Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs.

Details

Ressource 1Download: 37267387_BIB_E13D9401A4FF.pdf (913.47 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_E13D9401A4FF
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs.
Journal
PLoS computational biology
Author(s)
Kalweit M., Burden A.M., Boedecker J., Hügle T., Burkard T.
ISSN
1553-7358 (Electronic)
ISSN-L
1553-734X
Publication state
Published
Issued date
06/2023
Peer-reviewed
Oui
Volume
19
Number
6
Pages
e1011073
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Abstract
Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response is a problem preventing and delaying disease control. We aimed to assess and validate treatment response of b/tsDMARDs among clusters of RA patients identified by deep learning. We clustered RA patients clusters at first-time b/tsDMARD (cohort entry) in the Swiss Clinical Quality Management in Rheumatic Diseases registry (SCQM) [1999-2018]. We performed comparative effectiveness analyses of b/tsDMARDs (ref. adalimumab) using Cox proportional hazard regression. Within 15 months, we assessed b/tsDMARD stop due to non-response, and separately a ≥20% reduction in DAS28-esr as a response proxy. We validated results through stratified analyses according to most distinctive patient characteristics of clusters. Clusters comprised between 362 and 1481 patients (3516 unique patients). Stratified (validation) analyses confirmed comparative effectiveness results among clusters: Patients with ≥2 conventional synthetic DMARDs and prednisone at b/tsDMARD initiation, male patients, as well as patients with a lower disease burden responded better to tocilizumab than to adalimumab (hazard ratio [HR] 5.46, 95% confidence interval [CI] [1.76-16.94], and HR 8.44 [3.43-20.74], and HR 3.64 [2.04-6.49], respectively). Furthermore, seronegative women without use of prednisone at b/tsDMARD initiation as well as seropositive women with a higher disease burden and longer disease duration had a higher risk of non-response with golimumab (HR 2.36 [1.03-5.40] and HR 5.27 [2.10-13.21], respectively) than with adalimumab. Our results suggest that RA patient clusters identified by deep learning may have different responses to first-line b/tsDMARD. Thus, it may suggest optimal first-line b/tsDMARD for certain RA patients, which is a step forward towards personalizing treatment. However, further research in other cohorts is needed to verify our results.
Keywords
Humans, Male, Female, Adalimumab/therapeutic use, Prednisone/therapeutic use, Deep Learning, Antirheumatic Agents/therapeutic use, Arthritis, Rheumatoid/drug therapy, Biological Products/therapeutic use
Pubmed
Web of science
Open Access
Yes
Create date
08/06/2023 15:05
Last modification date
08/08/2024 7:41
Usage data