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Research article
Daniel Solomon
Brigham and Women's Hospital
Corresponding Author
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Background: There are numerous non-biologic and biologic disease modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice.
Methods: We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every six-months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use.
Results: 7,300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. 82% of TCZm use began within three years of starting any bDMARD. 93% of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with use of TCZm included: prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor.
Conclusions: Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data.
Keywords
DMARDs, Rheumatoid Arthritis, Treatment
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CURRENT STATUS: Published
View the published article at Arthritis Research & Therapy.
Version 3
Posted 03 Jan, 2021
No community comments so far
Editorial decision: Accept
On 24 Dec, 2020
Editor assigned
On 16 Dec, 2020
Submission checks complete
On 16 Dec, 2020
Editor invited
On 16 Dec, 2020
No comments provided
Editorial decision: Minor revision
On 10 Dec, 2020
Reviewer #2 agreed
On 17 Nov, 2020
Review #2 received
Received 17 Nov, 2020
Editor assigned
On 17 Nov, 2020
Reviewers invited
Invitations sent on 17 Nov, 2020
Reviewer #1 agreed
On 17 Nov, 2020
Review #1 received
Received 17 Nov, 2020
Submission checks complete
On 17 Nov, 2020
Editor invited
On 17 Nov, 2020
No comments provided
Editorial decision: Major revision
On 08 Oct, 2020
Review #2 received
Received 06 Oct, 2020
Reviewer #2 agreed
On 21 Sep, 2020
Review #1 received
Received 15 Sep, 2020
Reviewer #1 agreed
On 25 Aug, 2020
Reviewers invited
Invitations sent on 24 Aug, 2020
Editor assigned
On 11 Aug, 2020
First submitted
On 10 Aug, 2020
Submission checks complete
On 10 Aug, 2020
Editor invited
On 10 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
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A new preprint design is coming!
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See the published version of this preprint at Arthritis Research & Therapy.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Daniel Solomon
Brigham and Women's Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Arthritis Research & Therapy.
Version 3
Posted 03 Jan, 2021
No community comments so far
Editorial decision: Accept
On 24 Dec, 2020
Editor assigned
On 16 Dec, 2020
Submission checks complete
On 16 Dec, 2020
Editor invited
On 16 Dec, 2020
No comments provided
Editorial decision: Minor revision
On 10 Dec, 2020
Reviewer #2 agreed
On 17 Nov, 2020
Review #2 received
Received 17 Nov, 2020
Editor assigned
On 17 Nov, 2020
Reviewers invited
Invitations sent on 17 Nov, 2020
Reviewer #1 agreed
On 17 Nov, 2020
Review #1 received
Received 17 Nov, 2020
Submission checks complete
On 17 Nov, 2020
Editor invited
On 17 Nov, 2020
No comments provided
Editorial decision: Major revision
On 08 Oct, 2020
Review #2 received
Received 06 Oct, 2020
Reviewer #2 agreed
On 21 Sep, 2020
Review #1 received
Received 15 Sep, 2020
Reviewer #1 agreed
On 25 Aug, 2020
Reviewers invited
Invitations sent on 24 Aug, 2020
Editor assigned
On 11 Aug, 2020
First submitted
On 10 Aug, 2020
Submission checks complete
On 10 Aug, 2020
Editor invited
On 10 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Arthritis Research & Therapy.
Background: There are numerous non-biologic and biologic disease modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice.
Methods: We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every six-months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use.
Results: 7,300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. 82% of TCZm use began within three years of starting any bDMARD. 93% of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with use of TCZm included: prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor.
Conclusions: Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
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