Patterns in the sequential treatment of rheumatoid arthritis patients starting a b/tsDMARD: 10-year experience from a US-based registry

Objectives Developing and evaluating new treatment guidelines for rheumatoid arthritis (RA) based on observational data requires a quantitative understanding of patterns in current treatment practice with biologic and targeted synthetic disease-modifying anti-rheumatic drugs (b/tsDMARDs). Methods We used data from the CorEvitas RA registry to study patients starting their first b/tsDMARD therapy—defined as the first line of therapy—between 2012 and the end of 2021. We identified treatment patterns as unique sequences of therapy changes following and including the first-line therapy. Therapy cycling was defined as switching back to a treatment from a previously used therapeutic class. Results 6,015 b/tsDMARD-naive patients (77% female) were included in the analysis. Their median age was 58 years, and their median disease duration was 3 years. In 2012–2014, 80% of the patients started a tumor necrosis factor inhibitor (TNFi) as their first b/tsDMARD. However, the use of TNFi decreased in favour of Janus kinase inhibitors (JAKi) since 2015. While the number of treatment patterns was large, therapy cycling was relatively common. For example, 601 patterns were observed among 1133 patients who changed therapy at least four times, of whom 85.3% experienced therapy cycling. Furthermore, the duration of each of the first three lines of therapy decreased over the past decade. Conclusion First-line therapy was almost always TNFi, but diversity in treatment choice was high after that. This practice variation allows for proposing and evaluating new guidelines for sequential treatment of RA. It also presents statistical challenges to compare subjects with different treatment sequences.

[1]  S. Zhao,et al.  Effectiveness of sequential biologic and targeted disease modifying anti-rheumatic drugs for rheumatoid arthritis , 2022, Rheumatology.

[2]  R. Buchbinder,et al.  Patterns of biologic and targeted-synthetic disease-modifying antirheumatic drug use in rheumatoid arthritis in Australia , 2022, Rheumatology.

[3]  Shin-Seok Lee,et al.  Comparison of the efficacy and risk of discontinuation between non-TNF-targeted treatment and a second TNF inhibitor in patients with rheumatoid arthritis after first TNF inhibitor failure , 2022, Therapeutic advances in musculoskeletal disease.

[4]  P. Mease,et al.  Treatment patterns in rheumatoid arthritis patients newly initiated on biologic and conventional synthetic disease-modifying antirheumatic drug therapy and enrolled in a North American clinical registry , 2021, Arthritis Research & Therapy.

[5]  Michael L. Waskom,et al.  Seaborn: Statistical Data Visualization , 2021, J. Open Source Softw..

[6]  Fredrik D. Johansson,et al.  The sequence of disease-modifying anti-rheumatic drugs: pathways to and predictors of tocilizumab monotherapy , 2020, Arthritis Research & Therapy.

[7]  D. Sangiorgi,et al.  Real-World Analysis of Therapeutic Patterns in Patients Affected by Rheumatoid Arthritis in Italy: A Focus on Baricitinib , 2020, Rheumatology and Therapy.

[8]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[9]  P. Mahajan,et al.  Biologic Disease-Modifying Antirheumatic Drug Prescription Patterns Among Rheumatologists in Europe and Japan , 2020, Rheumatology and Therapy.

[10]  J. Curtis,et al.  Biologic Disease-Modifying Antirheumatic Drug Prescription Patterns for Rheumatoid Arthritis Among United States Physicians , 2020, Rheumatology and Therapy.

[11]  Tsutomu Takeuchi,et al.  EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2019 update , 2020, Annals of the Rheumatic Diseases.

[12]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[13]  Li Wang,et al.  Treatment Persistence and Clinical Outcomes of Tumor Necrosis Factor Inhibitor Cycling or Switching to a New Mechanism of Action Therapy: Real-world Observational Study of Rheumatoid Arthritis Patients in the United States with Prior Tumor Necrosis Factor Inhibitor Therapy , 2017, Advances in Therapy.

[14]  J. Curtis,et al.  Treatment effectiveness and treatment patterns among rheumatoid arthritis patients after switching from a tumor necrosis factor inhibitor to another medication , 2016, ClinicoEconomics and outcomes research : CEOR.

[15]  S. Kruger Design Of Observational Studies , 2016 .

[16]  Raveendhara R. Bannuru,et al.  American College of Rheumatology Guideline for the Treatment of Rheumatoid Arthritis , 2015 .

[17]  Wes McKinney,et al.  Data Structures for Statistical Computing in Python , 2010, SciPy.

[18]  M. Dougados,et al.  Rituximab inhibits structural joint damage in patients with rheumatoid arthritis with an inadequate response to tumour necrosis factor inhibitor therapies , 2008, Annals of the rheumatic diseases.

[19]  J. Kremer,et al.  The CORRONA database. , 2006, Autoimmunity reviews.

[20]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .