Cited by 181

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Risk of serious infections in tocilizumab versus other biologic drugs in patients with rheumatoid arthritis: a multidatabase cohort study

Pawar, Ajinkya, Desai, Rishi J, Solomon, Daniel H, Santiago Ortiz, Adrian J, Gale, Sara, Bao, Min, Sarsour, Khaled, Schneeweiss, Sebastian, Kim, Seoyoung C·2019·Annals of the Rheumatic Diseases

Abstract

Objective To investigate the rate of serious bacterial, viral or opportunistic infection in patients with rheumatoid arthritis (RA) starting tocilizumab (TCZ) versus tumour necrosis factor inhibitors (TNFi) or abatacept. Methods Using claims data from US Medicare from 2010 to 2015, and IMS and MarketScan from 2011 to 2015, we identified adults with RA who initiated TCZ or TNFi (primary comparator)/abatacept (secondary comparator) with prior use of ≥1 different biologic drug or tofacitinib. The primary outcome was hospitalised serious infection (SI), including bacterial, viral or opportunistic infection. To control for >70 confounders, TCZ initiators were propensity score (PS)-matched to TNFi or abatacept initiators. Database-specific HRs were combined by a meta-analysis. Results The primary cohort included 16 074 TCZ PS-matched to 33 109 TNFi initiators. The risk of composite SI was not different between TCZ and TNFi initiators (combined HR 1.05, 95% CI 0.95 to 1.16). However, TCZ was associated with an increased risk of serious bacterial infection (HR 1.19, 95% CI 1.07 to 1.33), skin and soft tissue infections (HR 2.38, 95% CI 1.47 to 3.86), and diverticulitis (HR 2.34, 95% CI 1.64 to 3.34) versus TNFi. An increased risk of composite SI, serious bacterial infection, diverticulitis, pneumonia/upper respiratory tract infection and septicaemia/bacteraemia was observed in TCZ versus abatacept users. Conclusions This large multidatabase cohort study found no difference in composi…

Overview

Integrity signals

Registry

Crossref · 10.1136/annrheumdis-2018-214367

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 36

  1. Au, 2011, High disease activity is associated with an increased risk of infection in patients with rheumatoid arthritis, Ann Rheum Dis, 70, 785, 10.1136/ard.2010.128637 · DOI
  2. Michaud, 2014, The comparative safety of tumor necrosis factor inhibitors in rheumatoid arthritis: a meta-analysis update of 44 trials, Am J Med, 127, 1208, 10.1016/j.amjmed.2014.06.012 · DOI
  3. Ramiro, 2014, Safety of synthetic and biological DMARDs: a systematic literature review Informing the 2013 update of the EULAR recommendations for management of rheumatoid arthritis, Ann Rheum Dis, 73, 529, 10.1136/annrheumdis-2013-204575 · DOI
  4. Singh, 2012, 2012 update of the 2008 American College of rheumatology recommendations for the use of disease-modifying antirheumatic drugs and biologic agents in the treatment of rheumatoid arthritis, Arthritis Care Res, 64, 625, 10.1002/acr.21641 · DOI
  5. Singh, 2011, Adverse effects of biologics: a network meta-analysis and Cochrane overview, Cochrane Database of Systematic Reviews, 33
  6. Jones, 2010, Comparison of tocilizumab monotherapy versus methotrexate monotherapy in patients with moderate to severe rheumatoid arthritis: the ambition study, Ann Rheum Dis, 69, 88, 10.1136/ard.2008.105197 · DOI · Paper page
  7. Smolen, 2008, Effect of interleukin-6 receptor inhibition with tocilizumab in patients with rheumatoid arthritis (option study): a double-blind, placebo-controlled, randomised trial, The Lancet, 371, 987, 10.1016/S0140-6736(08)60453-5 · DOI
  8. Emery, 2008, IL-6 receptor inhibition with tocilizumab improves treatment outcomes in patients with rheumatoid arthritis refractory to anti-tumour necrosis factor biologicals: results from a 24-week multicentre randomised placebo-controlled trial, Ann Rheum Dis, 67, 1516, 10.1136/ard.2008.092932 · DOI · Paper page
  9. Yun, 2016, Comparative risk of hospitalized infection associated with biologic agents in rheumatoid arthritis patients enrolled in Medicare, Arthritis Rheumatol, 68, 56, 10.1002/art.39399 · DOI · Paper page
  10. Sakai, 2015, Head-to-head comparison of the safety of tocilizumab and tumor necrosis factor inhibitors in rheumatoid arthritis patients (rA) in clinical practice: results from the registry of Japanese RA patients on biologics for long-term safety (real) registry, Arthritis Res Ther, 17, 74, 10.1186/s13075-015-0583-8 · DOI
  11. Rutherford, 2018, Serious infection across biologic-treated patients with rheumatoid arthritis: results from the british Society for rheumatology biologics register for rheumatoid arthritis, Ann Rheum Dis, 77, 905, 10.1136/annrheumdis-2017-212825 · DOI · Paper page
  12. Kappelman, 2007, The prevalence and geographic distribution of Crohn's disease and ulcerative colitis in the United States, Clin Gastroenterol Hepatol, 5, 1424, 10.1016/j.cgh.2007.07.012 · DOI
  13. Quek, 2016, A US Claims-Based analysis of real-world lipid-lowering treatment patterns in patients with high cardiovascular disease risk or a previous coronary event, Am J Cardiol, 117, 495, 10.1016/j.amjcard.2015.11.035 · DOI
  14. Desai, 2014, Tumor necrosis factor-α inhibitor treatment and the risk of incident cardiovascular events in patients with early rheumatoid arthritis: a nested case-control study, J Rheumatol, 41, 2129, 10.3899/jrheum.131464 · DOI
  15. Kim, 2011, Validation of rheumatoid arthritis diagnoses in health care utilization data, Arthritis Res Ther, 13, R32, 10.1186/ar3260 · DOI
  16. Schneeweiss, 2007, Veteran's Affairs hospital discharge databases coded serious bacterial infections accurately, Journal of Clinical Epidemiology, 60, 397, 10.1016/j.jclinepi.2006.07.011 · DOI · Paper page
  17. Winthrop, 2013, Association between the initiation of Anti–Tumor necrosis factor therapy and the risk of herpes zoster, JAMA, 309, 887, 10.1001/jama.2013.1099 · DOI
  18. Barber, 2013, Systematic review of validation studies of the use of administrative data to identify serious infections, Arthritis Care & Research, 65, 1343, 10.1002/acr.21959 · DOI
  19. Gagne, 2011, A combined comorbidity score predicted mortality in elderly patients better than existing scores, Journal of Clinical Epidemiology, 64, 749, 10.1016/j.jclinepi.2010.10.004 · DOI · Paper page
  20. Ting, 2008, Development of a health care utilisation data-based index for rheumatoid arthritis severity: a preliminary study, Arthritis Res Ther, 10, R95, 10.1186/ar2482 · DOI
  21. Schneeweiss, 2012, Supplementing claims data with outpatient laboratory test results to improve confounding adjustment in effectiveness studies of lipid-lowering treatments, BMC Medical Research Methodology, 12, 180, 10.1186/1471-2288-12-180 · DOI
  22. Rubin, 1997, Estimating causal effects from large data sets using propensity scores, Ann Intern Med, 127, 757, 10.7326/0003-4819-127-8_Part_2-199710151-00064 · DOI
  23. Rassen, 2012, One-to-many propensity score matching in cohort studies, Pharmacoepidemiology and Drug Safety, 21, 69, 10.1002/pds.3263 · DOI
  24. Austin, 2011, Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies, Pharmaceutical Statistics, 10, 150, 10.1002/pst.433 · DOI · Paper page
  25. Cummings, 2003, Matched-pair cohort methods in traffic crash research, Accident Analysis & Prevention, 35, 131, 10.1016/S0001-4575(01)00108-7 · DOI
  26. Walker, 1981, Rothman KJ: Vasectomy and non-fatal myocardial infarction, Lancet, 1, 13, 10.1016/S0140-6736(81)90116-1 · DOI
  27. Wang, 2016, Transparency and reproducibility of observational cohort studies using large healthcare databases, Clin. Pharmacol. Ther., 99, 325, 10.1002/cpt.329 · DOI
  28. Kim, 2017, Cardiovascular safety of tocilizumab versus tumor necrosis factor inhibitors in patients with rheumatoid arthritis: a Multi-Database cohort study, Arthritis & Rheumatology, 69, 1154, 10.1002/art.40084 · DOI
  29. Franklin, 2014, Metrics for covariate balance in cohort studies of causal effects, Statistics in Medicine, 33, 1685, 10.1002/sim.6058 · DOI
  30. Yamamoto, 2015, Long-term safety of tocilizumab: results from 3 years of followup postmarketing surveillance of 5573 patients with rheumatoid arthritis in Japan, The Journal of Rheumatology, 42, 1368, 10.3899/jrheum.141210 · DOI · Paper page
  31. Grijalva, 2011, Initiation of tumor necrosis factor-α antagonists and the risk of hospitalization for infection in patients with autoimmune diseases, JAMA, 306, 2331, 10.1001/jama.2011.1692 · DOI
  32. Schneeweiss, 2007, Anti–tumor necrosis factor α therapy and the risk of serious bacterial infections in elderly patients with rheumatoid arthritis, Arthritis Rheum, 56, 1754, 10.1002/art.22600 · DOI
  33. Zink, 2014, Evaluation of the rabbit risk score for serious infections, Annals of the Rheumatic Diseases, 73, 1673, 10.1136/annrheumdis-2013-203341 · DOI
  34. Galloway, 2011, Rheumatology, 50, 124, 10.1093/rheumatology/keq242 · DOI
  35. Ray, 2003, Evaluating medication effects outside of clinical trials: New-User designs, American Journal of Epidemiology, 158, 915, 10.1093/aje/kwg231 · DOI · Paper page
  36. Yoshida, 2015, Active-comparator design and new-user design in observational studies, Nat Rev Rheumatol, 11, 437, 10.1038/nrrheum.2015.30 · DOI

Cited by

Sorted by citations

See all 181

Metadata

Authors
Pawar, A., Desai, R. J., Solomon, D. H., Santiago Ortiz, A. J., Gale, S., Bao, M., Sarsour, K., Schneeweiss, S., & Kim, S. C.
Venue
Annals of the Rheumatic Diseases
Publisher
Elsevier BV
Published
2019-04
Volume
78
Issue
4
Pages
456-464
ISSN
0003-4967
Cited by
181
References
1
Language
en
Funders
Genentech

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review