoto M, Yamamoto W, Hirabo T, Hara R, Katayama M, et al. Drug tolerability and reasons for discontinuation of seven biologics in 4466 treatment courses of rheumatoid arthritis—the ANSWER cohort study. Arthritis Res Ther. 2019;21(1):91.
    23. Reason for exclusion: Infection rate was not reported; adverse outcomes were aggregated.
  23. Ebina K, Hashimoto M, Yamamoto W, Ohnishi A, Kabata D, Hirano T, et al. Drug retention and discontinuation reasons between seven biologics in patients with rheumatoid arthritis - The ANSWER cohort study. PLoS One. 2018;13(3):e0194130.
    24. Reason for exclusion: Infection rates were not reported.
  24. Freitas R, Godinho F, Madeira N, Fernandes BM, Costa F, Santiago M, et al. Safety and effectiveness of biologic disease-modifying antirheumatic drugs in older patients with rheumatoid arthritis: a prospective cohort study. Drugs Aging. 2020;37(12):899-907.
    25. Reason for exclusion: Infection rates were not stratified by drug type.
  25. Frisell T, Dehlin M, Giuseppe DD, Felelius N, Turesson C, Askling J, et al. Comparative effectiveness of abatacept, rituximab, tocilizumab and TNFi biologics in RA: results from the nationwide Swedish register. Rheumatology. 2019;58:1367-77.
    26. Reason for exclusion: Infection rates were not reported.
  26. Galloway JB, Hyrich KL, Mercer LK, Dixon WG, Ustianowski AP, Watson KD, et al. Anti-TNF therapy is associated with an increased risk of serious infections in patients with rheumatoid arthritis especially in the first 6 months of treatment: updated results from the British Society for Rheumatology Biologics Register with special emphasis on risks in the elderly. Rheumatology (Oxford). 2011;50(1):124-31.
    27. Reason for exclusion: Limited to the first 6 months of follow-up.
  27. Garcia-Vidal C, Rodríguez-Fernández S, Teijón S, Esteve M, Rodríguez-Carballeira M, Lacasa JM, et al. Risk factors for opportunistic infections in infliximab-treated patients: the importance of screening in prevention. Eur J Clin Microbiol Infect Dis. 2009;28(4):331-7.
    28. Reason for exclusion: Non RA patients were included.
  28. Genovese MC, Breedveld FC, Emery P, Cohen S, Keystone E, Matteson EL, et al. Safety of biological therapies following rituximab treatment in rheumatoid arthritis patients. Ann Rheum Di.s 2009;68(12):1894–7.
    29. Reason for exclusion: Randomized controlled trial follow-up.
  29. Giannitti C, Benucci M, Caporali R, Manganelli S, Bellisai F, Sebastiani GD, et al. Efficacy and safety of anti-TNF-alpha therapy combined with cyclosporine A in patients with rheumatoid arthritis and concomitant hepatitis C virus infection. Int J Immunopathol Pharmacol. 2009;22(2):543-6.
    30. Reason for exclusion: Case series without denominator data.
  30. Gomes CMF, Terreri MT, de Moraes-Pinto MI, Barbosa C, Machado NP, Melo MR, et al., Incidence of active mycobacterial infections in Brazilian patients with chronic inflammatory arthritis and negative evaluation for latent tuberculosis infection at baseline - A longitudinal analysis after using TNFa blockers. Mem Inst Oswaldo Cruz. 2015;110(7):921-8.
    31. Reason for exclusion: No RA-specific infection rate.
  31. Gomez-Reino JJ, Carmona L, Valverde VR, Mola EM, Montero MD; BIOBADASER Group. Treatment of rheumatoid arthritis with tumor necrosis factor inhibitors may predispose to significant increase in tuberculosis risk: a multicenter active-surveillance report. Arthritis Rheum. 2003;48(8):2122–7.
    32. Reason for exclusion: Severity of infection (tuberculosis) was not reported.
  32. Gottenberg JE, More J, Perrodeau E, Bardin T, Combe B, Dougados M, Flipo R-M, et al. Comparative effectiveness of rituximab, abatacept, and tocilizumab in adults with rheumatoid arthritis and inadequate response to TNF inhibitors: prospective cohort study. BMJ. 2019;364:l67.
    33. Reason for exclusion: Infection rate was not reported; adverse outcomes were aggregated.
  33. Harada S, Sakai R, Hirano F, Miyasaka N, Harigai M, REAL Study Group. Association between medications and herpes zoster in Japanese patients with rheumatoid arthritis: a 5-year prospective cohort study. J Rheumatol. 2017;44:988–95.
    34. Reason for exclusion: Participants were given oral/IV treatment, and severity of opportunistic infectionwas not described.
  34. Harrold LR, Reed GW, Karki C, Magner R, Shewade A, John A, et al. Risk of infection associated with subsequent biologic agent use after rituximab: results from a national rheumatoid arthritis patient registry. Arthritis Care Res (Hoboken). 2016;68(12):1888-93.
    35. Reason for exclusion: Patients with previous severe infections were recruited; the results cannot be compared with the other studies.
  35. Ishiguro T, Takayanagi N, Kagiyama N, Yanagisawa T, Sugita Y. Characteristics of tuberculosis in patients with rheumatoid arthritis: a retrospective single-center study. Intern Med. 2014;53(12):1291-8.
    36. Reason for exclusion: Case series of hospitalized tuberculosis cases.
  36. Kameda H, Tokuda H, Sakai F, Johkoh T, Mori S, Yoshida Y, et al. Clinical and radiological features of acute-onset diffuse interstitial lung diseases in patients with rheumatoid arthritis receiving treatment with biological agents: importance of Pneumocystis pneumonia in Japan revealed by a multicenter study. Intern Med 2011;50(4):305-13.
    37. Reason for exclusion: Case series without denominator data.
  37. Kaur N, Mahl TC. Pneumocystis jiroveci (carinii) pneumonia after infliximab therapy: a review of 84 cases. Dig Dis Sci. 2007;52(6):1481-4.
    38. Reason for exclusion: Case series without denominator data.
  38. Krabbe S, Grøn KL, Glintborg B, Nørgaard M, Menert F, Jarbøl DE, et al. Risk of serious infections in arthritis patients treated with biological drugs: a matched cohort study and development of prediction model Rheumatology (Oxford). 2021;60(8):3834-44.
    39. Reason for exclusion: Infection rates were not stratified by drug type.
  39. Lau AN, Wong-Pack M, Rodjanapiches R, Ioannidis G, Wade S, Spangler L, et al. Occurrence of serious infection in patients with rheumatoid arthritis treated with biologics and denosumab observed in a clinical setting. J Rheumatol. 2018;45(2):170-6.
    40. Reason for exclusion: Concurrent use of denosumab (a human monoclonal antibody for the treatment of osteoporosis) and biologic disease-modifying antirheumatic drug.
  40. Leboime A, Berthelot JM, Allanore Y, Khalil-Kallouche L, Herman P, Orcel P, et al. Sinus aspergilloma in rheumatoid arthritis before or during tumor necrosis factor-alpha antagonist therapy. Arthritis Res Therapy. 2009,11(6):R164.
    41. Reason for exclusion: Case series with no denominator data (person-years of exposure).
  41. Leon L, Peñuelas M, Candel FJ, et al. Indicator opportunistic infections after biological treatment in rheumatoid arthritis, 10 years follow-up in a real-world setting. Ther Adv Musculoskelet Dis. 2019;11:1759720×19878004.
    42. Reason for exclusion: The incidence of severe infection (n=33) was reported, but not by type of drug.
  42. Li N, Betts KA, Messali AJ, Skup M, Garg V. Real-world effectiveness of biologic disease-modifying antirheumatic drugs for the treatment of rheumatoid arthritis after etanercept discontinuation in the United Kingdom, France, and Germany. Clin Ther. 2017;39(8):1618-1627.
    43. Reason for exclusion: Infection rates were not reported.
  43. Liao TL, Lin CH, Chen YM, Chang CL, Chen HH, Chen DY. Different risk of tuberculosis and efficacy of isoniazid prophylaxis in rheumatoid arthritis patients with biologic therapy: a nationwide retrospective cohort study in Taiwan. PLoS One. 2016;11(4):e0153217.
    44. Reason for exclusion: Severity of infection was not reported.
  44. Lim C-H, Chen H-H, Chen Y-H, Chen D-Y, Huang W-N, Tsai J-J, et al. The risk of tuberculosis disease in rheumatoid arthritis patients on biologics and targeted therapy: a 15-year real world experience in Taiwan. PLoS One. 2017;12(6):e0178035.
    45. Reason for exclusion: Severity of infection (tuberculosis) was not reported.
  45. Lim C-H, Lin C-G, Chen D-Y, Chen Y-M, Chao W-C, Liao T-L, et al. One-year tuberculosis risk in rheumatoid arthritis patients starting their first tumor necrosis factor inhibitor therapy from 2008 to 2012 in Taiwan: a nationwide population-based cohort study. PLoS One. 2016;11(11):e0166339.
    46. Reason for exclusion: Severity of infection (tuberculosis) was not reported.
  46. Maillard H, Ornetti P, Grimault L, Ramon J-F, Ducamp SM, Saidani T, et al. Severe pyogenic infections in patients taking infliximab: a regional cohort study. Joint Bone Spine. 2005;72(4):330-4.
    47. Reason for exclusion: No RA-specific infection data; case report with no person-years data.
  47. Mariette X, Gottenberg JE, Ravaud P. Registries in rheumatoid arthritis and autoimmune diseases: data from the French registries. Rheumatology. 2011;50:222–9.
    48. Reason for exclusion: Autoimmune diseases other than RA were included.
  48. McDonald JR, Zeringue AL, Caplan L, Ranganathan P, Xian H, Burroughs TE, et al. Herpes zoster risk factors in a national cohort of veterans with rheumatoid arthritis. Clin Infect Dis. 2009;48(10):1364–71.
    49. Reason for exclusion: Severity of infection (herpes zoster) was not reported.
  49. Monti S, Klersy C, Gorla R, Sarzi-Puttini P, Atzeni F, Pellerito R, et al. Factors influencing the choice of first- and second-line biologic therapy for the treatment of rheumatoid arthritis: real-life data from the Italian LORHEN Registry. Clin Rheumatol. 2017;36(4):753-761.
    50. Reason for exclusion: Infection rates were not reported.
  50. Moreland LW, Weinblatt ME, Keystone EC, Kremer JM, Martin RW, Schiff MH, et al. Etanercept treatment in adults with established rheumatoid arthritis: 7 years of clinical experience. J Rheumatol. 2006;33(5):854-61.
    51. Reason for exclusion: Randomized controlled trial follow-up study, 7-year follow-up.
  51. Movahedi M, Hepworth E, Mirza R, Cesta A, Larche M, Bombardier C. Discontinuation of biologic therapy due to lack/loss of response and adverse events is similar between TNFi and non-TNFi class: results from a real-world rheumatoid arthritis cohort. Semin Arthritis Rheum. 2020;50(5):915-922.
    52. Reason for exclusion: Infection rates were not reported.
  52. Padovan M, Filipini M, Tincani A, Lanciano E, Bruschi E, Epis O, et al. Safety of abatacept in rheumatoid arthritis with serologic evidence of past or present hepatitis b virus infection. Arthritis Care Res. 2016;68(6):738–43.
    53. Reason for exclusion: Severity of infections (hepatis B virus) was not reported.
  53. Papalopoulos I, Fanouriakis A, Kougkas N, Flouri I, Sourvinos G, Bertsias G, et al. Liver safety of non-tumour necrosis factor inhibitors in rheumatic patients with past hepatitis B virus infection: an observational, controlled, long-term study. Clin Exp Rheumatol. 2018;36(1):102-9.
    54. Reason for exclusion: Severity of infections (hepatitis B virus) was not reported.
  54. Pappas D, Hooper MM, Kremer JM, Reed G, Shan Y, Wenkert D, et al. Herpes zoster reactivation in patients with rheumatoid arthritis: analysis of disease characteristics and disease-modifying antirheumatic drug. Arthritis Care Res. 2015;67(12):1671–8.
    55. Reason for exclusion: Severity of infection (herpes zoster) was not reported.
  55. Pappas DA, Kremer JM, Griffith J, Reed G, Salim B, Karki C, et al. Long-term effectiveness of adalimumab in patients with rheumatoid arthritis: an observational analysis from the Corrona Rheumatoid Arthritis Registry. Rheumatol Ther. 2017;(2)4:375–89.
    56. Reason for exclusion: Infection rate was not reported.
  56. Patel R, Mikuls TR, Richards JS, Kerr G, Cannon GW, Baker JF. Disease characteristics and treatment patterns in veterans with rheumatoid arthritis and concomitant hepatitis C infection. Arthritis Care Res (Hoboken). 2015;67(4):467-74.
    57. Reason for exclusion: Hepatitis C virus–positive patients follow-up. Hospitalization related to hepatitis C virus were not reported.
  57. Perez-Sola MJ, Torre-Cisneros J, Perez-Zafrilla B, Carmona L, Descalzo MA, Gómez-Reino JJ, et al. Infections in patients treated with tumor necrosis factor antagonists: incidence, etiology and mortality in the BIOBADASER registry. Med Clin (Barc). 2011;137(11)533-40.
    58. Reason for exclusion: A variety of autoimmune diseases were included: RA, ankylosing spondylitis, psoriatic arthritis, undifferentiated spondyloarthropathy, juvenile idiopathic arthritis, and other rheumatic diseases.
  58. Pettipher C, Rudolph R, Musenge E,Tikly M. A prospective study of anti-tumor necrosis factor therapy in South African rheumatoid arthritis patients. Int J Rheum Dis. 2016;19(6):594-9.
    59. Reason for exclusion: Severity of infection was not reported.
  59. Prior-Español A, Sánchez-Piedra C, Campos J, Manero FJ, Pérez-García C, Bohórquez C, et al. Clinical factors associated with discontinuation of ts/bDMARDs in rheumatic patients from the BIOBADASER III registry. Sci Rep. 2021;11(1):11091.
    60. Reason for exclusion: Infection rates were not reported.
  60. Quach LT, Chang BH, Brophy MT, Soe Thwin S, Hannagan K, O’Dell JR. Rheumatoid arthritis triple therapy compared with etanercept: difference in infectious and gastrointestinal adverse events. Rheumatology (Oxford). 2017;56(3):378-83.
    61. Reason for exclusion: Incident rates were not reported; only rate ratios.
  61. Quartuccio L, Zabotti A, Del Zotto S, Zanier L, De Vita S, Valent F. Risk of serious infection among patients receiving biologics for chronic inflammatory diseases: usefulness of administrative data. J Adv Res. 2018;15:87-93.
    62. Reason for exclusion: No RA-specific infection rate.
  62. Richter A, Listing J, Schneider M, Klopsch T, Kapelle A, Kaufmann J, et al. Impact of treatment with biologic DMARDs on the risk of sepsis or mortality after serious infection in patients with rheumatoid arthritis. Ann Rheum Dis. 2016;75(9):1667–73.
    63. Reason for exclusion: No denominator data were reported; only proportional morbidity was reported.
  63. Sakai R, Kasai S, Hirano F, Harada S, Kihara M, Yokoyama W, et al. No increased risk of herpes zoster in TNF inhibitor and non-TNF inhibitor users with rheumatoid arthritis: epidemiological study using the Japanese health insurance database. Int J Rheum Dis. 2018;21(9):1670-7.
    64. Reason for exclusion: Severity of infection (herpes zoster) was not reported.
  64. Salmon JH, Perotin JM, Morel J, Dramé M, Cantagrel A, Ziegler LE, et al. Serious infusion-related reaction after rituximab, abatacept and tocilizumab in rheumatoid arthritis: prospective registry data. Rheumatology. 2018;57(1):134-9.
    65. Reason for exclusion: Adverse outcomes were limited to infusion-related reactions, and infection rates were not reported.
  65. Sapart E, Sokolova, De Montjoye S, Dierckx S, Nzeusseu A, Avramovska A, et al. Should we use glucocorticoids in early rheumatoid arthritis? Results at 5 years from the early RA UCLouvain Brussels cohort. Rheumatology. 2020;72(SUPPL 10):4023-5.
    66. Reason for exclusion: Infection rates were not reported.
  66. Scherrer CB, Mannion AF, Kyburz D, Vogt M, Kramers-de Quervain IA. Infection risk after orthopedic surgery in patients with inflammatory rheumatic diseases treated with immunosuppressive drugs. Arthritis Care Res (Hoboken). 2013;65(12):2032-40.
    67. Reason for exclusion: No person-years of observation included.
  67. Seong S-S, Choi C-B, Woo J-H, Bae KW, Joung C-L, Uhm W-S, Kim T-H, et al. Incidence of tuberculosis in Korean patients with rheumatoid arthritis (RA): effects of RA itself and of tumor necrosis factor blockers. J Rheumatol. 2007;34:706–11.
    68. Reason for exclusion: Severity of infection (tuberculosis) was not reported.
  68. Silvagni E, Bortoluzzi A, Carrara G, Zanetti A, Govoni M, Scirè CA. Comparative effectiveness of first-line biological monotherapy use in rheumatoid arthritis: a retrospective analysis of the RECord-linkage On Rheumatic Diseases study on health care administrative databases. BMJ Open. 2018;8(9):e021447.
    69. Reason for exclusion: Infection rates were not reported.
  69. Solomon DH, Shadick NA, Weinblatt ME, Zak A, Frits M, Franklin JM. Drug safety analyses in a rheumatoid arthritis registry: application of different approaches regarding timing of exposure and confounder measurement Solomon et al. Arthritis Res Ther. 2017;19(1):130.
    70. Reason for exclusion: Non-severe infections were included and were not stratified by severity.
  70. Strand V, Miller P, Williams SA, Saunders K, Grant S, Kremer J. Discontinuation of biologic therapy in rheumatoid arthritis: analysis from the Corrona RA Registry. Rheumatol Ther. 2017;4(2):489-502.
    71. Reason for exclusion: Infection rates were not reported.
  71. Subesinghe S, Rutherford AI, Byng-Maddick R, Hyrich KL, Galloway JB. Biologic prescribing decisions following serious infection: results from the British Society for Rheumatology Biologics Register—Rheumatoid Arthritis. Rheumatology. 2018;57(12):651655.
    72. Reason for exclusion: Case series: cohort of a history of infection cases.
  72. Suwannalai P, Auethavekiat P, Udomsubpayakul U, Janvitayanujit S. The infectious profiles of anti-tumor necrosis factor agents in a Thai population: a retrospective study a the university-based hospital. Int J Rheum Dis. 2009;12(2):118-24.
    73. Reason for exclusion: No RA-specific infection data.
  73. Takeuchi T, Thorne C, Karpouzas G, Sheng S, Xu W, Rao R, et al. Sirukumab for rheumatoid arthritis: the phase III SIRROUND-D study. Ann Rheum Dis. 2017;76(12):2001-8.
    74. Reason for exclusion: Adverse outcomes related to infection were categorized as “infection and infestation.”
  74. Tarkiainen M, Tynjala P, Vahasalo P, Lahdenne P. Occurrence of adverse events in patients with JIA receiving biologic agents: long-term follow-up in a real-life setting. Rheumatology (Oxford). 2015;54(7):1170-6.Reason for exclusion: Participants with childhood-onset arthritis.
  75. Trifirò G, Isgrò V, Ingrasciotta Y, Ientile V, L’Abbate L, Foti SS, et al. Large-scale postmarketing surveillance of biological drugs for immune-mediated infammatory diseases through an Italian distributed multi-database healthcare network: the VALORE Project BioDrugs. 2021;35(6):749-64.
    76. Reason for exclusion: Infection rates were not reported.
  76. Tubach F, Salmon D, Ravaud P, Allanore Y, Goupille P, Bréban M, et al. 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. 2009;60(7):1884-94.
    77. Reason for exclusion: Severity of infection was not reported.
  77. Vergidis P, Avery RK, Wheat LJ, Dotson JL, Assi MA, Antoun SA, et al. Histoplasmosis complicating tumor necrosis factor-α blocker therapy: a retrospective analysis of 98 cases. Clin Infect Dis. 2015;61(3):409-17.
    78. Reason for exclusion: No person-years of denominators included.
  78. Winthrop KL, Baddley JW, Chen L, Liu L, Grijalva CG, Delzell E, et al. Association between the initiation of anti-TNF therapy and the risk of herpes zoster JAMA. 2013;309(9):887–95.
    79. Reason for exclusion: Severity of infection was not reported.
  79. Yamada T, Nakajima A, Inoue E, Tanaka E, Hara M, Tomatsu T, et al. Increased risk of tuberculosis in patients with rheumatoid arthritis in Japan. Ann Rheum Dis. 2006;65(12):1661–3.
    80. Reason for exclusion: Type of disease-modifying antirheumatic drug was not reported; the risk of tuberculosis in patients with RA was compared with that of general population.
  80. Yoo IK, Choung RS, Hyun JJ, Kim SY, Jung SW, Koo JS, et al. Incidences of serious infections and tuberculosis among patients receiving anti-tumor necrosis factor-α therapy. Yonsei Med J. 2014;55(2):442-8.
    81. Reason for exclusion: Person-years were not reported.
  81. Yun H, Xie F, Delzell E, Chen L, Levitan EB, Lewis JD, et al. Risk of hospitalized infection in rheumatoid arthritis patients receiving biologics following a previous infection while on treatment with anti-TNF therapy. Ann Rheum Dis. 2015;74(6):1065-71.
    82. Reason for exclusion: Cohort of RA patients with previous infection.
  82. Yun H, Xie F, Delzell E, Chen L, Levitan EB, Lewis JD, et al. Risks of herpes zoster in patients with rheumatoid arthritis according to biologic disease modifying therapy. Arthritis Care Res (Hoboken). 2015;67(5):731–6.
    83. Reason for exclusion: Severity of infections (herpes zoster) was not reported.
  83. Yusof MYM, Vital EM, McElvenny DM, Hensor EMA, Das S, Dass S, et al. Predicting severe infection and effects of hypogammaglobulinaemia during therapy with rituximab in rheumatic and musculoskeletal diseases. Arthritis Rheumatol. 2019;71:1812-23.
    84. Reason for exclusion: No RA-specific infection data.
  84. Zamora-Legoff JA, Krause ML, Crowson CS, Ryu JH, Matteson EL. Risk of serious infection in patients with rheumatoid arthritis-associated interstitial lung disease. Clin Rheumatol. 2016;35(10):2585-9.

Reason for exclusion: Biologic drug treatment was not described.

Appendix III: Data extraction instrument

-
Authors
Title
Citation
Year
Country
Registry
Study objective
Study period
Sample size
Person-years
Age
% women
Sympton onset (yrs), Disease duration
Smoking (%)
Glucocorticoids
Methotrexate
Comorbidity
Drugs
Instruments
Mortality rate
selective T-cell co-stimulation modulators: abatacept, (ABA, ABT) (Orencia)
adalimumab (ADA) (Humira)
certolizumab (CZT, CZP) (Cimzia)
etanercept (ETA, ETN) (Enbrel)
infliximab (IFX) (Remicade)
golimumab (ZLM, GOL) (Simponi)
tocilizumab, sarilumab (TCZ) (Actemra); anti-IL-6
rituximab (RTX) (Rituxan); B cell
tofacitinib (JAK); tofacitinib (Xeljanz)
TNFi, unspecified
anakinra (Kineret); IL-1
other biologic disease-modifying antirheumatic drugs (non-TNF inhibitors)
conventional disease-modifying antirheumatic drug
Definition of serious infection

Appendix IV: Characteristics of included studies

-
First author, year Country Registry Study period Sample size Age (years) % women Severe infection/hospitalized infection Type of infection TNFi cDMARD Other
Aaltonen,16 2015 Finland National Register for Biologic Treatment in Finland (ROB-FIN) 1998-2011 3762 infliximab 52(44-59); cDMARD 62(53-72) 69-77 HI Erysipelas, infectious gastroenteritis and colitis, bronchitis, TB, and sepsis Y Y Y
Askling,17 2007 Sweden Swedish Biologics Register (ARTIS) and other national Swedish registers 1999-2003 4167 RA patients starting TNFi treatment 0-49; 50-74; 75+ 75 HI, F Respiratory, pneumonia, gastrointestinal, skin/soft tissue, joint, septicemia Y
Carmona,18 2007 Spain i) BIOBADASER ii) EMECAR Groups BIOBADASER 2001-2006; EMECAR 1999-2005 4459 patients with RA treated with TNFi 61±13 72 EMECAR SI A condition that causes death or is life-threatening, implies inpatient hospitalization or prolonging of an existing one, and involves persistent or significant disability Y
Carrara,19 2019 Italy Health databases of Lombardy Region, Italy 2004-2013 4656 with at least one bDMARD prescription 55.8±12.7 77.4 HI, F Pneumonia, bacteremia, cellulitis, septic arthritis, osteomyelitis, pyelonephritis, meningitis, encephalitis, endocarditis Y Y
Cecconi,20 2020 Brazil Brazilian Society of Rheumatology Jan 2009-Jun 2015 1024 54.9±11.8 85 SI Skin/soft tissue, respiratory, urinary, osteoarticular, other infections Y
Chen,21 2020 USA RA enrolled in Medicare; the Truven MarketScan database Jan 1, 2006-Sep 30, 2010 10,794 64-69 76-84 HI Bacterial, viral or opportunistic infection (bone/joint, cardiac, gastrointestinal, genitourinary, respiratory, skin/soft tissue, neurologic) Y Y
Chiu,22 2014 Taiwan Taiwan’s National Health Insurance Research Database Jan 1999- Dec 2009 34,947 56 82.3 SI Serious bacterial infection, TB Y Y
Curtis,23 2007 USA Medical and pharmacy administrative claims of a large US health care organization May 1998-Dec 2003 5326 TNFi 50±12; methotrexate only 55±13 70 HI Pneumonia, soft tissue, sepsis, UTI, SSI, device-associated, septic arthritis, gastroenteritis, abdominal abscess, osteomyelitis, sinusitis, diverticulitis Y Y
Curtis,24 2013 USA i) Medicare and Medicaid ii) commercial insurance database 2000-2006 Medicare; 2005-2010 commercial TNFi; 6560 Medicare; 5097 commercial Medicare 72.8±5.8; commercial 47.9±10.4 Medicare 89.7; commercial 76.5 HI, F Pneumonia, soft tissue, sepsis, UTI, SSI, device-associated, septic arthritis, gastroenteritis, abdominal abscess, osteomyelitis, sinusitis, diverticulitis Y Y
Dixon,25 2006 UK British Society for Rheumatology Biologics Register Dec 2001 - Sep 2005 7664 TNFi-treated and 1354 DMARD-treated all TNFi 56±12; DMARD 60±13 TNFi 76; DMARD 71 SI Mycobacterium tuberculosis, Legionella pneumophila, Listeria monocytogenes, Mycobacterium fortuitum, Salmonella Y Y
Dominique,26 2020 Switzerland, Denmark Swiss Clinical Quality Management, Danish database for biologic therapies – 1213 52-58 78-85 HI – Y
Emery,27 2014 UK British Society of Rheumatology Biologics Register (BSRBR) Oct 2001 etanercept 3470; DMARD 1365 etanercept 55.4 ± 12.1; DMARD 59.5 ± 12.4 etanercept 77.2; DMARD 75.0 SI, F -- Y Y
Favalli,28 2009 Italy Lombardy Rheumatology Network (LORHEN) registry Oct 1999 1064; 1395 treatment courses 55.84±12.96 for all; non-significant among 3 groups 76.1 SI Pneumonia/bronchitis, skin/soft tissue (cellulitis, wound infections, herpes zoster), intra-abdominal (peritonitis, gastroenteritis, etc.), UTI Y
Gottenberg,29 2010 France Autoimmunity and rituximab registry was set up by the French Society of Rheumatology Sep 2005-Apr 2009 1303 57.7±12.7 77.7 SI, F Bacterial infections; bronchopulmonary, skin/soft tissue, UTI, gastrointestinal tract, osteoarticular, eye, nose/throat, septicemia Y
Gron,30 2019 Sweden, Denmark i) Denmark (DANBIO) ii) Sweden (Anti-Rheumatic Treatment in Sweden Register/Swedish Rheumatology Quality Register) 2010-2015 6648; 8987 treatment courses abatacept;Denmark 59 (50-67), Sweden 61 (52-69) 76-81 SI, F — Y Y
Hansen,31 2004 USA 6-center retrospective medical chart review study NA 88 53 (25-82) 72 HI Bacterial infections: cellulitis, septic arthritis, pneumonia, acute respiratory distress syndrome Y
Harrold,32 2018 USA Corrona RA registry May 1, 2009-Mar 31, 2016 5363 certolizumab 58±18.0; other TNFi 56±16.5; <0.001 79 SI Joint/bursa, cellulitis/skin, sinusitis, diverticulitis, sepsis, pneumonia, bronchitis, gastroenteritis, meningitis/encephalitis, UTI, upper respiratory infection, tuberculosis, including opportunistic infections Y
Harrold,33 2020 USA Corrona RA registry Jan 2008-June 2017 2798 Mean: 54.5 77 SI Joint/bursa, cellulitis, sinusitis, diverticulitis, sepsis, pneumonia, gastroenteritis, meningitis, UTI, opportunistic infections (pneumocystosis, TB, candidiasis) Y
Henry,34 2018 France The AIR registry Dec. 2008 1278 (1093 standard dose; 185 reduced dose) 57.9±12.2 79.1 SI NA Y
Inanc,35 2006 Turkey Marnara University Rheumatology clinic NA 130 DMARD, 48 TNFi TNFi 52±10; DMARD 55±13 DMARD 84; TNFi 90 HI Bacteremia/septicemia, septic arthritis, TB, pneumonia, lower respiratory tract, UTI, skin/soft tissue, intra-abdominal, serious viral, otitis media, sinusitis Y Y
Isvy,36 2012 France Single center prospective study 2005-2010 65 59 (26-83) 93 SI NA Y
Jani,37 2018 UK British Society for Rheumatology Biologics Register 2005-2010 703 58 ± 12 74 SI, F NA Y
Jeon,38 2021 South Korea Korean National Health Insurance data Jan 2013-Dec 2018 1395 tocilizumab, 7395 TNFi 55.4±13.0 vs. 52.1±14.3 81.6 vs. 72.3 SI Respiratory tract, gastrointestinal tract, urological and gynecological infections, skin and subcutaneous tissue, sepsis, TB, and herpes zoster Y Y
Johnston,39 2013 USA The Truven Health Analysis (Thomson Reuters) MarketScan, and Medicare Jan 2004-Mar 2010 4332 treatment episodes 54.3-57.0 80 SI Acute sinusitis, acute bronchitis and bronchiolitis, acute upper respiratory infections, UTI, cellulitis, abscess Y
Komano,40 2011 Japan Registry of Japanese Rheumatoid Arthritis Patients for Long Term Safety 1-year follow-up 1144 58.3±13.2 for exposed; 61.4±12.8 for unexposed 82 for exposed; 83.3 for unexposed SI Bacterial infections, opportunistic infections (TB, Pneumocystis jirovecii pneumonia, systemic fungal, cytomegalovirus) Y Y
Kroesen,41 2003 Switzerland Single center study, retrospective design Oct 1999-May 2002 60 52.98 (21.3-85.4) 78.3 SI Pneumonia, bloodstream infection, urosepsis, septic arthritis, diverticulitis, pyelonephritis Y
Lahaye,42 2016 France French Society of Rheumatology’s ORA registry Jun 2008-Apr 2010 1017 <50, 50-64, 65-74, 75+; of 1017 patients 103 very elderly (≥75) 74.4-84.6 (nonsignificant differences among 4 age groups) SI Bronchopulmonary, genitourinary, and articular Y
Lampropoulos,43 2015 Greece University hospital in Athens 1985-2013: for bDMARD 1995 1403 53.0 ± 14.1 78 SI, F - Y Y
Lane,44 2011 USA US Department of Veterans Affairs national database Oct 1998-Sep 2005 20,814 59.4±11.6 91 HI Pneumonia, bronchitis, cellulitis, UTI, infection due to implant, postoperative infection, osteomyelitis, diverticulitis, septicemia, intestinal infection Y Y
Lang,45 2012 Bavaria University hospital–based study Oct 2008-Mar 2010 112 54.75±13.27 79.4 SI Infected rheumatoid nodule, osteomyelitis, pneumonia, pseudomembranous colitis, gastroenteritis, hemorrhagic enterocolitis, sepsis Y
Listing,46 2005 Germany German biologics register RABBIT (a long-term prospective cohort study) May 2001-Sep 2003 512 etanercept 53.7±12.6; control 56.5±11.4 etanercept 78.1; control 82.7 SI Lower respiratory, bacterial skin and subcutaneous tissue, bone/joint Y Y
Machado,47 2018 USA MarketScan Commercial Claims database and MarketScan Medicare Supplemental database 2011-2014 21,832 56 (48-63) 77 SI, F NA Y Y Y
Montastruc,48 2019 USA Truven MarketScan, supplemental US Medicare databases 2007-2014 5752 with abatacept; 78,556 with another bDMARD 56 (48–63) 82.9 HI Any infection, including bone and joint, gastrointestinal, respiratory, skin and soft tissue, UTI Y Y
Morel,49 2017 France French REGistry-RoAcTEmra (REGATE) Jan 2011-Mar 2015 1491 56.6±13.6 79.9 SI, F Lung/respiratory infection, skin/soft tissue, urogenital, gastrointestinal tract, articular sites, pneumocystosis, pyelonephritis Y
Mori,50 2017 Japan SARABA study: The SAfety Profile of RA patients receiving Biological Agents study 2009-2014 First year of follow-up 1491; 1596 new treatment episodes 60.9±14.2 77.5 HI Includes viral infections, pneumonia, gastrointestinal, skin and soft tissue, UTI, musculoskeletal Y Y
Neven,51 2005 Netherlands Medical center–based study Apr 2000-Oct 2002 168 - 82 SI -- Y
Nguyen-Khoa,52 2012 USA MarketScan database supplemented with Medicare database 2000-2007 Non-switcher 13,752; switcher 2293; total 16,045 Non-switcher 56.1; switchers 55.7 74.8 for non-switcher; 78.4 for switchers SI Sepsis, pneumonia, UTI, skin, TB, opportunistic infections Y
Ozen,53 2019 USA FORWAR: The National Databank for Rheumatic Diseases 2001-2016 1162 60.3 ± 13.4 conventional synthetic DMARDs; 59.9 ± 14.1 TNFi 79.4 conventional synthetic DMARDs; 79.6 TNFi SI — Y Y
Pawar,54 2018 USA i) Medicare 2010-2015 ii) IMS ‘PharMetrics’ Plus 2011-2015 iii) Truven MarketScan 2010-2015 141,869 tocilizumab; Medicare 72.2±6.0; IMS 50.8±11.7; MarketScan 53.5±12.6 83.1-81.5 SI Bacterial and opportunistic infection, UTI, skin and soft tissue, TB, viral hepatitis, diverticulitis, pneumonia/upper respiratory tract, and septicemia/bacteremia Y Y
Ranza,55 2019 Brazil, Argentina i) BIOBADABRASIL (Brazil) ii) BIOBADASAR (Argentina) 2010-2016 3717 52.4 ± 13.1 85 SI -- Y Y
Rutherford,56 2018 UK British Society for Rheumatology Biologics Register for Rheumatoid Arthritis (BSRBR-RA) 2001 (inception of cohort) to Jun 2016 19,282 56 ± 12 - 60 ± 12 76.1-78.6 SI Sepsis, lower respiratory, skin, gastrointestinal, bone/joint, genitourinary, and other Y Y
Sakai,57 2012 Japan Registry of Japanese RA Patients for Long-Term Safety (REAL) database 2005-Nov 2009 727; 571 with nonbiologic DMARD 53.7-59.3 79.3-85.1 SI Pulmonary, skin, gastrointestinal, urinary, bone/joints, sepsis, SSI, bacterial meningitis, sinusitis, viral meningitis Y Y
Sakai,58 2014 Japan Registry of Japanese RA Patients for Long-Term Safety (REAL) database i) 2005- 2007 ii) 2008- 2011 i) 716 ii) 352 i) 56.1±13.3 ii) 57.9±14.8 81.8-81.2 SI Respiratory tract infections, etc. Y
Salmon,59 2016 France Orencia and RA registry supported by the French Society of Rheumatology 5-year prospective registry 976 58±13.6 79.1 SI, F Osteoarticular, including SSI, UTI, skin/soft tissues, gastrointestinal tract, eye/nose/throat, catheter infection, endocarditis, pericarditis, keratitis, or viral hepatitis Y
Schenfeld,60 2017 USA MarketScan Commercial and Medicare supplemental claims database 2005-2014 40,933 53.0 ± 12.6 77.4 HI Any inpatient HI event diagnostic claim with 1 overnight hospitalization stay in which the HI event diagnosis was the primary or secondary reason for hospitalization during the 90 days preceding and including the index date Y
Schneeweiss,61 2007 USA Pharmaceutical Assistance Contract for the Elderly (PACE) provided by the state of Pennsylvania Jan 1995-Jan 2003 15,597 75-79 88-91 HI, F Meningitis, encephalitis, endocarditis, pneumonia, pyelonephritis, septic arthritis, osteomyelitis, septicemia or bacteremia; opportunistic infections (pulmonary TB, etc.) Y Y
Sharma,62 2019 Australia 3 tertiary hospitals in Western Australia 2003-2006; 2006-2016 102 61 (53-68) 73 HI Skin/soft tissue, respiratory, UTI, bone/joint, abdominal infection, multiple infections Y Y
Silva-Fernandez,63 2018 UK British Society for Rheumatology Biologics Register (BSRBR-RA) switched to either a second TNFi or rituximab after failing a first TNFi 2007-2015 4815; 3419 in TNFi cohort, 1396 in rituximab cohort TNFi 55.9 ± 12.3; rituximab 58.3 ± 12.2 TNFi 80; rituximab 77 SI, F Respiratory tract/lung, UTI, bone/joint, skin structure and soft tissue, central nervous system and spinal, sepsis/bacteremia and fungal, muscle and soft tissue, eye and eyelid, influenza viral Y Y
Simon,64 2019 USA US health care databases: i) MarketScan ii) PharMetrics iii) Optum Jul 1, 2006-Sep 30, 2014 92,017 abatacept: Optum 51 ± 11; MarketScan 55 ± 13 81 - 83 HI Opportunistic infections, primary TB, dermatophytosis, dermato mycosis, candidiasis, coccidioidomycosis, histoplasmosis, blastomycosis Y
Tokunaga,65 2021 Japan National Database of Rheumatic Diseases by iR net in Japan (NinJa) Apr 2010-Mar 2019 tacrolimus (1328); abatacept (563); combination treatment (118) tacrolimus: 69; abatacept: 70; combination: 67 78.2 for tacrolimus, 83.1 for abatacept, 82.2 for combination HI Pneumonia, UTI, influenza virus, cellulitis, enteritis, SSI, bacterial vertebral osteomyelitis, sepsis, herpes zoster, mycosis, sinusitis Y Y
van Dartel,66 2013 Netherlands Dutch Rheumatoid Arthritis Monitoring (DREAM) registry 2003-Oct 2010, 5-year follow-up 2356 54±13 69 SI, F Upper/lower respiratory, sepsis, skin/soft tissue, musculoskeletal, cardiovascular, intra-abdominal, UTI, ear/nose/throat Y Y
Yun,67 2016 USA Medicare data 2006-2011 31,801 with new biologic treatment episodes in patients who previously received another bDMARD golimumab 60.4 ± 13.5; abatacept 66.8 ± 12.1 adalimumab 83.9; golimumab 88.7 HI, F Pneumonia/respiratory tract, skin/soft tissue, genitourinary tract, sepsis/bacteremia Y
bDMARD, biologic disease-modifying antirheumatic drug; cDMARD, conventional disease-modifying antirheumatic drug; F, first infection; HI, hospitalized infection; NA, not applicable; RA, rheumatoid arthritis; SI, severe infection; SSI, surgical site infection; TB, tuberculosis; TNFi, tumor necrosis factor inhibitor; UTI, urinary tract infection.

Appendix V: Characteristics of 4 studies reporting infection incidence in patients receiving tumor necrosis factor- inhibitors

-
Carrara et al., 201919 Harrold et al., 202033 Mori et al.,* 201750 Yun et al., 201667
Study period 2004-2013 2008-2017 2009-2014 2006-2011
Infection incidence rate 0.9 1.9 7.3 15.7
Number of TNFi 5 1 3 5
Definition of infection First hospitalized bacterial infection Serious infection Hospitalized infection First hospitalized infection
Country Italy USA Japan USA
Data source Administrative health record RA registry Cohort study Insurance claim (Medicare)†
Mean age (years) 55.8 ± 12.7 54.5 ± 13.2 60.9 ± 14.2 60.4 ± 13.5 to 65.3 ± 12.5‡
Duration of disease (years) ≤ 2: 47% 3-5: 30% 8.3 ± 9 7.8 ± 9.5 –
Severity of RA or DAS28 – Mean DAS28: 4.2 ± 1.6 RA stage III/IV: 43.1% –
Comorbidity Charlson Comorbidity Index mean 1.24 ± 0.75 – – –
Diabetes mellitus – 12.5% 21% to 23%‡
COPD or lung disease – 24.9% (chronic lung disease) 21% to 24%‡ (COPD)
Cardiovascular disease 34% (eg, hypertension, hyperlipidemia, stroke) – 4.5% to 5.9%‡ (heart failure)
Other 5% (malignancy) 13% (chronic kidney disease) 4.1% to 5.0%‡ (renal disease)
Smoking – Current smoker (20%), previous smoker (27%) 22.2% (≥ 10 pack-years) –
BMI – 25% (BMI < 25) 24% (BMI > 35) 12.8% (BMI < 18.5) –
Prior biologic treatment – 52% 29.5% 100%
≥ 2 biologic treatment – 16% – 16% to 52%‡
History of hospitalized/serious infection 0.5% 5% – 11%
Corticosteroid use Mean dosage: 2.57 mg/day 28% 49.9% 54% to 62%‡
High-dose corticosteroid use ≥ 10 mg/day: 9% Mean dosage: 5.8 ± mg/day > 7.5 mg/day: 14% to 16.3%‡
BMI, body mass index; COPD, chronic obstructive pulmonary disease; DAS28, Disease Activity Score-28; RA, rheumatoid arthritis; TNFi, tumor necrosis factor inhibitors
*Mori et al.50 was the only study to report the diagnostic criteria for comorbidity based on laboratory and radiographic results.
†Government insurance for older people (≥ 65 years) and younger persons with specific diseases, including RA.
‡Yun et al.67 presented data by type of TNFi, and the range among 5 drugs was presented.

References

1. Aggarwal D, Abraham S. Rheumatoid arthritis treatments: a historical perspective. JSM Arthritis 2016;1(2):1011–20.
2. Allaire S, Wolfe F, Niu J, Lavalley M. Contemporary prevalence and incidence of work disability associated with rheumatoid arthritis in the US. Arthritis Rheum 2008;59(4):474–80.
3. Bongartz T, Sutton AJ, Sweeting MJ, Buchan I, Matteson EL, Montori V. Anti-TNF antibody therapy in rheumatoid arthritis and the risk of serious infections and malignancies. JAMA 2006;295(19):2275–85.
4. Sepriano A, Kerschbaumer A, Smolen JS, van der Heijde D, Dougados M, van Vollenhoven R, et al. Safety of synthetic and biological DMARDs: a systematic literature review informing the 2019 update of the EULAR recommendations for the management of rheumatoid arthritis. Ann Rheum Dis 2020;79:760–70.
5. Singh JA, Cameron C, Noorbaloochi S, Cullis T, Tucker M, Christensen R, et al. Risk of serious infection in biological treatment of patients with rheumatoid arthritis: a systematic review and meta-analysis. Lancet 2015;386:258–65.
6. Jani M, Barton A, Hyrich K. Prediction of infection risk in rheumatoid arthritis patients treated with biologics: are we any closer to risk stratification? Curr Opin Rheumatol 2019;31(3):285–92.
7. Cabral VP, de Andrade CAF, Passos SRL, Martins MFM, Hökerberg YHM. Severe infection in patients with rheumatoid arthritis taking anakinra, rituximab, or abatacept: a systematic review of observational studies. Rev Bras Reumatol 2016;56(6):543–550.
8. Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO, et al. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum 2010;62(9):2569–81.
9. Sokolove J, Strand V. Rheumatoid arthritis classification criteria – it’s finally time to move on! Bull NYU Hosp Jt Dis 2010;68(3):232–8.
10. Burke RA, White ND Murphy JE, Lee MW. Biologic disease-modifying antirheumatic drugs. PSAP 2014 Book 2 (Chronic illnesses): Pharmacotherapy self-assessment program [internet]. American College of Clinical Pharmacy; 2014 [cited 2022 Dec 9]. Available from: https://www.accp.com/docs/bookstore/psap/p14b2_m1ch.pdf.
11. Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Chapter 5: Systematic reviews of prevalence and incidence. In: Aromataris E, Munn Z. JBI Manual for Evidence Synthesis [internet]. JBI, 2020 [cited 2022 Feb 16]. Available from: https://synthesismanual.jbi.global.
12. Moher D, Liberati A, Tetzlaff J, Altman DG; The PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009;6(7):e1000097.
13. Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. Int J Evid Based Healthc 2015;13(3):147–53.
14. Doi SAR, Barendregt JJ, Khan S, Thalib L, Williams GM. Advances in the meta-analysis of heterogeneous clinical trials I: the inverse variance heterogeneity model. Contemp Clin Trials 2015;45(Pt A):130–8.
15. Rander H, Dixon W, Hyrich K, Asking J. Consistency and utility of data items across European rheumatoid arthritis clinical cohorts and registers. Arthritis Care Res 2015;67(9):1219–29.
16. Aaltonen KJ, Joensuu JT, Virkki L, Sokka T, Aronen P, Relas H. Rates of serious infections and malignancies among patients with rheumatoid arthritis receiving either tumor necrosis factor inhibitor or rituximab therapy. J Rheumatol 2015;42:372–8.
17. Askling J, Fored CM, Brandt L, Baecklund E, Bertilsson L, Feltelius N, et al. Time-dependent increase in risk of hospitalisation with infection among Swedish RA patients treated with TNF antagonists. Ann Rheum Dis 2007;66(10):1339–44.
18. Carmona L, Descalzo MA, Perez-Pampin. All-cause and cause-specific mortality in rheumatoid arthritis are not greater than expected when treated with tumour necrosis factor antagonists. Ann Rheum Dis 2007;66:880–5.
19. Carrara G, Bortoluzzi A, Sakellariou G, Silvagni E, Zanetti A, Govoni M, et al. Risk of hospitalisation for serious bacterial infections in patients with rheumatoid arthritis treated with biologics. Analysis from the RECORD linkage on rheumatic disease study of the Italian Society for Rheumatology. Clin Exp Rheumatol 2019;37(1):60–66.
20. Cecconi M, Ranza R, Titton DC, Moraes JCB, Bertolo M, Bianchi W, et al. Incidence of infectious adverse events in patients with rheumatoid arthritis and spondyloarthritis on biologic drugs-data from the Brazilian Registry for Biologics Monitoring. J Clin Rheumatol 2020;26(2):73–8.
21. Chen SK, Liao KP, Liu J, Kim SC. Risk of hospitalized infection and initiation of abatacept versus tumor necrosis factor inhibitors among patients with rheumatoid arthritis: a propensity score-matched cohort study. Arthritis Care Res 2020;72(1):9–17.
22. Chiu YM, Lang HC, Lin HY, Yang MT, Fang C. Risk of tuberculosis, serious infection and lymphoma with disease-modifying biologic drugs in rheumatoid arthritis patients in Taiwan. Int J Rheumatic Dis 2014;17(Suppl 3):9–19.
23. Curtis JR, Patkar N, Xie A, Martin C, Allison JJ. Risk of serious bacterial infections among rheumatoid arthritis patients exposed to tumor necrosis factor alpha antagonists. Arthritis Rheumat 2007;56(4):1125–33.
24. Curtis JR, Xie F, Chen L, Muntner P, Grijalva CG, Spettell C. Use of a disease risk score to compare serious infections associated with anti-TNF therapy among high versus lower risk rheumatoid arthritis patients. Arthritis Care Res (Hoboken) 2013;65(2):235–43.
25. Dixon WG, Watson K, Lunt M, Hyrich KL, Silman AJ, Symmons DP. Rates of serious infection, including site-specific and bacterial intracellular infection, in rheumatoid arthritis patients receiving anti-tumor necrosis factor therapy: results from the British Society for Rheumatology Biologics Register. Arthritis Rheumat 2006;54(8):2368–76.
26. Dominique A, Hetland M, Finckh A, Gottenberg J, Iannone F, et al. Infection and malignancy outcomes in patients with ra treated with abatacept: results from a multinational surveillance study. Arthritis Rheumatol 2020;72(Suppl 10):1010; Abstract no.
27. Emery P, Gallo G, Boyd H, Morgan CL, Currie CJ, Poole CD, et al. Association between disease activity and risk of serious infections in subjects with rheumatoid arthritis treated with etanercept or disease-modifying antirheumatic drugs. Clin Exp Rheumatol 2014;32(5):653–660.
28. Favalli EG, Desiati F, Atzeni F, Sarzi-Puttini P, Caporali R, Pallavicini FB, et al. Serious infections during anti-TNF-alpha treatment in rheumatoid arthritis patients. Autoimmun Rev 2009;8(3):266–73.
29. Gottenberg JE, Ravaud P, Bardin T, Cacoub P, Cantagrel A, Combe B, et al. Risk factors for severe infections in patients with rheumatoid arthritis treated with rituximab in the autoimmunity and rituximab registry. Arthritis Rheum 2010;62(9):2625–32.
30. Gron KL, Arkema EV, Glintborg B, Mehnert F, Østergaard M, Dreyer L, et al. Risk of serious infections in patients with rheumatoid arthritis treated in routine care with abatacept, rituximab and tocilizumab in Denmark and Sweden. Ann Rheum Dis 2019;78(3):320–7.
31. Hansen KE, Cush J, Singhal A, Cooley DA, Cohen S, Paten SR, et al. The safety and efficacy of leflunomide in combination with infliximab in rheumatoid arthritis. Arthritis Rheumatol 2004;51(2):228–32.
32. Harrold LR, Litman HJ, Saunders KC, Dandreo KJ, Gershenson B, Greenberg JD, et al. One-year risk of serious infection in patients treated with certolizumab pegol as compared with other TNF inhibitors in a real-world setting: data from a national US rheumatoid arthritis registry. Arthritis Res Ther 2018;20(1):2.
33. Harrold LR, Griffith J, Zueger P, Litman JH, Gershenson B, Islam SS, et al. Long-term, real-world safety of adalimumab in rheumatoid arthritis: analysis of a prospective US-based registry. J Rheumatol 2020;47(7):959–67.
34. Henry J, Gottenberg JE, Rouanet S, Pavy S, Sellam J, Tubach F, et al. Doses of rituximab for retreatment in rheumatoid arthritis: influence on maintenance and risk of serious infection. Rheumatology (Oxford) 2018;57(3):538–47.
35. Inanc N, Direskeneli H. Serious infections under treatment with TNF-antagonists compared to traditional DMARDs in patients with rheumatoid. Rheumatol Int 2006;27:67–71.
36. Isvy A, Meunier M, Gobeaux-Chenevier C, Maury E, Wipff J, Job-Deslandre C, et al. Safety of rituximab in rheumatoid arthritis: a long-term prospective single-center study of gammaglobulin concentrations and infections. Joint Bone Spine 2012;79(4):365–9.
37. Jani M, Dixon WG, Lunt M, De Cock D, Isaacs J, Morgan A, et al. The association of biologic drug-levels with infection risk: results from the British society for rheumatology biologics register for rheumatoid arthritis. Arthritis Rheumatol 2018;70(Suppl 9):1073–4.
38. Jeon HL, Kim SC, Park SH, Shin JY. The risk of serious infection in rheumatoid arthritis patients receiving tocilizumab compared with tumor necrosis factor inhibitors in Korea. Semin Arthritis Rheum 2021;51(5):989–95.
39. Johnston SS, Turpcu A, Shi N, Fowler R, Chu BC, Alexander K. Risk of infections in rheumatoid arthritis patients switching from anti-TNF agents to rituximab, abatacept, or another anti-TNF agent, a retrospective administrative claims analysis. Semin Arthritis Rheum 2013;43:39–47.
40. Komano Y, Tanaka M, Nanki T, Koike R, Sakai R, Kameda H, et al. Incidence and risk factors for serious infection in patients with rheumatoid arthritis treated with tumor necrosis factor inhibitors: a report from the Registry of Japanese Rheumatoid Arthritis Patients for Longterm Safety. J Rheumatol 2011;38(7):1258–64.
41. Kroesen S, Widmer AF, Tyndall A, Hasler P. Serious bacterial infections in patients with rheumatoid arthritis under anti-TNF-alpha therapy. Rheumatology (Oxford) 2003;42(5):617–21.
42. Lahaye C, Soubrier M, Mulliez A, Bardin T, Cantagrel A, Combe B, et al. Effectiveness and safety of abatacept in elderly patients with rheumatoid arthritis enrolled in the French Society of Rheumatology’s ORA registry. Rheumatology (Oxford) 2016;55(5):874–882.
43. Lampropoulos CE, Orfanos P, Bournia VK, Karatsourakis T, Mavragani C, Pikazis D, et al. Adverse events and infections in patients with rheumatoid arthritis treated with conventional drugs or biologic agents: a real world study. Clin Exp Rheumatol 2015;33(2):216–224.
44. Lane MA, McDonald JR, Zeringue AL, Caplan L, Curtis JR. TNF-alpha antagonist use and risk of hospitalization for infection in a national cohort of veterans with rheumatoid arthritis. Medicine (Baltimore) 2011;90(2):139–45.
45. Lang VR, Englbrecht M, Rech J, Nüsslein H, Manger K, Schuch F, et al. Risk of infections in rheumatoid arthritis patients treated with tocilizumab. Rheumatol (Oxford) 2012;51(5):852–7.
46. Listing J, Strangfeld A, Kary S, Rau R, von Hinueber U, Stoyanova-Scholz M, et al. Infections in patients with rheumatoid arthritis treated with biologic agents. Arthritis Rheum 2005;52(11):3403–12.
47. Machado MAA, Moura CS, Guerra SF, Curtis JR, Abrahamowicz M, Bernatsky S. Effectiveness and safety of tofacitinib in rheumatoid arthritis: a cohort study. Arthritis Res Therap 2018;20:60.
48. Montastruc F, Renoux C, Hudson M, Dell’Aniello S, Simon TA, Suissa S. Abatacept initiation in rheumatoid arthritis and the risk of serious infection: a population-based cohort study. Semin Arthritis Rheum 2019;48(6):1053–8.
49. Morel J, Constantin A, Baron G, Dernis E, Flipo RM, Rist S, et al. Risk factors of serious infections in patients with rheumatoid arthritis treated with tocilizumab in the French Registry REGATE. Rheumatol (Oxford) 2017;56(10):1746–54.
50. Mori J, Yoshitama T, Hidaka T, Sakai F, Hasegawa M, Hashiba Y, et al. Comparative risk of hospitalized infection between biological agents in rheumatoid arthritis patients: a multicenter retrospective cohort study in Japan. PLoS One 2017;12(6):e0179179.
51. Neven N, Vis M, Voskuyl AE, Wolbink GJ, Nurmohamed MT, Dijkmans BA, et al. Adverse events in patients with rheumatoid arthritis treated with infliximab in daily clinical practice. Ann Rheum Dis 2005;64(4):645–66.
52. Nguyen-Khoa BA, Goehring EL Jr, Alexander KA, Dong W, Napalkov P, Jones JK. Risk of significant infection in rheumatoid arthritis patients switching anti-tumor necrosis factor-alpha drugs. Semin Arthritis Rheum 2012;42(2):119–26.
53. Ozen G, Pedro S, England BR, Mehta B, Wolfe F, Michaud K. Risk of serious infection in patients with rheumatoid arthritis treated with biologic versus nonbiologic disease-modifying antirheumatic drugs. ACR Open Rheumatol 2019;1(7):424–32.
54. Pawar A, Desai RJ, Solomon DH, Ortiz AJS, Gale S, Bao M, et al. Risk of serious infections in tocilizumab versus other biologic drugs in patients with rheumatoid arthritis: a multidatabase cohort study. Ann Rheum Dis 2019;78:456–64.
55. Ranza R, de la Vega MC, Laurindo IMM, Gómez MG, Titton DC, Kakehasi AM, et al. Changing rate of serious infections in biologic-exposed rheumatoid arthritis patients. Data from South American registries BIOBADABRASIL and BIOBADASAR. Clinical Rheumatol 2019;38(8):2129–39.
56. Rutherford AI, Subesinghe S, Hyrich KL, Galloway JB. Serious infection across biologic-treated patients with rheumatoid arthritis: results from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis. Ann Rheum Dis 2018;77(6):905–10.
57. Sakai R, Komano Y, Tanaka M, Nanki T, Koike R, Nagasawa H, et al. Time-dependent increased risk for serious infection from continuous use of tumor necrosis factor antagonists over three years in patients with rheumatoid arthritis. Arthritis Care Res (Hoboken) 2012;64(8):1125–34.
58. Sakai R, Cho SK, Nanki T, Koike R, Watanabe K, Yamazaki H, et al. The risk of serious infection in patients with rheumatoid arthritis treated with tumor necrosis factor inhibitors decreased over time: a report from the registry of Japanese rheumatoid arthritis patients on biologics for long-term safety (REAL) database. Rheumatol Int 2014;34(12):1729–36.
59. Salmon JH, Gottenberg JE, Ravaud P, Cantagrel A, Combe B, Flipo RM, et al. Predictive risk factors of serious infections in patients with rheumatoid arthritis treated with abatacept in common practice: results from the Orencia and Rheumatoid Arthritis (ORA) registry. Ann Rheum Dis 2016;75(6):1108–113.
60. Schenfeld J, Iles J, Trivedi M, Accortt NA. Dose relationship between oral glucocorticoids and tumor necrosis factor inhibitors and the risk of hospitalized infectious events among patients with rheumatoid arthritis. Rheumatol Int 2017;37(7):1075–82.
61. Schneeweiss S, Setoguchi S, Weinblatt ME, Katz JN, Avorn J, Sax PE, et al. Anti-tumor necrosis factor alpha therapy and the risk of serious bacterial infections in elderly patients with rheumatoid arthritis. Arthritis Rheum 2007;56(6):1754–64.
62. Sharma C, Keen H. Ten-year retrospective review of the incidence of serious infections in patients on biologic disease modifying agents for rheumatoid arthritis in three tertiary hospitals in Western Australia. Intern Med J 2019;49(4):519–25.
63. Silva-Fernandez L, De Cock D, Lunt M, Low AS, Watson KD, Group B-RC, et al. Serious infection risk after 1 year between patients with rheumatoid arthritis treated with rituximab or with a second TNFi after initial TNFi failure: results from The British Society for Rheumatology Biologics Register for Rheumatoid. Arthritis Rheumatol 2018;57(9):1533–40.
64. Simon TA, Boers M, Hochberg BM, Baker N, Skovron ML, et al. Comparative risk of malignancies and infections in patients with rheumatoid arthritis initiating abatacept versus other biologics: a multi-database real-world study. Arthritis Res Ther 2019;21:228.
65. Tokunaga K, Matsui K, Oshikawa H, Matsui T, Tohma S. Risk of serious infection, malignancy, or death in Japanese rheumatoid arthritis patients treated with a combination of abatacept and tacrolimus: a retrospective cohort study. Clin Rheumatol 2021;40(5):1811–17.
66. van Dartel SAA, Fransen J, Kievit W, Flendrie M, den Broeder AA, Visser H, et al. Difference in the risk of serious infections in patients with rheumatoid arthritis treated with adalimumab, infliximab and etanercept: results from the Dutch Rheumatoid Arthritis Monitoring (DREAM) registry. Ann Rheum Dis 2013;72(6):895–900.
67. Yun H, Xie F, Delzell E, Levitan EB, Chen L, Lewis JD, et al. Comparative risk of hospitalized infection associated with biologic agents in rheumatoid arthritis patients enrolled in Medicare. Arthritis Rheumatol 2016;68(1):56–66.
68. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol 2011;173(6):676–82.
69. Centers for Medicare and Medicaid Services. Medicare and Medicaid Milestones, 1937 to 2015 [internet]. CMS; 2015 [cited 2020 Jul 6]. Available from: https://www.cms.gov/About-CMS/Agency-Information/History/Downloads/Medicare-and-Medicaid-Milestones-1937-2015.pdf.
70. Jin S, Li M, Fang Y, Li Q, Liu J, Duan X, et al. Chinese registry of rheumatoid arthritis (CREDIT): II. prevalence and risk factors of major comorbidities in Chinese patients with rheumatoid arthritis. Arthritis Res Therapy 2017;19(1):251.
71. Orlewska E, Ancuta I, Anic B, Codrenau C, Damjanov N, Djukic P, et al. Access to biologic treatment for rheumatoid arthritis in Central and Eastern European countries. Med Sci Monit 2011;17(4):SR1–13.
72. van Vollenhoven R. Treat-to-target in rheumatoid arthritis - are we there yet? Nat Rev Rheumatol 2019;15(3):180–6.
73. Finckh A, Neto D, Iannone F, Loza E, Lie E, van Riel P, et al. The impact of patient heterogeneity and socioeconomic factors on abatacept retention in rheumatoid arthritis across nine European countries. RMD Open 2015;1(1):e000040.
74. Arora A, Mahajan A, Spurden D, Boyd H, Porter D. Long-term drug survival of TNF inhibitor therapy in RA patients: a systematic review of European National Drug Registers. Int J Rheumatology 2013:764518.
75. Souto A, Maneiro JR, Gomez-Reino JJ. Rate of discontinuation and drug survival of biologic therapies in rheumatoid arthritis: a systematic review and meta-analysis of drug registries and health care databases. Rheumatology 2016;55:523–34.
76. Li N, Betts KA, Messali AJ, Skup M, Garg V. Real-world effectiveness of biologic disease-modifying antirheumatic drugs for the treatment of rheumatoid arthritis after etanercept discontinuation in the United Kingdom, France, and Germany. Clin Therapeut 2017;39(8):1618–27.
77. Strand V, Miller P, Williams SA, Saunders K, Grant S, Kremer J. Discontinuation of biologic therapy in rheumatoid arthritis: analysis from the Corrona RA Registry. Rheumatol Ther 2017;4:489–502.
78. Baumgart D, Misery L, Naeyaert S, Taylor PC. Biologic therapies in immune-mediated inflammatory diseases: can biosimilars reduce access inequities? Front Pharmacol 2019;10:279.
79. Thomas K, Vassilopoulos D. Infections in patients with rheumatoid arthritis in the era of targeted synthetic therapies. Mediterr J Rheumatol 2020;31(Suppl 1):129–36.
80. Ali T, Kaitha S, Mahmood S, Ftesi A, Stone J, Bronze MS. Clinical use of anti-TNF therapy and increased risk of infections. Drug Healthc Patient Saf 2013;5:79–99.
81. Li X, Lau SKP, Woo PCY. Fungal infection risks associated with the use of cytokine antagonists and immune checkpoint inhibitors. Experiment Biol Med 2020;245:1104–114.
Keywords:

adverse effect; biologic; disease-modifying antirheumatic drug; rheumatoid arthritis; severe infection

Supplemental Digital Content

© 2023 JBI