Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Kalweit, Maria, Burden, Andrea M., Boedecker, Joschka, Hügle, Thomas, Burkard, Theresa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266686/
https://www.ncbi.nlm.nih.gov/pubmed/37267387
http://dx.doi.org/10.1371/journal.pcbi.1011073
_version_ 1785058792024571904
author Kalweit, Maria
Burden, Andrea M.
Boedecker, Joschka
Hügle, Thomas
Burkard, Theresa
author_facet Kalweit, Maria
Burden, Andrea M.
Boedecker, Joschka
Hügle, Thomas
Burkard, Theresa
author_sort Kalweit, Maria
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10266686
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-102666862023-06-15 Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs Kalweit, Maria Burden, Andrea M. Boedecker, Joschka Hügle, Thomas Burkard, Theresa PLoS Comput Biol Research Article 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. Public Library of Science 2023-06-02 /pmc/articles/PMC10266686/ /pubmed/37267387 http://dx.doi.org/10.1371/journal.pcbi.1011073 Text en © 2023 Kalweit et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kalweit, Maria
Burden, Andrea M.
Boedecker, Joschka
Hügle, Thomas
Burkard, Theresa
Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs
title Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs
title_full Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs
title_fullStr Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs
title_full_unstemmed Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs
title_short Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs
title_sort patient groups in rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic dmards
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266686/
https://www.ncbi.nlm.nih.gov/pubmed/37267387
http://dx.doi.org/10.1371/journal.pcbi.1011073
work_keys_str_mv AT kalweitmaria patientgroupsinrheumatoidarthritisidentifiedbydeeplearningresponddifferentlytobiologicortargetedsyntheticdmards
AT burdenandream patientgroupsinrheumatoidarthritisidentifiedbydeeplearningresponddifferentlytobiologicortargetedsyntheticdmards
AT boedeckerjoschka patientgroupsinrheumatoidarthritisidentifiedbydeeplearningresponddifferentlytobiologicortargetedsyntheticdmards
AT huglethomas patientgroupsinrheumatoidarthritisidentifiedbydeeplearningresponddifferentlytobiologicortargetedsyntheticdmards
AT burkardtheresa patientgroupsinrheumatoidarthritisidentifiedbydeeplearningresponddifferentlytobiologicortargetedsyntheticdmards