Identifying Predictors of First-Line Subcutaneous TNF-Inhibitor Persistence in Patients with Inflammatory Arthritis : A Decision Tree Analysis by Indication
(2023) In Advances in Therapy 40(10). p.4657-4674- Abstract
Introduction: Treatment persistence is a proxy for efficacy, safety and patient satisfaction, and a switch in treatment or treatment discontinuation has been associated with increased indirect and direct costs in inflammatory arthritis (IA). Hence, there are both clinical and economic incentives for the identification of factors associated with treatment persistence. Until now, studies have mainly leveraged traditional regression analysis, but it has been suggested that novel approaches, such as statistical learning techniques, may improve our understanding of factors related to treatment persistence. Therefore, we set up a study using nationwide Swedish high-coverage administrative register data with the objective to identify patient... (More)
Introduction: Treatment persistence is a proxy for efficacy, safety and patient satisfaction, and a switch in treatment or treatment discontinuation has been associated with increased indirect and direct costs in inflammatory arthritis (IA). Hence, there are both clinical and economic incentives for the identification of factors associated with treatment persistence. Until now, studies have mainly leveraged traditional regression analysis, but it has been suggested that novel approaches, such as statistical learning techniques, may improve our understanding of factors related to treatment persistence. Therefore, we set up a study using nationwide Swedish high-coverage administrative register data with the objective to identify patient groups with distinct persistence of subcutaneous tumor necrosis factor inhibitor (SC-TNFi) treatment in IA, using recursive partitioning, a statistical learning algorithm. Methods: IA was defined as a diagnosis of rheumatic arthritis (RA), ankylosing spondylitis/unspecified spondyloarthritis (AS/uSpA) or psoriatic arthritis (PsA). Adult swedish biologic-naïve patients with IA initiating biologic treatment with a SC-TNFi (adalimumab, etanercept, certolizumab or golimumab) between May 6, 2010, and December 31, 2017. Treatment persistence of SC-TNFi was derived based on prescription data and a defined standard daily dose. Patient characteristics, including age, sex, number of health care contacts, comorbidities and treatment, were collected at treatment initiation and 12 months before treatment initiation. Based on these characteristics, we used recursive partitioning in a conditional inference framework to identify patient groups with distinct SC-TNFi treatment persistence by IA diagnosis. Results: A total of 13,913 patients were included. Approximately 50% had RA, while 27% and 23% had AS/uSpA and PsA, respectively. The recursive partitioning algorithm identified sex and treatment as factors associated with SC-TNFi treatment persistence in PsA and AS/uSpA. Time on treatment in the groups with the lowest treatment persistence was similar across all three indications (9.5–11.3 months), whereas there was more variation in time on treatment across the groups with the highest treatment persistence (18.4–48.9 months). Conclusions: Women have low SC-TNFi treatment persistence in PsA and AS/uSpA whereas male sex and golimumab are associated with high treatment persistence in these indications. The factors associated with treatment persistence in RA were less distinct but may comprise disease activity and concurrent conventional systemic disease-modifying anti-rheumatic drug (DMARD) treatment.
(Less)
- author
- Dalén, Johan ; Svedbom, Axel ; Hernlund, Emma ; Olofsson, Tor LU and Black, Christopher M.
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Ankylosing spondylitis, Biologics, Psoriatic arthritis, Recursive partitioning, Rheumatoid arthritis, Spondyloarthritis, Treatment persistence, Tumor necrosis factor-alpha inhibitors
- in
- Advances in Therapy
- volume
- 40
- issue
- 10
- pages
- 18 pages
- publisher
- Springer
- external identifiers
-
- pmid:37599341
- scopus:85168338645
- ISSN
- 0741-238X
- DOI
- 10.1007/s12325-023-02600-3
- language
- English
- LU publication?
- yes
- id
- ea33e34f-f629-4cb9-bb0f-a9fbe788c13a
- date added to LUP
- 2023-11-13 14:13:02
- date last changed
- 2024-04-25 05:30:46
@article{ea33e34f-f629-4cb9-bb0f-a9fbe788c13a,
abstract = {{<p>Introduction: Treatment persistence is a proxy for efficacy, safety and patient satisfaction, and a switch in treatment or treatment discontinuation has been associated with increased indirect and direct costs in inflammatory arthritis (IA). Hence, there are both clinical and economic incentives for the identification of factors associated with treatment persistence. Until now, studies have mainly leveraged traditional regression analysis, but it has been suggested that novel approaches, such as statistical learning techniques, may improve our understanding of factors related to treatment persistence. Therefore, we set up a study using nationwide Swedish high-coverage administrative register data with the objective to identify patient groups with distinct persistence of subcutaneous tumor necrosis factor inhibitor (SC-TNFi) treatment in IA, using recursive partitioning, a statistical learning algorithm. Methods: IA was defined as a diagnosis of rheumatic arthritis (RA), ankylosing spondylitis/unspecified spondyloarthritis (AS/uSpA) or psoriatic arthritis (PsA). Adult swedish biologic-naïve patients with IA initiating biologic treatment with a SC-TNFi (adalimumab, etanercept, certolizumab or golimumab) between May 6, 2010, and December 31, 2017. Treatment persistence of SC-TNFi was derived based on prescription data and a defined standard daily dose. Patient characteristics, including age, sex, number of health care contacts, comorbidities and treatment, were collected at treatment initiation and 12 months before treatment initiation. Based on these characteristics, we used recursive partitioning in a conditional inference framework to identify patient groups with distinct SC-TNFi treatment persistence by IA diagnosis. Results: A total of 13,913 patients were included. Approximately 50% had RA, while 27% and 23% had AS/uSpA and PsA, respectively. The recursive partitioning algorithm identified sex and treatment as factors associated with SC-TNFi treatment persistence in PsA and AS/uSpA. Time on treatment in the groups with the lowest treatment persistence was similar across all three indications (9.5–11.3 months), whereas there was more variation in time on treatment across the groups with the highest treatment persistence (18.4–48.9 months). Conclusions: Women have low SC-TNFi treatment persistence in PsA and AS/uSpA whereas male sex and golimumab are associated with high treatment persistence in these indications. The factors associated with treatment persistence in RA were less distinct but may comprise disease activity and concurrent conventional systemic disease-modifying anti-rheumatic drug (DMARD) treatment.</p>}},
author = {{Dalén, Johan and Svedbom, Axel and Hernlund, Emma and Olofsson, Tor and Black, Christopher M.}},
issn = {{0741-238X}},
keywords = {{Ankylosing spondylitis; Biologics; Psoriatic arthritis; Recursive partitioning; Rheumatoid arthritis; Spondyloarthritis; Treatment persistence; Tumor necrosis factor-alpha inhibitors}},
language = {{eng}},
number = {{10}},
pages = {{4657--4674}},
publisher = {{Springer}},
series = {{Advances in Therapy}},
title = {{Identifying Predictors of First-Line Subcutaneous TNF-Inhibitor Persistence in Patients with Inflammatory Arthritis : A Decision Tree Analysis by Indication}},
url = {{http://dx.doi.org/10.1007/s12325-023-02600-3}},
doi = {{10.1007/s12325-023-02600-3}},
volume = {{40}},
year = {{2023}},
}