The sequence of disease-modifying anti-rheumatic drugs: pathways to and predictors of tocilizumab monotherapy

Analyzing the risk of osteoporosis and fracture in rheumatoid arthritis patients who have been treated with various biologics

BACKGROUND

Rheumatoid arthritis (RA) is a major risk factor for osteoporosis/osteoporotic fractures. We aimed to elucidate the role of treatment choices among osteoporosis/osteoporotic fractures.

METHODOLOGY

We utilized the Chang-Gung Research Database to assess the risks of osteoporosis/osteoporotic fractures among independently treated RA patients, using retrospective time-to-event outcomes analysis.

RESULTS

A total of 3509 RA patients with a mean of 63.1 ± 8.6 years were analyzed. Among all, 1300 RA patients (37%) were diagnosed with newly diagnosed osteoporosis. The crude incidence of newly diagnosed osteoporosis was the highest among those treated with other conventional disease-modifying anti-rheumatic drugs (cDMARDs; 74.1 events/1000-PYs, 95%CI 66.0-82.3), followed by those with a non-treatment period (68 events/1000-PYs, 95%CI 63.1-72.9), methotrxate (MTX) monotherapy (60.7 events/1000-PYs, 95%CI 41.2-80.3), MTX plus other cDMARDs (51.9 events/1000-PYs, 95%CI 43.4-60.3), and abatacept/rituximab (48.6 events/1000-PYs, 95%CI 14.9-82.3). The lowest crude incidence was found in patients treated with anti-TNFi biologics (40.4 events/1000-PYs, 95%CI 28.6-52.2) and other biologic disease-modifying anti-rheumatic drugs (bDMARDs; 40.1 events/1000-PYs, 95%CI 8.0-72.1). A total of 270 patients (20.8%) suffered from an incident fracture during follow-ups. The crude incidence of fracture was the highest among those treated with abatacept/rituximab (49.0 events/1000-PYs, 95%CI 6.0-91.9), followed by those with non-treatment periods (24.3 events/1000-PYs, 95%CI 19.3-29.4), other cDMARDs (24.2 events/1000-PYs, 95%CI 18.1-30.2), anti-TNFi biologics (20.2 events/1000-PYs, 95%CI 8.8-31.6). Other bDMARDs (13.3 events/1000-PYs, 95%CI 0-39.2), MTX mono (12.5 events/1000-PYs, 95%CI 0.3-24.8), and MTX plus other cDMARDs (11.4 events/1000-PYs, 95%CI 5.4-17.4) were low incidences.

CONCLUSION

The treatment option has emerged as a critical determinant in the context of future osteoporosis and osteoporotic fracture risks among RA. These findings offer a valuable resource for clinicians, empowering them to tailor bespoke treatment strategies for RA patients, thereby mitigating the potential for future osteoporosis and fractures.

Patterns in the Sequential Treatment of Patients With Rheumatoid Arthritis Starting a Biologic or Targeted Synthetic Disease-Modifying Antirheumatic Drug: 10-Year Experience From a US-Based Registry

OBJECTIVE

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 antirheumatic 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

A total of 6015 b/tsDMARD-naïve 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 favor of Janus kinase inhibitors since 2015. Although 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. For example, the median duration of the first-line therapy was 153 days in 2018-2021 compared to 208 days in 2015-2017 (P < 0.001).

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 patients with different treatment sequences.

Suboptimal management of rheumatoid arthritis in France: a real-world study based on data from the French National Health Data System

OBJECTIVES

The emergence of targeted therapy is changing rheumatoid arthritis (RA) management, but real-world data remain limited. This study aimed to describe real-world RA treatment patterns using data from a French national claims database.

METHODS

This longitudinal study used the French Permanent Representative Sample (Echantillon Généraliste des Bénéficiaires) claims database. Patients with RA were identified between 2013 and 2017, with treatment patterns, persistence and adherence described.

RESULTS

The study population included 2553 patients with RA. Disease-modifying antirheumatic drugs (DMARDs) were prescribed for 1512 (59.2%) patients, of whom 721 (47.6%) did not require discontinuation or treatment switch. There were 377 (24.9%) treatment discontinuations and 114 patients (7.5%) switched to a targeted DMARD (biological and synthetic (Janus kinase inhibitor) DMARDs). Among the 2315 patients with RA in 2017, almost half (n=1102, 47.6%) were not treated with a DMARD. Most (85.7%) received symptomatic treatment (analgesics (81.0%), steroids (49.2%), non-steroidal anti-inflammatory drugs (39.5%)). Of the 1142 treatment initiations identified, 713 (62.4%) were conventional synthetic DMARDs (csDMARDs), with methotrexate being the most frequent (n=553, 48.45%). One-year persistence rates varied between 55.9% (49.2-62.0%) for tumour necrosis factor inhibitors, and 63.4% (59.6-67.0%) for csDMARDs. Treatment adherence, assessed through medication possession ratio, varied between 71.9% and 90.8%, with ≥80% being the adherence cut-off. Almost half of DMARD initiations were associated with long-term (>6 months), high-dose oral steroid use (~7 mg/day prednisone equivalent).

CONCLUSION

Despite a diverse therapeutic arsenal, there remains a medical need that is not covered by current RA management, which is frequently compensated for by overprescription of steroids.

Therapeutic Utility and Adverse Effects of Biologic Disease-Modifying Anti-Rheumatic Drugs in Inflammatory Arthritis

Targeting specific pathologic pro-inflammatory cytokines or related molecules leads to excellent therapeutic effects in inflammatory arthritis, including rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis. Most of these agents, known as biologic disease-modifying anti-rheumatic drugs (bDMARDs), are produced in live cell lines and are usually monoclonal antibodies. Several types of monoclonal antibodies target different pro-inflammatory cytokines, such as tumor necrosis factor-α, interleukin (IL)-17A, IL-6, and IL-23/12. Some bDMARDs, such as rituximab and abatacept, target specific cell-surface molecules to control the inflammatory response. The therapeutic effects of these bDMARDs differ in different forms of inflammatory arthritis and are associated with different adverse events. In this article, we summarize the therapeutic utility and adverse effects of bDMARDs and suggest future research directions for developing bDMARDs.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Pharmacological treatment patterns in patients with juvenile idiopathic arthritis in the Netherlands: a real-world data analysis

OBJECTIVE

To investigate medication prescription patterns among children with JIA, including duration, sequence and reasons for medication discontinuation.

METHODS

This study is a single-centre, retrospective analysis of prospective data from the electronic medical records of JIA patients receiving systemic therapy aged 0-18 years between 1 April 2011 and 31 March 2019. Patient characteristics (age, gender, JIA subtype) and medication prescriptions were extracted and analysed using descriptive statistics, Sankey diagrams and Kaplan-Meier survival methods.

RESULTS

Over a median of 4.2 years follow-up, the 20 different medicines analysed were prescribed as monotherapy (n = 15) or combination therapy (n = 48 unique combinations) among 236 patients. In non-systemic JIA, synthetic DMARDs were prescribed to almost all patients (99.5%), and always included MTX. In contrast, 43.9% of non-systemic JIA patients received a biologic DMARD (mostly adalimumab or etanercept), ranging from 30.9% for oligoarticular persistent ANA-positive JIA, to 90.9% for polyarticular RF-positive JIA. Among systemic JIA, 91.7% received a biologic DMARD (always including anakinra). When analysing medication prescriptions according to their class, 32.6% involved combination therapy. In 56.8% of patients, subsequent treatment lines were initiated after unsuccessful first-line treatment, resulting in 68 unique sequences. Remission was the most common reason for DMARD discontinuation (44.7%), followed by adverse events (28.9%) and ineffectiveness (22.1%).

CONCLUSION

This paper reveals the complexity of pharmacological treatment in JIA, as indicated by: the variety of mono- and combination therapies prescribed, substantial variation in medication prescriptions between subtypes, most patients receiving two or more treatment lines, and the large number of unique treatment sequences.