Certolizumab Pegol

Participants are required to meet the Assessment of SpondyloArthritis international Society (ASAS) criteria for radiographic or non-radiographic axSpA, i.e they must satisfy at least one of the following: i) the modified New York criteria for AS [16 (link)]; ii) the imaging-based ASAS definition of axSpA [17 (link)]; or iii) the “clinical” ASAS definition of axSpA [17 (link)]. At the time of recruitment they are required to be naïve to biologic therapy. Individuals starting an “eligible” biologic therapy comprise the “biologic” cohort and those not starting a biologic therapy join the “non-biologic” cohort. The eligible drug list currently comprises adalimumab (Humira), etanercept (Enbrel) and certolizumab pegol (Cimzia). Participants must be aged at least 16 years and be willing to give informed consent for follow-up including access to medical records. Study exclusion criteria are: i) otherwise eligible patients who are starting a biologic therapy not on the eligible drug list and ii) inability to communicate in English. However, once recruited to the study, participants are eligible to remain in the study irrespective of the subsequent pharmacological or non-pharmacological management of their condition. Recruitment can take place through participating National Health Service (NHS) hospitals across the UK.

This was a retrospective claim-based cohort study that utilized longitudinal claims data from the HealthCore Integrated Research Database^® (HIRD^®) from January 1, 2016 to August 31, 2019. The HIRD^® contains data from January 2006 on patient enrollment, inpatient and outpatient medical care, prescription, and health care utilization. It is a large longitudinal medical and pharmacy claims database of health plan members comprising all regions of the US.
The data were accessed and used in full compliance with the relevant provisions of the Health Insurance Portability and Accountability Act. The study was conducted under the research provisions of Privacy Rule 45 CFR 164.514(e). Researchers’ access to claims data was limited to data stripped of identifiers to ensure confidentiality. An Institutional Review Board did not review the study since only this limited data set was accessed. This study was conducted in accordance with the ethical principles that have their origin in the Declaration of Helsinki and that are consistent with Good Pharmacoepidemiology Practices as well as legal and regulatory requirements.
Adult patients aged ≥ 18 years with CD (International Classification of Diseases, 10th Revision, Clinical Modification [ICD-10-CM] diagnosis codes: K50.x) or UC (ICD-10-CM diagnosis codes: K51.x) who initiated an advanced therapy during the index period of July 1, 2016 through August 31, 2018 were included in the study. Index date was defined as the first observed occurrence of a claim (medical or pharmacy) for any eligible advanced therapy during the index period. For patients who started more than one therapy, only the earliest one observed was used. Included patients were enrolled in commercial, Medicare Advantage, or Medicare Supplemental plus Part D insurance plans for ≥ 6 months before the index date (pre-index period) and ≥ 12 months after index date (follow-up period). Eligible patients were required to have ≥ 2 medical claims for CD or UC from a provider of any specialty at least seven days apart during the study period, of which ≥ 1 claim occurred during the pre-index period.
In this study, advanced therapies for CD included TNFi (adalimumab, certolizumab, infliximab) and non-TNFi (natalizumab, ustekinumab, vedolizumab). For UC, advanced therapies included TNFi (adalimumab, golimumab, infliximab), non-TNFi (vedolizumab; ustekinumab as a potential switcher but not index drug), and other therapies (tofacitinib). Conventional therapies included 5-aminosalicylic acid derivatives (mesalazine and sulfasalazine) and immunosuppressants (azathioprine, methotrexate, mycophenolate, cyclosporine, tacrolimus, 6-mercaptopurine).
Patients were excluded if they had claims for ≥ 1 advanced therapy during the 6-month pre-index period to identify new initiators of advanced therapy. Patients who had evidence for other autoimmune diseases including psoriasis, lupus, ankylosing spondylitis, psoriatic arthritis, or rheumatoid arthritis (defined as ≥ 2 claims on different dates for the same disease) were also excluded in order to avoid misclassification of the estimated response rate (e.g., related to non-adherence) due to multiple indications.

Next, we selected patients with RA who were ≤100 years old as judged from the INDI and DEMO tables and conducted a multiple logistic regression analysis to calculate aRORs for the adverse events. After excluding cases with unknown sex, we set each adverse event as the objective variable and age, sex, and treatment patterns of MTX as explanatory variables. We defined four treatment patterns of MTX: i) MTX group that did not use FA or TNFi, ii) MTX + FA group that did not use TNFi, iii) MTX + TNFi group that did not use FA, and iv) MTX + FA + TNFi group. TNFi was used if at least one TNFis (infliximab, adalimumab, etanercept, golimumab, or certolizumab) was employed. In our preliminary analysis to establish a logistic model, we confirmed that higher variance inflation factor (VIF) values were obtained with a logistic model incorporating the use of MTX, FA, and TNFi as covariables and factors of drug combination expressed as products (e.g., MTX*FA or MTX*FA*TNFi). Thus, we used an alternative model for logistic analysis as follows: $\mathit{Log}\left(RORs\right)={\beta }_0+{\beta }_{1}A+{\beta }_{2}S+{\beta }_3{M}_1+{\beta }_4{M}_2+{\beta }_5{M}_3+{\beta }_6{M}_4$
$(\mathrm{A}=\mathrm{a}\mathrm{g}\mathrm{e},\mathrm{S}=\mathrm{s}\mathrm{e}\mathrm{x},{\mathrm{M}}_1=\mathrm{M}\mathrm{T}\mathrm{X}\left(\mathrm{n}\mathrm{o}\mathrm{F}\mathrm{A},\mathrm{T}\mathrm{N}\mathrm{F}\mathrm{i}\right),{\mathrm{M}}_2=\mathrm{M}\mathrm{T}\mathrm{X}+\mathrm{F}\mathrm{A}\left(\mathrm{n}\mathrm{o}\mathrm{T}\mathrm{N}\mathrm{F}\mathrm{i}\right),{\mathrm{M}}_3=\mathrm{M}\mathrm{T}\mathrm{X}+\mathrm{T}\mathrm{N}\mathrm{F}\mathrm{i}\left(\mathrm{n}\mathrm{o}\mathrm{F}\mathrm{A}\right),{\mathrm{M}}_4=\mathrm{M}\mathrm{T}\mathrm{X}+\mathrm{F}\mathrm{A}+\mathrm{T}\mathrm{N}\mathrm{F}\mathrm{i})$
Using this logistic model, we confirmed that all VIF values were ˂ 1.4, and the deviance value was statistically significant, supporting the model’s suitability.
Statistical significance was determined if the upper 95% CI of the ROR was ˂ 1.0 or the lower 95% CI of the ROR was ˃1.0. Fisher’s exact test was used to calculate the p-values of cRORs. Data mining and all statistical analyses were performed using Microsoft Access 2016 (Microsoft Inc. Tokyo, Japan), R version 3.4.1 (R Foundation for Statistical Computing, Vienna, Austria), EZR version 1.36 (Kanda, 2013 (link)), and GraphPad Prism ver. 9.2 (GraphPad Software, San Diego, CA).